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import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging __snake_case : Optional[Any] =logging.get_logger(__name__) __snake_case : str ={ 'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json', # See all SEW-D models at https://huggingface.co/models?filter=sew-d } class lowerCamelCase__ ( lowerCamelCase__): '''simple docstring''' snake_case_ ="""sew-d""" def __init__(self ,__lowerCamelCase=32 ,__lowerCamelCase=7_68 ,__lowerCamelCase=12 ,__lowerCamelCase=12 ,__lowerCamelCase=30_72 ,__lowerCamelCase=2 ,__lowerCamelCase=5_12 ,__lowerCamelCase=2_56 ,__lowerCamelCase=True ,__lowerCamelCase=True ,__lowerCamelCase=("p2c", "c2p") ,__lowerCamelCase="layer_norm" ,__lowerCamelCase="gelu_python" ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.0 ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.02 ,__lowerCamelCase=1e-7 ,__lowerCamelCase=1e-5 ,__lowerCamelCase="group" ,__lowerCamelCase="gelu" ,__lowerCamelCase=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) ,__lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,__lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,__lowerCamelCase=False ,__lowerCamelCase=1_28 ,__lowerCamelCase=16 ,__lowerCamelCase=True ,__lowerCamelCase=0.05 ,__lowerCamelCase=10 ,__lowerCamelCase=2 ,__lowerCamelCase=0.0 ,__lowerCamelCase=10 ,__lowerCamelCase=0 ,__lowerCamelCase="mean" ,__lowerCamelCase=False ,__lowerCamelCase=False ,__lowerCamelCase=2_56 ,__lowerCamelCase=0 ,__lowerCamelCase=1 ,__lowerCamelCase=2 ,**__lowerCamelCase ,) -> int: """simple docstring""" super().__init__(**__a ,pad_token_id=__a ,bos_token_id=__a ,eos_token_id=__a ) lowerCAmelCase__ : Dict = hidden_size lowerCAmelCase__ : Any = feat_extract_norm lowerCAmelCase__ : List[str] = feat_extract_activation lowerCAmelCase__ : Optional[Any] = list(__a ) lowerCAmelCase__ : Union[str, Any] = list(__a ) lowerCAmelCase__ : Tuple = list(__a ) lowerCAmelCase__ : Tuple = conv_bias lowerCAmelCase__ : List[Any] = num_conv_pos_embeddings lowerCAmelCase__ : Optional[Any] = num_conv_pos_embedding_groups lowerCAmelCase__ : str = len(self.conv_dim ) lowerCAmelCase__ : List[str] = num_hidden_layers lowerCAmelCase__ : List[str] = intermediate_size lowerCAmelCase__ : List[str] = squeeze_factor lowerCAmelCase__ : str = max_position_embeddings lowerCAmelCase__ : Dict = position_buckets lowerCAmelCase__ : List[Any] = share_att_key lowerCAmelCase__ : Optional[int] = relative_attention lowerCAmelCase__ : int = norm_rel_ebd lowerCAmelCase__ : Tuple = list(__a ) lowerCAmelCase__ : List[Any] = hidden_act lowerCAmelCase__ : Union[str, Any] = num_attention_heads lowerCAmelCase__ : List[Any] = hidden_dropout lowerCAmelCase__ : List[str] = attention_dropout lowerCAmelCase__ : Dict = activation_dropout lowerCAmelCase__ : str = feat_proj_dropout lowerCAmelCase__ : List[str] = final_dropout lowerCAmelCase__ : Dict = layer_norm_eps lowerCAmelCase__ : Optional[int] = feature_layer_norm_eps lowerCAmelCase__ : List[Any] = initializer_range lowerCAmelCase__ : Union[str, Any] = vocab_size if ( (len(self.conv_stride ) != self.num_feat_extract_layers) or (len(self.conv_kernel ) != self.num_feat_extract_layers) or (len(self.conv_dim ) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect.''' '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,''' f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)""" f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 lowerCAmelCase__ : Any = apply_spec_augment lowerCAmelCase__ : Any = mask_time_prob lowerCAmelCase__ : Optional[Any] = mask_time_length lowerCAmelCase__ : Tuple = mask_time_min_masks lowerCAmelCase__ : List[str] = mask_feature_prob lowerCAmelCase__ : Optional[int] = mask_feature_length lowerCAmelCase__ : List[Any] = mask_feature_min_masks # ctc loss lowerCAmelCase__ : Tuple = ctc_loss_reduction lowerCAmelCase__ : int = ctc_zero_infinity # sequence classification lowerCAmelCase__ : Tuple = use_weighted_layer_sum lowerCAmelCase__ : int = classifier_proj_size @property def lowerCAmelCase__ (self ) -> int: """simple docstring""" return functools.reduce(operator.mul ,self.conv_stride ,1 )
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"""simple docstring""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging _a = logging.get_logger(__name__) _a = { """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""", # See all WavLM models at https://huggingface.co/models?filter=wavlm } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'wavlm' def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]: '''simple docstring''' super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a) _UpperCamelCase = hidden_size _UpperCamelCase = feat_extract_norm _UpperCamelCase = feat_extract_activation _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = conv_bias _UpperCamelCase = num_buckets _UpperCamelCase = max_bucket_distance _UpperCamelCase = num_conv_pos_embeddings _UpperCamelCase = num_conv_pos_embedding_groups _UpperCamelCase = len(self.conv_dim) _UpperCamelCase = num_hidden_layers _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = num_attention_heads _UpperCamelCase = hidden_dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = feat_proj_dropout _UpperCamelCase = final_dropout _UpperCamelCase = layerdrop _UpperCamelCase = layer_norm_eps _UpperCamelCase = initializer_range _UpperCamelCase = num_ctc_classes _UpperCamelCase = vocab_size _UpperCamelCase = do_stable_layer_norm _UpperCamelCase = use_weighted_layer_sum _UpperCamelCase = classifier_proj_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==''' ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =''' F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,''' F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''') # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _UpperCamelCase = apply_spec_augment _UpperCamelCase = mask_time_prob _UpperCamelCase = mask_time_length _UpperCamelCase = mask_time_min_masks _UpperCamelCase = mask_feature_prob _UpperCamelCase = mask_feature_length # parameters for pretraining with codevector quantized representations _UpperCamelCase = num_codevectors_per_group _UpperCamelCase = num_codevector_groups _UpperCamelCase = contrastive_logits_temperature _UpperCamelCase = num_negatives _UpperCamelCase = codevector_dim _UpperCamelCase = proj_codevector_dim _UpperCamelCase = diversity_loss_weight # ctc loss _UpperCamelCase = ctc_loss_reduction _UpperCamelCase = ctc_zero_infinity # adapter _UpperCamelCase = add_adapter _UpperCamelCase = adapter_kernel_size _UpperCamelCase = adapter_stride _UpperCamelCase = num_adapter_layers _UpperCamelCase = output_hidden_size or hidden_size # SequenceClassification-specific parameter. Feel free to ignore for other classes. _UpperCamelCase = classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = xvector_output_dim @property def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return functools.reduce(operator.mul , self.conv_stride , 1)
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'''simple docstring''' import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class UpperCAmelCase__ ( unittest.TestCase ): """simple docstring""" @property def __lowercase ( self : Tuple ): '''simple docstring''' torch.manual_seed(0 ) _a : Union[str, Any] = UNetaDModel( block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,) return model @property def __lowercase ( self : Optional[Any] ): '''simple docstring''' torch.manual_seed(0 ) _a : Optional[Any] = VQModel( block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,latent_channels=3 ,) return model @property def __lowercase ( self : List[str] ): '''simple docstring''' torch.manual_seed(0 ) _a : Any = CLIPTextConfig( bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1000 ,) return CLIPTextModel(__a ) def __lowercase ( self : Union[str, Any] ): '''simple docstring''' _a : Any = self.dummy_uncond_unet _a : Union[str, Any] = DDIMScheduler() _a : int = self.dummy_vq_model _a : int = LDMPipeline(unet=__a ,vqvae=__a ,scheduler=__a ) ldm.to(__a ) ldm.set_progress_bar_config(disable=__a ) _a : Optional[Any] = torch.manual_seed(0 ) _a : Dict = ldm(generator=__a ,num_inference_steps=2 ,output_type='numpy' ).images _a : List[Any] = torch.manual_seed(0 ) _a : List[str] = ldm(generator=__a ,num_inference_steps=2 ,output_type='numpy' ,return_dict=__a )[0] _a : str = image[0, -3:, -3:, -1] _a : Tuple = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) _a : str = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] ) _a : Any = 1E-2 if torch_device != 'mps' else 3E-2 assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance @slow @require_torch class UpperCAmelCase__ ( unittest.TestCase ): """simple docstring""" def __lowercase ( self : Dict ): '''simple docstring''' _a : Union[str, Any] = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' ) ldm.to(__a ) ldm.set_progress_bar_config(disable=__a ) _a : Any = torch.manual_seed(0 ) _a : Union[str, Any] = ldm(generator=__a ,num_inference_steps=5 ,output_type='numpy' ).images _a : Dict = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) _a : Tuple = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] ) _a : Optional[Any] = 1E-2 if torch_device != 'mps' else 3E-2 assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
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"""simple docstring""" import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer _a = """bart""" _a = True @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Dict: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' ) _UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' ) _UpperCamelCase = qar_model.eval() else: _UpperCamelCase , _UpperCamelCase = (None, None) if MODEL_TYPE == "bart": _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' ) _UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' ) _UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' ) sas_model.load_state_dict(save_dict['''model'''] ) _UpperCamelCase = sas_model.eval() else: _UpperCamelCase , _UpperCamelCase = make_qa_sas_model( model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' ) return (qar_tokenizer, qar_model, sas_tokenizer, sas_model) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = faiss.StandardGpuResources() _UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train'''] _UpperCamelCase = np.memmap( '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) _UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case ) wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU else: _UpperCamelCase , _UpperCamelCase = (None, None) _UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] ) return (wikiaab_passages, wikiaab_gpu_index_flat, es_client) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' ) _UpperCamelCase = elia['''train_eli5'''] _UpperCamelCase = np.memmap( '''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) eli5_train_q_index.add(__snake_case ) return (elia_train, eli5_train_q_index) _a , _a , _a = load_indexes() _a , _a , _a , _a = load_models() _a , _a = load_train_data() def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]: """simple docstring""" _UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case ) _UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case ) _UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]] return nn_examples def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]: """simple docstring""" if source == "none": _UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), []) else: if method == "dense": _UpperCamelCase , _UpperCamelCase = query_qa_dense_index( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) else: _UpperCamelCase , _UpperCamelCase = query_es_index( __snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, ) _UpperCamelCase = [ (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst ] _UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case ) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda __snake_case : None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None), } ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict: """simple docstring""" with torch.no_grad(): _UpperCamelCase = qa_sas_generate( __snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0] return (answer, support_list) st.title("""Long Form Question Answering with ELI5""") # Start sidebar _a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>""" _a = """ <html> <head> <style> .img-container { padding-left: 90px; padding-right: 90px; padding-top: 50px; padding-bottom: 50px; background-color: #f0f3f9; } </style> </head> <body> <span class=\"img-container\"> <!-- Inline parent element --> %s </span> </body> </html> """ % ( header_html, ) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia _a = """ This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html). First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset, a pre-processed fixed snapshot of Wikipedia. """ st.sidebar.markdown(description, unsafe_allow_html=True) _a = [ """Answer the question""", """View the retrieved document only""", """View the most similar ELI5 question and answer""", """Show me everything, please!""", ] _a = st.sidebar.checkbox("""Demo options""") if demo_options: _a = st.sidebar.selectbox( """""", action_list, index=3, ) _a = action_list.index(action_st) _a = st.sidebar.selectbox( """""", ["""Show full text of passages""", """Show passage section titles"""], index=0, ) _a = show_type == """Show full text of passages""" else: _a = 3 _a = True _a = st.sidebar.checkbox("""Retrieval options""") if retrieval_options: _a = """ ### Information retriever options The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs. The answer is then generated by sequence to sequence model which takes the question and retrieved document as input. """ st.sidebar.markdown(retriever_info) _a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""]) _a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""]) else: _a = """wiki40b""" _a = """dense""" _a = """beam""" _a = 2 _a = 64 _a = 256 _a = None _a = None _a = st.sidebar.checkbox("""Generation options""") if generate_options: _a = """ ### Answer generation options The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large) weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with **beam** search, or **sample** from the decoder's output probabilities. """ st.sidebar.markdown(generate_info) _a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""]) _a = st.sidebar.slider( """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None ) _a = st.sidebar.slider( """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None ) if sampled == "beam": _a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: _a = st.sidebar.slider( """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) _a = st.sidebar.slider( """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) _a = None # start main text _a = [ """<MY QUESTION>""", """How do people make chocolate?""", """Why do we get a fever when we are sick?""", """How can different animals perceive different colors?""", """What is natural language processing?""", """What's the best way to treat a sunburn?""", """What exactly are vitamins ?""", """How does nuclear energy provide electricity?""", """What's the difference between viruses and bacteria?""", """Why are flutes classified as woodwinds when most of them are made out of metal ?""", """Why do people like drinking coffee even though it tastes so bad?""", """What happens when wine ages? How does it make the wine taste better?""", """If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""", """How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""", """How does New Zealand have so many large bird predators?""", ] _a = st.selectbox( """What would you like to ask? ---- select <MY QUESTION> to enter a new query""", questions_list, index=1, ) if question_s == "<MY QUESTION>": _a = st.text_input("""Enter your question here:""", """""") else: _a = question_s if st.button("""Show me!"""): if action in [0, 1, 3]: if index_type == "mixed": _a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10) _a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10) _a = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] _a = support_list[:10] _a = """<P> """ + """ <P> """.join([res[-1] for res in support_list]) else: _a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10) if action in [0, 3]: _a , _a = answer_question( question_doc, sas_model, sas_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == """sampled"""), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown("""### The model generated answer is:""") st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""") for i, res in enumerate(support_list): _a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_""")) _a = res[1].strip() if sec_titles == "": _a = """[{}]({})""".format(res[0], wiki_url) else: _a = sec_titles.split(""" & """) _a = """ & """.join( ["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list] ) st.markdown( """{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( """> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True ) if action in [2, 3]: _a = find_nearest_training(question) _a = nn_train_list[0] st.markdown( """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""]) ) _a = [ """{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""])) for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""])) if i == 0 or sc > 2 ] st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st))) _a = """ --- **Disclaimer** *The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system. Evaluating biases of such a model and ensuring factual generations are still very much open research problems. Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.* """ st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
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import argparse import torch from torch import nn from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration def __lowerCamelCase (UpperCAmelCase__ : Any ): SCREAMING_SNAKE_CASE = [ "encoder.version", "decoder.version", "model.encoder.version", "model.decoder.version", "decoder.output_projection.weight", "_float_tensor", "encoder.embed_positions._float_tensor", "decoder.embed_positions._float_tensor", ] for k in ignore_keys: state_dict.pop(__snake_case , __snake_case ) def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ): SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape SCREAMING_SNAKE_CASE = nn.Linear(__snake_case , __snake_case , bias=__snake_case ) SCREAMING_SNAKE_CASE = emb.weight.data return lin_layer def __lowerCamelCase (UpperCAmelCase__ : str ): SCREAMING_SNAKE_CASE = torch.load(__snake_case , map_location="cpu" ) SCREAMING_SNAKE_CASE = mam_aaa["args"] or mam_aaa["cfg"]["model"] SCREAMING_SNAKE_CASE = mam_aaa["model"] remove_ignore_keys_(__snake_case ) SCREAMING_SNAKE_CASE = state_dict["encoder.embed_tokens.weight"].shape[0] SCREAMING_SNAKE_CASE = MaMaaaConfig( vocab_size=__snake_case , max_position_embeddings=1_0_2_4 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , ) SCREAMING_SNAKE_CASE = state_dict["decoder.embed_tokens.weight"] SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(__snake_case ) model.model.load_state_dict(__snake_case , strict=__snake_case ) SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared ) return model if __name__ == "__main__": _lowerCamelCase : List[str] = argparse.ArgumentParser() # Required parameters parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''') parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''') _lowerCamelCase : List[Any] = parser.parse_args() _lowerCamelCase : Dict = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß) model.save_pretrained(args.pytorch_dump_folder_path)
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"""simple docstring""" import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() _a = logging.get_logger(__name__) _a = { """post_extract_proj""": """feature_projection.projection""", """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""", """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""", """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""", """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""", """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""", """self_attn_layer_norm""": """encoder.layers.*.layer_norm""", """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""", """fc2""": """encoder.layers.*.feed_forward.output_dense""", """final_layer_norm""": """encoder.layers.*.final_layer_norm""", """encoder.layer_norm""": """encoder.layer_norm""", """w2v_model.layer_norm""": """feature_projection.layer_norm""", """quantizer.weight_proj""": """quantizer.weight_proj""", """quantizer.vars""": """quantizer.codevectors""", """project_q""": """project_q""", """final_proj""": """project_hid""", """w2v_encoder.proj""": """lm_head""", """mask_emb""": """masked_spec_embed""", } _a = [ """lm_head""", """quantizer.weight_proj""", """quantizer.codevectors""", """project_q""", """project_hid""", ] def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple: """simple docstring""" for attribute in key.split('''.''' ): _UpperCamelCase = getattr(__snake_case, __snake_case ) if weight_type is not None: _UpperCamelCase = getattr(__snake_case, __snake_case ).shape else: _UpperCamelCase = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": _UpperCamelCase = value elif weight_type == "weight_g": _UpperCamelCase = value elif weight_type == "weight_v": _UpperCamelCase = value elif weight_type == "bias": _UpperCamelCase = value else: _UpperCamelCase = value logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' ) def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = [] _UpperCamelCase = fairseq_model.state_dict() _UpperCamelCase = hf_model.feature_extractor _UpperCamelCase = hf_model.adapter for name, value in fairseq_dict.items(): _UpperCamelCase = False if "conv_layers" in name: load_conv_layer( __snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', ) _UpperCamelCase = True elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ): load_adapter(__snake_case, __snake_case, __snake_case, __snake_case ) _UpperCamelCase = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: _UpperCamelCase = True if "*" in mapped_key: _UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2] _UpperCamelCase = mapped_key.replace('''*''', __snake_case ) if "weight_g" in name: _UpperCamelCase = '''weight_g''' elif "weight_v" in name: _UpperCamelCase = '''weight_v''' elif "bias" in name: _UpperCamelCase = '''bias''' elif "weight" in name: _UpperCamelCase = '''weight''' else: _UpperCamelCase = None set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) continue if not is_used: unused_weights.append(__snake_case ) logger.warning(F'''Unused weights: {unused_weights}''' ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = full_name.split('''conv_layers.''' )[-1] _UpperCamelCase = name.split('''.''' ) _UpperCamelCase = int(items[0] ) _UpperCamelCase = int(items[1] ) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict: """simple docstring""" _UpperCamelCase = full_name.split('''adaptor.''' )[-1] _UpperCamelCase = name.split('''.''' ) if items[1].isdigit(): _UpperCamelCase = int(items[1] ) else: _UpperCamelCase = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' _UpperCamelCase = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' ) elif isinstance(__snake_case, __snake_case ): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase , _UpperCamelCase = emb.weight.shape _UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case ) _UpperCamelCase = emb.weight.data return lin_layer @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = WavaVecaConfig.from_pretrained( __snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, ) _UpperCamelCase = MBartConfig.from_pretrained(__snake_case ) # load model _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={ '''config_yaml''': config_yaml_path, '''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path, '''load_pretrained_decoder_from''': None, }, ) _UpperCamelCase = model[0].eval() # load feature extractor _UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case ) # set weights for wav2vec2 encoder _UpperCamelCase = WavaVecaModel(__snake_case ) recursively_load_weights_wavaveca(model.encoder, __snake_case ) # load decoder weights _UpperCamelCase = MBartForCausalLM(__snake_case ) _UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case ) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' ) logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' ) _UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case ) _UpperCamelCase = False _UpperCamelCase = MBartaaTokenizer(__snake_case ) tokenizer.save_pretrained(__snake_case ) _UpperCamelCase = hf_wavavec.config.to_dict() _UpperCamelCase = tokenizer.pad_token_id _UpperCamelCase = tokenizer.bos_token_id _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = '''mbart50''' _UpperCamelCase = '''wav2vec2''' _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = 25_00_04 _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case ) hf_wavavec.save_pretrained(__snake_case ) feature_extractor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""") parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""") parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""") parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""") parser.add_argument( """--encoder_config_path""", default="""facebook/wav2vec2-xls-r-1b""", type=str, help="""Path to hf encoder wav2vec2 checkpoint config""", ) parser.add_argument( """--decoder_config_path""", default="""facebook/mbart-large-50-one-to-many-mmt""", type=str, help="""Path to hf decoder checkpoint config""", ) parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""") parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""") parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""") parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""") parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""") _a = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )
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'''simple docstring''' import copy import os import tempfile from unittest import TestCase from unittest.mock import patch import numpy as np import pyarrow as pa import pyarrow.parquet as pq import pytest from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence from datasets.features import ArrayaD, ClassLabel, Features, Image, Value from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects from datasets.keyhash import DuplicatedKeysError, InvalidKeyError from .utils import require_pil class A ( _a ): def __lowerCAmelCase ( self : Any ) -> Optional[int]: """simple docstring""" _a = pa.array(TypedSequence([1, 2, 3] ) ) self.assertEqual(arr.type , pa.intaa() ) def __lowerCAmelCase ( self : Tuple ) -> List[str]: """simple docstring""" with self.assertRaises(__a ): _a = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() ) def __lowerCAmelCase ( self : List[str] ) -> Any: """simple docstring""" with self.assertRaises(__a ): _a = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''bool''' ) , type=Value('''int64''' ) ) ) def __lowerCAmelCase ( self : List[str] ) -> Tuple: """simple docstring""" _a = pa.array(TypedSequence([1, 2, 3] , type=Value('''int32''' ) ) ) self.assertEqual(arr.type , pa.intaa() ) def __lowerCAmelCase ( self : List[str] ) -> int: """simple docstring""" with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ): _a = pa.array(TypedSequence(['''foo''', '''bar'''] , type=Value('''int64''' ) ) ) def __lowerCAmelCase ( self : str ) -> int: """simple docstring""" _a = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''int32''' ) ) ) self.assertEqual(arr.type , pa.intaa() ) def __lowerCAmelCase ( self : Tuple ) -> Tuple: """simple docstring""" _a = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=Value('''int64''' ) ) ) self.assertEqual(arr.type , pa.string() ) def __lowerCAmelCase ( self : Any ) -> Any: """simple docstring""" _a = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , '''int64''' ) ) ) self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) ) def __lowerCAmelCase ( self : str ) -> Optional[int]: """simple docstring""" with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ): _a = pa.array(TypedSequence(['''foo''', '''bar'''] , type=ArrayaD((1, 3) , '''int64''' ) ) ) def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict: """simple docstring""" _a = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , '''int64''' ) ) ) self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) ) def __lowerCAmelCase ( self : Dict ) -> Tuple: """simple docstring""" _a = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=ArrayaD((1, 3) , '''int64''' ) ) ) self.assertEqual(arr.type , pa.string() ) @require_pil def __lowerCAmelCase ( self : Optional[int] ) -> List[Any]: """simple docstring""" import PIL.Image _a = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) ) with patch( '''datasets.arrow_writer.cast_to_python_objects''' , side_effect=__a ) as mock_cast_to_python_objects: _a = pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image] , type=Image() ) ) _a , _a = mock_cast_to_python_objects.call_args_list[-1] self.assertIn('''optimize_list_casting''' , __a ) self.assertFalse(kwargs['''optimize_list_casting'''] ) def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Any ): '''simple docstring''' _a = pa.BufferReader(__snake_case ) if isinstance(__snake_case , pa.Buffer ) else pa.memory_map(__snake_case ) _a = pa.ipc.open_stream(__snake_case ) _a = f.read_all() assert len(pa_table.to_batches() ) == expected_num_chunks assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]} del pa_table @pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] ) @pytest.mark.parametrize( '''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] ) def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : Any ): '''simple docstring''' _a = pa.BufferOutputStream() _a = pa.schema(__snake_case ) if fields else None with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer: writer.write({'''col_1''': '''foo''', '''col_2''': 1} ) writer.write({'''col_1''': '''bar''', '''col_2''': 2} ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 if not fields: _a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()} assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata ) _check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 ) def snake_case_ (): '''simple docstring''' _a = pa.BufferOutputStream() _a = Features({'''labels''': ClassLabel(names=['''neg''', '''pos'''] )} ) with ArrowWriter(stream=__snake_case , features=__snake_case ) as writer: writer.write({'''labels''': 0} ) writer.write({'''labels''': 1} ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 assert writer._schema == features.arrow_schema assert writer._schema.metadata == features.arrow_schema.metadata _a = pa.BufferReader(output.getvalue() ) _a = pa.ipc.open_stream(__snake_case ) _a = f.read_all() _a = pa_table.schema assert pa_table.num_rows == 2 assert schema == features.arrow_schema assert schema.metadata == features.arrow_schema.metadata assert features == Features.from_arrow_schema(__snake_case ) @pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] ) def snake_case_ (UpperCamelCase : Dict ): '''simple docstring''' _a = pa.BufferOutputStream() with ArrowWriter( stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer: with pytest.raises(__snake_case ): writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=[1, 2] ) _a , _a = writer.finalize() @pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] ) def snake_case_ (UpperCamelCase : int ): '''simple docstring''' _a = pa.BufferOutputStream() with ArrowWriter( stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer: with pytest.raises(__snake_case ): writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=10 ) writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=10 ) _a , _a = writer.finalize() @pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] ) def snake_case_ (UpperCamelCase : List[Any] ): '''simple docstring''' _a = pa.BufferOutputStream() with ArrowWriter( stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer: writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=1 ) writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=2 ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 _check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 ) @pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] ) @pytest.mark.parametrize( '''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] ) def snake_case_ (UpperCamelCase : int , UpperCamelCase : Dict ): '''simple docstring''' _a = pa.BufferOutputStream() _a = pa.schema(__snake_case ) if fields else None with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer: writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} ) writer.write_batch({'''col_1''': [], '''col_2''': []} ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 if not fields: _a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()} assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata ) _check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 ) @pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] ) @pytest.mark.parametrize( '''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] ) def snake_case_ (UpperCamelCase : Tuple , UpperCamelCase : str ): '''simple docstring''' _a = pa.BufferOutputStream() _a = pa.schema(__snake_case ) if fields else None with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer: writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} ) ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 if not fields: _a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()} assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata ) _check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 ) @pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] ) @pytest.mark.parametrize( '''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] ) def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : int ): '''simple docstring''' _a = pa.BufferOutputStream() _a = pa.schema(__snake_case ) if fields else None with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer: writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]} ) ) writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]} ) ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 if not fields: _a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()} assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata ) _check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 ) def snake_case_ (): '''simple docstring''' with tempfile.TemporaryDirectory() as tmp_dir: _a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()} _a = os.path.join(__snake_case , '''test.arrow''' ) with ArrowWriter(path=__snake_case , schema=pa.schema(__snake_case ) ) as writer: writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata ) _check_output(__snake_case , 1 ) def snake_case_ (UpperCamelCase : List[str] ): '''simple docstring''' if pa.types.is_list(__snake_case ): return get_base_dtype(arr_type.value_type ) else: return arr_type def snake_case_ (UpperCamelCase : List[str] , UpperCamelCase : Tuple ): '''simple docstring''' if isinstance(lst[0] , __snake_case ): change_first_primitive_element_in_list(lst[0] , __snake_case ) else: _a = value @pytest.mark.parametrize('''optimized_int_type, expected_dtype''' , [(None, pa.intaa()), (Value('''int32''' ), pa.intaa())] ) @pytest.mark.parametrize('''sequence''' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] ) def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : List[Any] ): '''simple docstring''' _a = pa.array(TypedSequence(__snake_case , optimized_int_type=__snake_case ) ) assert get_base_dtype(arr.type ) == expected_dtype @pytest.mark.parametrize( '''col, expected_dtype''' , [ ('''attention_mask''', pa.inta()), ('''special_tokens_mask''', pa.inta()), ('''token_type_ids''', pa.inta()), ('''input_ids''', pa.intaa()), ('''other''', pa.intaa()), ] , ) @pytest.mark.parametrize('''sequence''' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] ) def snake_case_ (UpperCamelCase : int , UpperCamelCase : str , UpperCamelCase : Tuple ): '''simple docstring''' _a = pa.array(OptimizedTypedSequence(__snake_case , col=__snake_case ) ) assert get_base_dtype(arr.type ) == expected_dtype # not in range if col != "other": # avoids errors due to in-place modifications _a = copy.deepcopy(__snake_case ) _a = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1 change_first_primitive_element_in_list(__snake_case , __snake_case ) _a = pa.array(OptimizedTypedSequence(__snake_case , col=__snake_case ) ) assert get_base_dtype(arr.type ) == pa.intaa() @pytest.mark.parametrize('''raise_exception''' , [False, True] ) def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : Optional[int] ): '''simple docstring''' _a = str(tmp_path / '''dataset-train.arrow''' ) try: with ArrowWriter(path=__snake_case ) as writer: if raise_exception: raise pa.lib.ArrowInvalid() else: writer.stream.close() except pa.lib.ArrowInvalid: pass finally: assert writer.stream.closed def snake_case_ (UpperCamelCase : Union[str, Any] ): '''simple docstring''' _a = '''mock://dataset-train.arrow''' with ArrowWriter(path=__snake_case , storage_options=mockfs.storage_options ) as writer: assert isinstance(writer._fs , type(__snake_case ) ) assert writer._fs.storage_options == mockfs.storage_options writer.write({'''col_1''': '''foo''', '''col_2''': 1} ) writer.write({'''col_1''': '''bar''', '''col_2''': 2} ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 assert mockfs.exists(__snake_case ) def snake_case_ (): '''simple docstring''' _a = pa.BufferOutputStream() with ParquetWriter(stream=__snake_case ) as writer: writer.write({'''col_1''': '''foo''', '''col_2''': 1} ) writer.write({'''col_1''': '''bar''', '''col_2''': 2} ) _a , _a = writer.finalize() assert num_examples == 2 assert num_bytes > 0 _a = pa.BufferReader(output.getvalue() ) _a = pq.read_table(__snake_case ) assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]} @require_pil @pytest.mark.parametrize('''embed_local_files''' , [False, True] ) def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : str ): '''simple docstring''' import PIL.Image _a = str(tmp_path / '''test_image_rgb.jpg''' ) PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(__snake_case , format='''png''' ) _a = pa.BufferOutputStream() with ParquetWriter( stream=__snake_case , features=Features({'''image''': Image()} ) , embed_local_files=__snake_case ) as writer: writer.write({'''image''': image_path} ) writer.finalize() _a = pa.BufferReader(output.getvalue() ) _a = pq.read_table(__snake_case ) _a = pa_table.to_pydict() if embed_local_files: assert isinstance(out['''image'''][0]['''path'''] , __snake_case ) with open(__snake_case , '''rb''' ) as f: assert out["image"][0]["bytes"] == f.read() else: assert out["image"][0]["path"] == image_path assert out["image"][0]["bytes"] is None def snake_case_ (): '''simple docstring''' _a = pa.schema([pa.field('''col_1''' , pa.string() , nullable=__snake_case )] ) _a = pa.BufferOutputStream() with ArrowWriter(stream=__snake_case ) as writer: writer._build_writer(inferred_schema=__snake_case ) assert writer._schema == pa.schema([pa.field('''col_1''' , pa.string() )] )
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"""simple docstring""" import fire from utils import calculate_rouge, save_json def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]: """simple docstring""" _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()] _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )] _UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case ) if save_path is not None: save_json(__snake_case, __snake_case, indent=__snake_case ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)
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0
'''simple docstring''' from __future__ import annotations def __a ( A__ , A__ , A__ ) -> tuple[float, list[float]]: lowerCAmelCase = list(range(len(__snake_case ) ) ) lowerCAmelCase = [v / w for v, w in zip(__snake_case , __snake_case )] index.sort(key=lambda A__ : ratio[i] , reverse=__snake_case ) lowerCAmelCase = 0 lowerCAmelCase = [0] * len(__snake_case ) for i in index: if weight[i] <= capacity: lowerCAmelCase = 1 max_value += value[i] capacity -= weight[i] else: lowerCAmelCase = capacity / weight[i] max_value += value[i] * capacity / weight[i] break return max_value, fractions if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['image_processor', 'tokenizer'] lowercase__ = 'ViTImageProcessor' lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast') def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = None if "feature_extractor" in kwargs: warnings.warn( '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`''' ''' instead.''' , __a , ) _UpperCamelCase = kwargs.pop('''feature_extractor''') _UpperCamelCase = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError('''You need to specify an `image_processor`.''') if tokenizer is None: raise ValueError('''You need to specify a `tokenizer`.''') super().__init__(__a , __a) def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple: '''simple docstring''' if text is None and visual_prompt is None and images is None: raise ValueError('''You have to specify either text, visual prompt or images.''') if text is not None and visual_prompt is not None: raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''') if text is not None: _UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a) if visual_prompt is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if images is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if visual_prompt is not None and images is not None: _UpperCamelCase = { '''pixel_values''': image_features.pixel_values, '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding elif text is not None and images is not None: _UpperCamelCase = image_features.pixel_values return encoding elif text is not None: return encoding elif visual_prompt is not None: _UpperCamelCase = { '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding else: return BatchEncoding(data=dict(**__a) , tensor_type=__a) def UpperCAmelCase ( self , *__a , **__a) -> Any: '''simple docstring''' return self.tokenizer.batch_decode(*__a , **__a) def UpperCAmelCase ( self , *__a , **__a) -> List[str]: '''simple docstring''' return self.tokenizer.decode(*__a , **__a) @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , ) return self.image_processor_class @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , ) return self.image_processor
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0
'''simple docstring''' import sys from pathlib import Path UpperCamelCase__ : Optional[Any] = Path(__file__).resolve().parents[3] / 'src' sys.path.insert(1, str(git_repo_path)) import dataclasses # noqa import io # noqa import itertools # noqa import json # noqa import os # noqa import unittest # noqa from copy import deepcopy # noqa from parameterized import parameterized # noqa from transformers import TrainingArguments, is_torch_available # noqa from transformers.deepspeed import is_deepspeed_available # noqa from transformers.file_utils import WEIGHTS_NAME # noqa from transformers.testing_utils import ( # noqa CaptureLogger, ExtendSysPath, TestCasePlus, execute_subprocess_async, get_gpu_count, mockenv_context, require_deepspeed, require_torch_gpu, require_torch_multi_gpu, slow, ) from transformers.trainer_utils import set_seed # noqa set_seed(42) UpperCamelCase__ : Optional[Any] = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'} UpperCamelCase__ : Optional[Any] = 'zero2' UpperCamelCase__ : int = 'zero3' UpperCamelCase__ : Union[str, Any] = [ZEROa, ZEROa] def __UpperCamelCase( _A : List[Any] , _A : Optional[int] , _A : Dict ): '''simple docstring''' UpperCAmelCase__ : Dict = parameterized.to_safe_name('''_'''.join(str(__snake_case ) for x in param.args ) ) return F'''{func.__name__}_{param_based_name}''' # Cartesian-product of zero stages with models to test UpperCamelCase__ : List[Any] = list(itertools.product(stages, models.keys())) @slow @require_deepspeed @require_torch_gpu class _lowercase ( lowerCAmelCase ): '''simple docstring''' @parameterized.expand(__a ,name_func=__a ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> List[str]: '''simple docstring''' self.run_and_check( stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,) @require_torch_multi_gpu @parameterized.expand(__a ,name_func=__a ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> int: '''simple docstring''' self.run_and_check( stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,) @parameterized.expand(__a ,name_func=__a ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> List[str]: '''simple docstring''' self.run_and_check( stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,) @require_torch_multi_gpu @parameterized.expand(__a ,name_func=__a ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> Dict: '''simple docstring''' self.run_and_check( stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,) def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Tuple: '''simple docstring''' pass def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 10 ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,) -> Dict: '''simple docstring''' UpperCAmelCase__ : List[str] = models[model] UpperCAmelCase__ : Optional[int] = self.run_trainer( stage=__a ,model_name=__a ,eval_steps=__a ,num_train_epochs=1 ,distributed=__a ,fpaa=__a ,) self.do_checks(__a ) return output_dir def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 10 ,lowerCamelCase_ = 1 ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,) -> int: '''simple docstring''' UpperCAmelCase__ : Optional[Any] = self.get_auto_remove_tmp_dir('''./xxx''' ,after=__a ) UpperCAmelCase__ : str = f''' --model_name_or_path {model_name} --dataset_name hf-internal-testing/librispeech_asr_dummy --dataset_config_name clean --train_split_name validation --validation_split_name validation --output_dir {output_dir} --num_train_epochs {str(__a )} --per_device_train_batch_size 2 --per_device_eval_batch_size 2 --evaluation_strategy steps --learning_rate 5e-4 --warmup_steps 8 --orthography timit --preprocessing_num_workers 1 --group_by_length --freeze_feature_extractor --report_to none --save_steps 0 --eval_steps {eval_steps} --report_to none '''.split() if fpaa: args.extend(['''--fp16'''] ) # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true, # hence the separate config files UpperCAmelCase__ : Optional[int] = f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split() UpperCAmelCase__ : Optional[Any] = [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py'''] UpperCAmelCase__ : Optional[int] = self.get_launcher(__a ) UpperCAmelCase__ : Any = launcher + script + args + ds_args # keep for quick debug # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die execute_subprocess_async(__a ,env=self.get_env() ) return output_dir def lowerCAmelCase__ ( self ,lowerCamelCase_=False ) -> Tuple: '''simple docstring''' UpperCAmelCase__ : List[str] = min(2 ,get_gpu_count() ) if distributed else 1 return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()
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"""simple docstring""" import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import UperNetForSemanticSegmentation from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = num_channels _UpperCamelCase = num_stages _UpperCamelCase = hidden_sizes _UpperCamelCase = depths _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = out_features _UpperCamelCase = num_labels _UpperCamelCase = scope _UpperCamelCase = num_stages def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ConvNextConfig( num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return UperNetConfig( backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = UperNetForSemanticSegmentation(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size)) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else () lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = UperNetModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*__a) @unittest.skip(reason='''UperNet does not use inputs_embeds''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not support input and output embeddings''') def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> int: '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''') def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' pass @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' def check_hidden_states_output(__a , __a , __a): _UpperCamelCase = model_class(__a) model.to(__a) model.eval() with torch.no_grad(): _UpperCamelCase = model(**self._prepare_for_class(__a , __a)) _UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states _UpperCamelCase = self.model_tester.num_stages self.assertEqual(len(__a) , expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) _UpperCamelCase = _config_zero_init(configs_no_init.backbone_config) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @unittest.skip(reason='''UperNet does not have tied weights''') def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' pass @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = hf_hub_download( repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' ) _UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' ) return image @require_torch @require_vision @slow class _UpperCAmelCase( unittest.TestCase ): def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
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0
'''simple docstring''' import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpta.tokenization_gpta import GPTaTokenizer from transformers.testing_utils import require_keras_nlp, require_tf, slow if is_tf_available(): import tensorflow as tf if is_keras_nlp_available(): from transformers.models.gpta import TFGPTaTokenizer lowercase =['gpt2'] lowercase ='gpt2' if is_tf_available(): class __magic_name__ ( tf.Module ): def __init__( self , snake_case) -> Optional[Any]: '''simple docstring''' super().__init__() _UpperCAmelCase : int =tokenizer _UpperCAmelCase : Dict =AutoConfig.from_pretrained(__a) _UpperCAmelCase : Optional[int] =TFGPTaLMHeadModel.from_config(__a) @tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name='text'),)) def lowerCAmelCase ( self , snake_case) -> List[str]: '''simple docstring''' _UpperCAmelCase : Tuple =self.tokenizer(__a) _UpperCAmelCase : List[str] =tokenized['input_ids'].to_tensor() _UpperCAmelCase : Any =tf.cast(input_ids_dense > 0 , tf.intaa) # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN]) _UpperCAmelCase : Union[str, Any] =self.model(input_ids=__a , attention_mask=__a)['logits'] return outputs @require_tf @require_keras_nlp class __magic_name__ ( unittest.TestCase ): def lowerCAmelCase ( self) -> Optional[int]: '''simple docstring''' super().setUp() _UpperCAmelCase : Tuple =[GPTaTokenizer.from_pretrained(__a) for checkpoint in (TOKENIZER_CHECKPOINTS)] _UpperCAmelCase : str =[TFGPTaTokenizer.from_pretrained(__a) for checkpoint in TOKENIZER_CHECKPOINTS] assert len(self.tokenizers) == len(self.tf_tokenizers) _UpperCAmelCase : Any =[ 'This is a straightforward English test sentence.', 'This one has some weird characters\rto\nsee\r\nif those\u00E9break things.', 'Now we\'re going to add some Chinese: 一 二 三 一二三', 'And some much more rare Chinese: 齉 堃 齉堃', 'Je vais aussi écrire en français pour tester les accents', 'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ', ] _UpperCAmelCase : Optional[Any] =list(zip(self.test_sentences , self.test_sentences[::-1])) def lowerCAmelCase ( self) -> Dict: '''simple docstring''' for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers): for test_inputs in self.test_sentences: _UpperCAmelCase : List[Any] =tokenizer([test_inputs] , return_tensors='tf') _UpperCAmelCase : str =tf_tokenizer([test_inputs]) for key in python_outputs.keys(): # convert them to numpy to avoid messing with ragged tensors _UpperCAmelCase : Union[str, Any] =python_outputs[key].numpy() _UpperCAmelCase : Tuple =tf_outputs[key].numpy() self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape)) self.assertTrue(tf.reduce_all(tf.cast(__a , tf.intaa) == tf_outputs_values)) @slow def lowerCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for tf_tokenizer in self.tf_tokenizers: _UpperCAmelCase : Dict =tf.function(__a) for test_inputs in self.test_sentences: _UpperCAmelCase : Dict =tf.constant(__a) _UpperCAmelCase : Union[str, Any] =compiled_tokenizer(__a) _UpperCAmelCase : Dict =tf_tokenizer(__a) for key in eager_outputs.keys(): self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key])) @slow def lowerCAmelCase ( self) -> List[Any]: '''simple docstring''' for tf_tokenizer in self.tf_tokenizers: _UpperCAmelCase : str =ModelToSave(tokenizer=__a) _UpperCAmelCase : List[str] =tf.convert_to_tensor([self.test_sentences[0]]) _UpperCAmelCase : Tuple =model.serving(__a) # Build model with some sample inputs with TemporaryDirectory() as tempdir: _UpperCAmelCase : List[str] =Path(__a) / 'saved.model' tf.saved_model.save(__a , __a , signatures={'serving_default': model.serving}) _UpperCAmelCase : List[Any] =tf.saved_model.load(__a) _UpperCAmelCase : Union[str, Any] =loaded_model.signatures['serving_default'](__a)['output_0'] # We may see small differences because the loaded model is compiled, so we need an epsilon for the test self.assertTrue(tf.reduce_all(out == loaded_output)) @slow def lowerCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' for tf_tokenizer in self.tf_tokenizers: _UpperCAmelCase : Dict =tf.convert_to_tensor([self.test_sentences[0]]) _UpperCAmelCase : List[Any] =tf_tokenizer(__a) # Build model with some sample inputs _UpperCAmelCase : Union[str, Any] =tf_tokenizer.get_config() _UpperCAmelCase : List[Any] =TFGPTaTokenizer.from_config(__a) _UpperCAmelCase : Optional[int] =model_from_config(__a) for key in from_config_output.keys(): self.assertTrue(tf.reduce_all(from_config_output[key] == out[key])) @slow def lowerCAmelCase ( self) -> Optional[int]: '''simple docstring''' for tf_tokenizer in self.tf_tokenizers: # for the test to run _UpperCAmelCase : List[Any] =1_2_3_1_2_3 for max_length in [3, 5, 1_0_2_4]: _UpperCAmelCase : int =tf.convert_to_tensor([self.test_sentences[0]]) _UpperCAmelCase : Dict =tf_tokenizer(__a , max_length=__a) _UpperCAmelCase : Any =out['input_ids'].numpy().shape[1] assert out_length == max_length
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DDPMScheduler,) def UpperCAmelCase ( self , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 10_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''variance_type''': '''fixed_small''', '''clip_sample''': True, } config.update(**__a) return config def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for timesteps in [1, 5, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.check_over_configs(thresholding=__a) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=__a , prediction_type=__a , sample_max_value=__a , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for t in [0, 5_00, 9_99]: self.check_over_forward(time_step=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 258.9606) < 1e-2 assert abs(result_mean.item() - 0.3372) < 1e-3 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 202.0296) < 1e-2 assert abs(result_mean.item() - 0.2631) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=__a) _UpperCamelCase = scheduler.timesteps for i, timestep in enumerate(__a): if i == len(__a) - 1: _UpperCamelCase = -1 else: _UpperCamelCase = timesteps[i + 1] _UpperCamelCase = scheduler.previous_timestep(__a) _UpperCamelCase = prev_t.item() self.assertEqual(__a , __a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 51, 0] with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''): scheduler.set_timesteps(timesteps=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] _UpperCamelCase = len(__a) with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''): scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [scheduler.config.num_train_timesteps] with self.assertRaises( __a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ): scheduler.set_timesteps(timesteps=__a)
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from ...utils import is_torch_available, is_transformers_available if is_transformers_available() and is_torch_available(): from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
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"""simple docstring""" from __future__ import annotations from functools import lru_cache from math import ceil _a = 100 _a = set(range(3, NUM_PRIMES, 2)) primes.add(2) _a = 42 for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=1_00 ) def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} _UpperCamelCase = set() _UpperCamelCase = 42 _UpperCamelCase = 42 for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None: """simple docstring""" for number_to_partition in range(1, __snake_case ): if len(partition(__snake_case ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(F"""{solution() = }""")
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import os import tempfile import unittest from transformers import NezhaConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, NezhaForMaskedLM, NezhaForMultipleChoice, NezhaForNextSentencePrediction, NezhaForPreTraining, NezhaForQuestionAnswering, NezhaForSequenceClassification, NezhaForTokenClassification, NezhaModel, ) from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST class __magic_name__ : def __init__( self : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any]=13 , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : str=99 , UpperCamelCase__ : List[Any]=32 , UpperCamelCase__ : Dict=5 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : List[Any]=37 , UpperCamelCase__ : Any="gelu" , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : List[str]=1_28 , UpperCamelCase__ : Optional[Any]=32 , UpperCamelCase__ : int=16 , UpperCamelCase__ : int=2 , UpperCamelCase__ : Optional[int]=0.02 , UpperCamelCase__ : Optional[Any]=3 , UpperCamelCase__ : Tuple=4 , UpperCamelCase__ : List[str]=None , ) -> List[str]: '''simple docstring''' UpperCAmelCase = parent UpperCAmelCase = batch_size UpperCAmelCase = seq_length UpperCAmelCase = is_training UpperCAmelCase = use_input_mask UpperCAmelCase = use_token_type_ids UpperCAmelCase = use_labels UpperCAmelCase = vocab_size UpperCAmelCase = hidden_size UpperCAmelCase = num_hidden_layers UpperCAmelCase = num_attention_heads UpperCAmelCase = intermediate_size UpperCAmelCase = hidden_act UpperCAmelCase = hidden_dropout_prob UpperCAmelCase = attention_probs_dropout_prob UpperCAmelCase = max_position_embeddings UpperCAmelCase = type_vocab_size UpperCAmelCase = type_sequence_label_size UpperCAmelCase = initializer_range UpperCAmelCase = num_labels UpperCAmelCase = num_choices UpperCAmelCase = scope def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase = None if self.use_input_mask: UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase = None if self.use_token_type_ids: UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase = None UpperCAmelCase = None UpperCAmelCase = None if self.use_labels: UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Any: '''simple docstring''' return NezhaConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , ) def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[int]: '''simple docstring''' ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) = self.prepare_config_and_inputs() UpperCAmelCase = True UpperCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def SCREAMING_SNAKE_CASE_ ( self : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase = NezhaModel(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a ) UpperCAmelCase = model(__a , token_type_ids=__a ) UpperCAmelCase = model(__a ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) ) def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] , ) -> Dict: '''simple docstring''' UpperCAmelCase = True UpperCAmelCase = NezhaModel(__a ) model.to(__a ) model.eval() UpperCAmelCase = model( __a , attention_mask=__a , token_type_ids=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , ) UpperCAmelCase = model( __a , attention_mask=__a , token_type_ids=__a , encoder_hidden_states=__a , ) UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) ) def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Any , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] ) -> Dict: '''simple docstring''' UpperCAmelCase = NezhaForMaskedLM(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple ) -> Any: '''simple docstring''' UpperCAmelCase = NezhaForNextSentencePrediction(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = model( __a , attention_mask=__a , token_type_ids=__a , labels=__a , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) ) def SCREAMING_SNAKE_CASE_ ( self : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str ) -> Dict: '''simple docstring''' UpperCAmelCase = NezhaForPreTraining(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = model( __a , attention_mask=__a , token_type_ids=__a , labels=__a , next_sentence_label=__a , ) self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) ) def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Tuple ) -> List[Any]: '''simple docstring''' UpperCAmelCase = NezhaForQuestionAnswering(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = model( __a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int ) -> Dict: '''simple docstring''' UpperCAmelCase = self.num_labels UpperCAmelCase = NezhaForSequenceClassification(__a ) model.to(__a ) model.eval() UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any ) -> List[str]: '''simple docstring''' UpperCAmelCase = self.num_labels UpperCAmelCase = NezhaForTokenClassification(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase = self.num_choices UpperCAmelCase = NezhaForMultipleChoice(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase = model( __a , attention_mask=__a , token_type_ids=__a , labels=__a , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def SCREAMING_SNAKE_CASE_ ( self : int ) -> Tuple: '''simple docstring''' UpperCAmelCase = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) = config_and_inputs UpperCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class __magic_name__ ( A__, A__, A__, unittest.TestCase ): lowercase : Dict =( ( NezhaModel, NezhaForMaskedLM, NezhaForMultipleChoice, NezhaForNextSentencePrediction, NezhaForPreTraining, NezhaForQuestionAnswering, NezhaForSequenceClassification, NezhaForTokenClassification, ) if is_torch_available() else () ) lowercase : Dict =( { '''feature-extraction''': NezhaModel, '''fill-mask''': NezhaForMaskedLM, '''question-answering''': NezhaForQuestionAnswering, '''text-classification''': NezhaForSequenceClassification, '''token-classification''': NezhaForTokenClassification, '''zero-shot''': NezhaForSequenceClassification, } if is_torch_available() else {} ) lowercase : Any =True def SCREAMING_SNAKE_CASE_ ( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int=False ) -> str: '''simple docstring''' UpperCAmelCase = super()._prepare_for_class(__a , __a , return_labels=__a ) if return_labels: if model_class in get_values(__a ): UpperCAmelCase = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__a ) UpperCAmelCase = torch.zeros( self.model_tester.batch_size , dtype=torch.long , device=__a ) return inputs_dict def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> int: '''simple docstring''' UpperCAmelCase = NezhaModelTester(self ) UpperCAmelCase = ConfigTester(self , config_class=__a , hidden_size=37 ) def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Any: '''simple docstring''' self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> str: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Optional[int]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Any: '''simple docstring''' ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) = self.model_tester.prepare_config_and_inputs_for_decoder() UpperCAmelCase = None self.model_tester.create_and_check_model_as_decoder( __a , __a , __a , __a , __a , __a , __a , __a , __a , ) def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Any: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__a ) def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Tuple: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_next_sequence_prediction(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Tuple: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> str: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Any: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__a ) def SCREAMING_SNAKE_CASE_ ( self : int ) -> List[str]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__a ) @slow def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase = NezhaModel.from_pretrained(__a ) self.assertIsNotNone(__a ) @slow @require_torch_gpu def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Tuple: '''simple docstring''' UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # NezhaForMultipleChoice behaves incorrectly in JIT environments. if model_class == NezhaForMultipleChoice: return UpperCAmelCase = True UpperCAmelCase = model_class(config=__a ) UpperCAmelCase = self._prepare_for_class(__a , __a ) UpperCAmelCase = torch.jit.trace( __a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(__a , os.path.join(__a , "bert.pt" ) ) UpperCAmelCase = torch.jit.load(os.path.join(__a , "bert.pt" ) , map_location=__a ) loaded(inputs_dict["input_ids"].to(__a ) , inputs_dict["attention_mask"].to(__a ) ) @require_torch class __magic_name__ ( unittest.TestCase ): @slow def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase = NezhaModel.from_pretrained("sijunhe/nezha-cn-base" ) UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] ) UpperCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1]] ) with torch.no_grad(): UpperCAmelCase = model(__a , attention_mask=__a )[0] UpperCAmelCase = torch.Size((1, 6, 7_68) ) self.assertEqual(output.shape , __a ) UpperCAmelCase = torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1e-4 ) ) @slow def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Tuple: '''simple docstring''' UpperCAmelCase = NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base" ) UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] ) UpperCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1]] ) with torch.no_grad(): UpperCAmelCase = model(__a , attention_mask=__a )[0] UpperCAmelCase = torch.Size((1, 6, 2_11_28) ) self.assertEqual(output.shape , __a ) UpperCAmelCase = torch.tensor( [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1e-4 ) )
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"""simple docstring""" from collections.abc import Callable import numpy as np def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array: """simple docstring""" _UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) ) _UpperCamelCase = np.zeros((n + 1,) ) _UpperCamelCase = ya _UpperCamelCase = xa for k in range(__snake_case ): _UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] ) _UpperCamelCase = y[k] + ( (step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": __a: Any = argparse.ArgumentParser( description=( """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned""" """ Distillation""" ) ) parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""]) parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str) parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str) parser.add_argument("""--vocab_transform""", action="""store_true""") __a: int = parser.parse_args() if args.model_type == "bert": __a: Optional[int] = BertForMaskedLM.from_pretrained(args.model_name) __a: Dict = """bert""" else: raise ValueError("""args.model_type should be \"bert\".""") __a: Tuple = model.state_dict() __a: Union[str, Any] = {} for w in ["word_embeddings", "position_embeddings"]: __a: Optional[Any] = state_dict[F'{prefix}.embeddings.{w}.weight'] for w in ["weight", "bias"]: __a: List[Any] = state_dict[F'{prefix}.embeddings.LayerNorm.{w}'] __a: Optional[int] = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: __a: int = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}' ] __a: List[str] = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}' ] __a: Tuple = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}' ] __a: Union[str, Any] = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}' ] __a: Union[str, Any] = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}' ] __a: Tuple = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}' ] __a: str = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}' ] __a: int = state_dict[ F'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}' ] std_idx += 1 __a: Optional[int] = state_dict["""cls.predictions.decoder.weight"""] __a: Union[str, Any] = state_dict["""cls.predictions.bias"""] if args.vocab_transform: for w in ["weight", "bias"]: __a: Any = state_dict[F'cls.predictions.transform.dense.{w}'] __a: Any = state_dict[F'cls.predictions.transform.LayerNorm.{w}'] print(F'N layers selected for distillation: {std_idx}') print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}') print(F'Save transferred checkpoint to {args.dump_checkpoint}.') torch.save(compressed_sd, args.dump_checkpoint)
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"""simple docstring""" import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": _a = argparse.ArgumentParser( description=( """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned""" """ Distillation""" ) ) parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""]) parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str) parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str) parser.add_argument("""--vocab_transform""", action="""store_true""") _a = parser.parse_args() if args.model_type == "bert": _a = BertForMaskedLM.from_pretrained(args.model_name) _a = """bert""" else: raise ValueError("""args.model_type should be \"bert\".""") _a = model.state_dict() _a = {} for w in ["word_embeddings", "position_embeddings"]: _a = state_dict[F"""{prefix}.embeddings.{w}.weight"""] for w in ["weight", "bias"]: _a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""] _a = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}""" ] std_idx += 1 _a = state_dict["""cls.predictions.decoder.weight"""] _a = state_dict["""cls.predictions.bias"""] if args.vocab_transform: for w in ["weight", "bias"]: _a = state_dict[F"""cls.predictions.transform.dense.{w}"""] _a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""] print(F"""N layers selected for distillation: {std_idx}""") print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""") print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""") torch.save(compressed_sd, args.dump_checkpoint)
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import os import shutil import sys import tempfile import unittest from pathlib import Path import pytest import transformers from transformers import ( BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoTokenizer, BertConfig, BertTokenizer, BertTokenizerFast, CTRLTokenizer, GPTaTokenizer, GPTaTokenizerFast, PreTrainedTokenizerFast, RobertaTokenizer, RobertaTokenizerFast, is_tokenizers_available, ) from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig from transformers.models.auto.tokenization_auto import ( TOKENIZER_MAPPING, get_tokenizer_config, tokenizer_class_from_name, ) from transformers.models.roberta.configuration_roberta import RobertaConfig from transformers.testing_utils import ( DUMMY_DIFF_TOKENIZER_IDENTIFIER, DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tokenizers, slow, ) sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils''')) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_tokenization import CustomTokenizer # noqa E402 if is_tokenizers_available(): from test_module.custom_tokenization_fast import CustomTokenizerFast class UpperCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def _snake_case ( self ): """simple docstring""" lowerCamelCase : Any = 0 @slow def _snake_case ( self ): """simple docstring""" for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x): lowerCamelCase : int = AutoTokenizer.from_pretrained(__a ) self.assertIsNotNone(__a ) self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) ) self.assertGreater(len(__a ) , 0 ) for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys(): lowerCamelCase : Any = AutoTokenizer.from_pretrained(__a ) self.assertIsNotNone(__a ) self.assertIsInstance(__a , (GPTaTokenizer, GPTaTokenizerFast) ) self.assertGreater(len(__a ) , 0 ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a ) self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) ) self.assertEqual(tokenizer.vocab_size , 12 ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a ) self.assertIsInstance(__a , (RobertaTokenizer, RobertaTokenizerFast) ) self.assertEqual(tokenizer.vocab_size , 20 ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Tuple = AutoConfig.from_pretrained(__a ) self.assertIsInstance(__a , __a ) # Check that tokenizer_type ≠ model_type lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained(__a , config=__a ) self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) ) self.assertEqual(tokenizer.vocab_size , 12 ) def _snake_case ( self ): """simple docstring""" with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.txt" , os.path.join(__a , "vocab.txt" ) ) lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(__a , tokenizer_type="bert" , use_fast=__a ) self.assertIsInstance(__a , __a ) with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.json" , os.path.join(__a , "vocab.json" ) ) shutil.copy("./tests/fixtures/merges.txt" , os.path.join(__a , "merges.txt" ) ) lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(__a , tokenizer_type="gpt2" , use_fast=__a ) self.assertIsInstance(__a , __a ) @require_tokenizers def _snake_case ( self ): """simple docstring""" with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.txt" , os.path.join(__a , "vocab.txt" ) ) lowerCamelCase : Dict = AutoTokenizer.from_pretrained(__a , tokenizer_type="bert" ) self.assertIsInstance(__a , __a ) with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.json" , os.path.join(__a , "vocab.json" ) ) shutil.copy("./tests/fixtures/merges.txt" , os.path.join(__a , "merges.txt" ) ) lowerCamelCase : int = AutoTokenizer.from_pretrained(__a , tokenizer_type="gpt2" ) self.assertIsInstance(__a , __a ) def _snake_case ( self ): """simple docstring""" with pytest.raises(__a ): AutoTokenizer.from_pretrained("./" , tokenizer_type="xxx" ) @require_tokenizers def _snake_case ( self ): """simple docstring""" for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]: lowerCamelCase : List[str] = tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased" ) self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) ) if isinstance(__a , __a ): self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , __a ) else: self.assertEqual(tokenizer.do_lower_case , __a ) self.assertEqual(tokenizer.model_max_length , 512 ) @require_tokenizers def _snake_case ( self ): """simple docstring""" for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]: with self.assertRaisesRegex( __a , "julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier" , ): lowerCamelCase : Tuple = tokenizer_class.from_pretrained("julien-c/herlolip-not-exists" ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Tuple = TOKENIZER_MAPPING.values() lowerCamelCase : int = [] for slow_tok, fast_tok in tokenizers: if slow_tok is not None: tokenizer_names.append(slow_tok.__name__ ) if fast_tok is not None: tokenizer_names.append(fast_tok.__name__ ) for tokenizer_name in tokenizer_names: # must find the right class tokenizer_class_from_name(__a ) @require_tokenizers def _snake_case ( self ): """simple docstring""" self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" , use_fast=__a ) , __a ) self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" ) , __a ) @require_tokenizers def _snake_case ( self ): """simple docstring""" lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained("distilbert-base-uncased" , do_lower_case=__a ) lowerCamelCase : List[str] = "Hello, world. How are you?" lowerCamelCase : str = tokenizer.tokenize(__a ) self.assertEqual("[UNK]" , tokens[0] ) lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("microsoft/mpnet-base" , do_lower_case=__a ) lowerCamelCase : Union[str, Any] = tokenizer.tokenize(__a ) self.assertEqual("[UNK]" , tokens[0] ) @require_tokenizers def _snake_case ( self ): """simple docstring""" lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config" ) self.assertEqual(type(__a ) , __a ) self.assertEqual(tokenizer.model_max_length , 512 ) self.assertEqual(tokenizer.vocab_size , 3_0000 ) self.assertEqual(tokenizer.unk_token , "[UNK]" ) self.assertEqual(tokenizer.padding_side , "right" ) self.assertEqual(tokenizer.truncation_side , "right" ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Any = AutoTokenizer.from_pretrained(__a ) self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) ) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(__a ) lowerCamelCase : List[str] = AutoTokenizer.from_pretrained(__a ) self.assertIsInstance(__a , tokenizer.__class__ ) self.assertEqual(tokenizera.vocab_size , 12 ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : str = AutoTokenizer.from_pretrained("ctrl" ) # There is no fast CTRL so this always gives us a slow tokenizer. self.assertIsInstance(__a , __a ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Optional[Any] = get_tokenizer_config("bert-base-cased" ) lowerCamelCase : Union[str, Any] = config.pop("_commit_hash" , __a ) # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated. self.assertEqual(__a , {"do_lower_case": False} ) # This model does not have a tokenizer_config so we get back an empty dict. lowerCamelCase : Optional[Any] = get_tokenizer_config(__a ) self.assertDictEqual(__a , {} ) # A tokenizer saved with `save_pretrained` always creates a tokenizer config. lowerCamelCase : str = AutoTokenizer.from_pretrained(__a ) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(__a ) lowerCamelCase : Optional[Any] = get_tokenizer_config(__a ) # Check the class of the tokenizer was properly saved (note that it always saves the slow class). self.assertEqual(config["tokenizer_class"] , "BertTokenizer" ) def _snake_case ( self ): """simple docstring""" try: AutoConfig.register("custom" , __a ) AutoTokenizer.register(__a , slow_tokenizer_class=__a ) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(__a ): AutoTokenizer.register(__a , slow_tokenizer_class=__a ) lowerCamelCase : Tuple = CustomTokenizer.from_pretrained(__a ) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(__a ) lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(__a ) self.assertIsInstance(__a , __a ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] @require_tokenizers def _snake_case ( self ): """simple docstring""" try: AutoConfig.register("custom" , __a ) # Can register in two steps AutoTokenizer.register(__a , slow_tokenizer_class=__a ) self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) ) AutoTokenizer.register(__a , fast_tokenizer_class=__a ) self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) ) del TOKENIZER_MAPPING._extra_content[CustomConfig] # Can register in one step AutoTokenizer.register( __a , slow_tokenizer_class=__a , fast_tokenizer_class=__a ) self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) ) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(__a ): AutoTokenizer.register(__a , fast_tokenizer_class=__a ) # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer # and that model does not have a tokenizer.json with tempfile.TemporaryDirectory() as tmp_dir: lowerCamelCase : List[str] = BertTokenizerFast.from_pretrained(__a ) bert_tokenizer.save_pretrained(__a ) lowerCamelCase : Optional[Any] = CustomTokenizerFast.from_pretrained(__a ) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(__a ) lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a ) self.assertIsInstance(__a , __a ) lowerCamelCase : int = AutoTokenizer.from_pretrained(__a , use_fast=__a ) self.assertIsInstance(__a , __a ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] def _snake_case ( self ): """simple docstring""" with self.assertRaises(__a ): lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" ) # If remote code is disabled, we can't load this config. with self.assertRaises(__a ): lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a ) lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a ) self.assertTrue(tokenizer.special_attribute_present ) # Test tokenizer can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(__a ) lowerCamelCase : Tuple = AutoTokenizer.from_pretrained(__a , trust_remote_code=__a ) self.assertTrue(reloaded_tokenizer.special_attribute_present ) if is_tokenizers_available(): self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" ) self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizerFast" ) # Test we can also load the slow version lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a , use_fast=__a ) self.assertTrue(tokenizer.special_attribute_present ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" ) # Test tokenizer can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(__a ) lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained(__a , trust_remote_code=__a , use_fast=__a ) self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizer" ) self.assertTrue(reloaded_tokenizer.special_attribute_present ) else: self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" ) self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizer" ) @require_tokenizers def _snake_case ( self ): """simple docstring""" class UpperCAmelCase_ ( UpperCamelCase ): '''simple docstring''' __A : List[Any] = False class UpperCAmelCase_ ( UpperCamelCase ): '''simple docstring''' __A : str = NewTokenizer __A : Optional[int] = False try: AutoConfig.register("custom" , __a ) AutoTokenizer.register(__a , slow_tokenizer_class=__a ) AutoTokenizer.register(__a , fast_tokenizer_class=__a ) # If remote code is not set, the default is to use local lowerCamelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" ) self.assertFalse(tokenizer.special_attribute_present ) lowerCamelCase : str = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" , use_fast=__a ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" ) self.assertFalse(tokenizer.special_attribute_present ) # If remote code is disabled, we load the local one. lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" ) self.assertFalse(tokenizer.special_attribute_present ) lowerCamelCase : int = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a , use_fast=__a ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" ) self.assertFalse(tokenizer.special_attribute_present ) # If remote is enabled, we load from the Hub lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" ) self.assertTrue(tokenizer.special_attribute_present ) lowerCamelCase : List[str] = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a , use_fast=__a ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" ) self.assertTrue(tokenizer.special_attribute_present ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] def _snake_case ( self ): """simple docstring""" lowerCamelCase : Tuple = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer_legacy" , trust_remote_code=__a ) self.assertTrue(tokenizer.special_attribute_present ) if is_tokenizers_available(): self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" ) # Test we can also load the slow version lowerCamelCase : Any = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer_legacy" , trust_remote_code=__a , use_fast=__a ) self.assertTrue(tokenizer.special_attribute_present ) self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" ) else: self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" ) def _snake_case ( self ): """simple docstring""" with self.assertRaisesRegex( __a , "bert-base is not a local folder and is not a valid model identifier" ): lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained("bert-base" ) def _snake_case ( self ): """simple docstring""" with self.assertRaisesRegex( __a , R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a , revision="aaaaaa" ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" ) with RequestCounter() as counter: lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" ) self.assertEqual(counter.get_request_count , 0 ) self.assertEqual(counter.head_request_count , 1 ) self.assertEqual(counter.other_request_count , 0 )
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"""simple docstring""" import unittest from transformers import PegasusConfig, PegasusTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html _a = """platform""" import jax import jax.numpy as jnp import numpy as np from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel @require_flax class _UpperCAmelCase: lowercase__ = PegasusConfig lowercase__ = {} lowercase__ = 'gelu' def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = eos_token_id _UpperCamelCase = pad_token_id _UpperCamelCase = bos_token_id def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size) _UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1) _UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , ) _UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a) return config, inputs_dict def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''') _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ] , axis=-1 , ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]: """simple docstring""" if attention_mask is None: _UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta ) if decoder_attention_mask is None: _UpperCamelCase = np.concatenate( [ np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ), np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ), ], axis=-1, ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_flax class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( FlaxPegasusForConditionalGeneration, FlaxPegasusModel, ) if is_flax_available() else () ) lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else () lowercase__ = True lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = FlaxPegasusModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = self._prepare_for_class(__a , __a) _UpperCamelCase = model_class(__a) @jax.jit def encode_jitted(__a , __a=None , **__a): return model.encode(input_ids=__a , attention_mask=__a) with self.subTest('''JIT Enabled'''): _UpperCamelCase = encode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = encode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = model_class(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask''']) _UpperCamelCase = { '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''], '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''], '''encoder_outputs''': encoder_outputs, } @jax.jit def decode_jitted(__a , __a , __a): return model.decode( decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , ) with self.subTest('''JIT Enabled'''): _UpperCamelCase = decode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = decode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) @slow def UpperCAmelCase ( self) -> int: '''simple docstring''' for model_class_name in self.all_model_classes: _UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a) _UpperCamelCase = np.ones((1, 1)) _UpperCamelCase = model(__a) self.assertIsNotNone(__a) @slow def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = [ ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''', ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''', ] _UpperCamelCase = [ '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''', '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''', ] _UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a) _UpperCamelCase = model.generate(**__a , num_beams=2).sequences _UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a) assert tgt_text == decoded
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import os from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home __snake_case : Optional[Any] =HUGGINGFACE_HUB_CACHE __snake_case : str ='config.json' __snake_case : Optional[int] ='diffusion_pytorch_model.bin' __snake_case : Dict ='diffusion_flax_model.msgpack' __snake_case : Any ='model.onnx' __snake_case : int ='diffusion_pytorch_model.safetensors' __snake_case : Optional[int] ='weights.pb' __snake_case : int ='https://huggingface.co' __snake_case : Optional[int] =default_cache_path __snake_case : Tuple ='diffusers_modules' __snake_case : Dict =os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules')) __snake_case : str =['fp16', 'non-ema'] __snake_case : int ='.self_attn'
647
"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_input_mask _UpperCamelCase = use_token_type_ids _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = type_vocab_size _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = num_labels _UpperCamelCase = num_choices _UpperCamelCase = scope _UpperCamelCase = projection_dim def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py _UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length]) _UpperCamelCase = None if self.use_token_type_ids: _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size) _UpperCamelCase = None _UpperCamelCase = None _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels) _UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices) _UpperCamelCase = BertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , ) _UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]: '''simple docstring''' _UpperCamelCase = TFDPRContextEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict: '''simple docstring''' _UpperCamelCase = TFDPRReader(config=__a) _UpperCamelCase = model(__a , attention_mask=__a) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,)) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''input_ids''': input_ids} return config, inputs_dict @require_tf class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__a) @slow def UpperCAmelCase ( self) -> str: '''simple docstring''' for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRReader.from_pretrained(__a) self.assertIsNotNone(__a) @require_tf class _UpperCAmelCase( unittest.TestCase ): @slow def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''') _UpperCamelCase = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP] _UpperCamelCase = model(__a)[0] # embedding shape = (1, 768) # compare the actual values for a slice. _UpperCamelCase = tf.constant( [ [ 0.0323_6253, 0.1275_3335, 0.1681_8509, 0.0027_9786, 0.389_6933, 0.2426_4945, 0.217_8971, -0.0233_5227, -0.0848_1959, -0.1432_4117, ] ]) self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
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'''simple docstring''' import hashlib import unittest from typing import Dict import numpy as np from transformers import ( MODEL_FOR_MASK_GENERATION_MAPPING, TF_MODEL_FOR_MASK_GENERATION_MAPPING, is_vision_available, pipeline, ) from transformers.pipelines import MaskGenerationPipeline from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_tf, require_torch, require_vision, slow, ) if is_vision_available(): from PIL import Image else: class UpperCAmelCase__ : """simple docstring""" @staticmethod def __lowercase ( *_a : Union[str, Any] ,**_a : str ): '''simple docstring''' pass def UpperCAmelCase_ (__a : int ): """simple docstring""" _a : Tuple = hashlib.mda(image.tobytes() ) return m.hexdigest()[:1_0] def UpperCAmelCase_ (__a : Optional[Any] ): """simple docstring""" _a : Optional[Any] = np.array(__snake_case ) _a : Optional[Any] = npimg.shape return {"hash": hashimage(__snake_case ), "shape": shape} @is_pipeline_test @require_vision @require_torch class UpperCAmelCase__ ( unittest.TestCase ): """simple docstring""" __UpperCAmelCase : List[str] = dict( (list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) ) __UpperCAmelCase : List[str] = dict( (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) ) def __lowercase ( self : str ,_a : Optional[int] ,_a : List[str] ,_a : Optional[int] ): '''simple docstring''' _a : Tuple = MaskGenerationPipeline(model=__a ,image_processor=__a ) return image_segmenter, [ "./tests/fixtures/tests_samples/COCO/000000039769.png", "./tests/fixtures/tests_samples/COCO/000000039769.png", ] def __lowercase ( self : Optional[int] ,_a : str ,_a : int ): '''simple docstring''' pass @require_tf @unittest.skip('Image segmentation not implemented in TF' ) def __lowercase ( self : int ): '''simple docstring''' pass @slow @require_torch def __lowercase ( self : Union[str, Any] ): '''simple docstring''' _a : Optional[Any] = pipeline('mask-generation' ,model='facebook/sam-vit-huge' ) _a : Any = image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,points_per_batch=256 ) # Shortening by hashing _a : Optional[int] = [] for i, o in enumerate(outputs['masks'] ): new_outupt += [{"mask": mask_to_test_readable(__a ), "scores": outputs["scores"][i]}] # fmt: off self.assertEqual( nested_simplify(__a ,decimals=4 ) ,[ {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444}, {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021}, {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167}, {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132}, {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053}, {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967}, {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993}, {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909}, {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879}, {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834}, {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716}, {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612}, {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599}, {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552}, {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532}, {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516}, {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499}, {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483}, {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464}, {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943}, {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943}, {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408}, {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335}, {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326}, {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262}, {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999}, {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986}, {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984}, {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873}, {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871} ] ,) # fmt: on @require_torch @slow def __lowercase ( self : int ): '''simple docstring''' _a : Dict = 'facebook/sam-vit-huge' _a : Union[str, Any] = pipeline('mask-generation' ,model=__a ) _a : str = image_segmenter( 'http://images.cocodataset.org/val2017/000000039769.jpg' ,pred_iou_thresh=1 ,points_per_batch=256 ) # Shortening by hashing _a : Union[str, Any] = [] for i, o in enumerate(outputs['masks'] ): new_outupt += [{"mask": mask_to_test_readable(__a ), "scores": outputs["scores"][i]}] self.assertEqual( nested_simplify(__a ,decimals=4 ) ,[ {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444}, {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210}, {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167}, {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132}, {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053}, ] ,)
229
"""simple docstring""" import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x2_0000 and cp <= 0x2_A6DF) # or (cp >= 0x2_A700 and cp <= 0x2_B73F) # or (cp >= 0x2_B740 and cp <= 0x2_B81F) # or (cp >= 0x2_B820 and cp <= 0x2_CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2_F800 and cp <= 0x2_FA1F) # ): # return True return False def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" for char in word: _UpperCamelCase = ord(__snake_case ) if not _is_chinese_char(__snake_case ): return 0 return 1 def lowerCamelCase__ ( __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = set() for token in tokens: _UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case ) if chinese_word: word_set.add(__snake_case ) _UpperCamelCase = list(__snake_case ) return word_list def lowerCamelCase__ ( __snake_case, __snake_case ) -> int: """simple docstring""" if not chinese_word_set: return bert_tokens _UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] ) _UpperCamelCase = bert_tokens _UpperCamelCase , _UpperCamelCase = 0, len(__snake_case ) while start < end: _UpperCamelCase = True if is_chinese(bert_word[start] ): _UpperCamelCase = min(end - start, __snake_case ) for i in range(__snake_case, 1, -1 ): _UpperCamelCase = ''''''.join(bert_word[start : start + i] ) if whole_word in chinese_word_set: for j in range(start + 1, start + i ): _UpperCamelCase = '''##''' + bert_word[j] _UpperCamelCase = start + i _UpperCamelCase = False break if single_word: start += 1 return bert_word def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws _UpperCamelCase = [get_chinese_word(__snake_case ) for r in res] ltp_res.extend(__snake_case ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 ) bert_res.extend(res['''input_ids'''] ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for input_ids, chinese_word in zip(__snake_case, __snake_case ): _UpperCamelCase = [] for id in input_ids: _UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case ) input_tokens.append(__snake_case ) _UpperCamelCase = add_sub_symbol(__snake_case, __snake_case ) _UpperCamelCase = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(__snake_case ): if token[:2] == "##": _UpperCamelCase = token[2:] # save chinese tokens' pos if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ): ref_id.append(__snake_case ) ref_ids.append(__snake_case ) assert len(__snake_case ) == len(__snake_case ) return ref_ids def lowerCamelCase__ ( __snake_case ) -> Optional[int]: """simple docstring""" with open(args.file_name, '''r''', encoding='''utf-8''' ) as f: _UpperCamelCase = f.readlines() _UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029' _UpperCamelCase = LTP(args.ltp ) # faster in GPU device _UpperCamelCase = BertTokenizer.from_pretrained(args.bert ) _UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case ) with open(args.save_path, '''w''', encoding='''utf-8''' ) as f: _UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids] f.writelines(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser(description="""prepare_chinese_ref""") parser.add_argument( """--file_name""", required=False, type=str, default="""./resources/chinese-demo.txt""", help="""file need process, same as training data in lm""", ) parser.add_argument( """--ltp""", required=False, type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path""", ) parser.add_argument( """--bert""", required=False, type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""", ) parser.add_argument( """--save_path""", required=False, type=str, default="""./resources/ref.txt""", help="""path to save res""", ) _a = parser.parse_args() main(args)
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import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils import torch_device from diffusers.utils.testing_utils import require_torchsde from .test_schedulers import SchedulerCommonTest @require_torchsde class lowercase ( a ): lowercase__ : Any = (DPMSolverSDEScheduler,) lowercase__ : int = 10 def __snake_case( self : Optional[int] , **_UpperCamelCase : Tuple ) -> int: '''simple docstring''' SCREAMING_SNAKE_CASE = { "num_train_timesteps": 1_100, "beta_start": 0.0_0_0_1, "beta_end": 0.0_2, "beta_schedule": "linear", "noise_sampler_seed": 0, } config.update(**__a ) return config def __snake_case( self : Tuple ) -> Union[str, Any]: '''simple docstring''' for timesteps in [10, 50, 100, 1_000]: self.check_over_configs(num_train_timesteps=__a ) def __snake_case( self : Any ) -> Dict: '''simple docstring''' for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] , [0.0_0_0_2, 0.0_0_2, 0.0_2] ): self.check_over_configs(beta_start=__a , beta_end=__a ) def __snake_case( self : Union[str, Any] ) -> Optional[int]: '''simple docstring''' for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=__a ) def __snake_case( self : Union[str, Any] ) -> str: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=__a ) def __snake_case( self : int ) -> Union[str, Any]: '''simple docstring''' SCREAMING_SNAKE_CASE = self.scheduler_classes[0] SCREAMING_SNAKE_CASE = self.get_scheduler_config() SCREAMING_SNAKE_CASE = scheduler_class(**__a ) scheduler.set_timesteps(self.num_inference_steps ) SCREAMING_SNAKE_CASE = self.dummy_model() SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma SCREAMING_SNAKE_CASE = sample.to(__a ) for i, t in enumerate(scheduler.timesteps ): SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a ) SCREAMING_SNAKE_CASE = model(__a , __a ) SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a ) SCREAMING_SNAKE_CASE = output.prev_sample SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) ) SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5 ) < 1e-2 assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7 ) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6 ) < 1e-2 assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2 ) < 1e-3 else: assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2 assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3 def __snake_case( self : Optional[int] ) -> List[str]: '''simple docstring''' SCREAMING_SNAKE_CASE = self.scheduler_classes[0] SCREAMING_SNAKE_CASE = self.get_scheduler_config(prediction_type="v_prediction" ) SCREAMING_SNAKE_CASE = scheduler_class(**__a ) scheduler.set_timesteps(self.num_inference_steps ) SCREAMING_SNAKE_CASE = self.dummy_model() SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma SCREAMING_SNAKE_CASE = sample.to(__a ) for i, t in enumerate(scheduler.timesteps ): SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a ) SCREAMING_SNAKE_CASE = model(__a , __a ) SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a ) SCREAMING_SNAKE_CASE = output.prev_sample SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) ) SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3 ) < 1e-2 assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4 ) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3 ) < 1e-2 assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7 ) < 1e-3 else: assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5 ) < 1e-2 assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1 ) < 1e-3 def __snake_case( self : List[str] ) -> Any: '''simple docstring''' SCREAMING_SNAKE_CASE = self.scheduler_classes[0] SCREAMING_SNAKE_CASE = self.get_scheduler_config() SCREAMING_SNAKE_CASE = scheduler_class(**__a ) scheduler.set_timesteps(self.num_inference_steps , device=__a ) SCREAMING_SNAKE_CASE = self.dummy_model() SCREAMING_SNAKE_CASE = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma for t in scheduler.timesteps: SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a ) SCREAMING_SNAKE_CASE = model(__a , __a ) SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a ) SCREAMING_SNAKE_CASE = output.prev_sample SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) ) SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8 ) < 1e-2 assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5 ) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2 ) < 1e-2 assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1 ) < 1e-3 else: assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2 assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3 def __snake_case( self : List[Any] ) -> Any: '''simple docstring''' SCREAMING_SNAKE_CASE = self.scheduler_classes[0] SCREAMING_SNAKE_CASE = self.get_scheduler_config() SCREAMING_SNAKE_CASE = scheduler_class(**__a , use_karras_sigmas=__a ) scheduler.set_timesteps(self.num_inference_steps , device=__a ) SCREAMING_SNAKE_CASE = self.dummy_model() SCREAMING_SNAKE_CASE = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma SCREAMING_SNAKE_CASE = sample.to(__a ) for t in scheduler.timesteps: SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a ) SCREAMING_SNAKE_CASE = model(__a , __a ) SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a ) SCREAMING_SNAKE_CASE = output.prev_sample SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) ) SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) ) if torch_device in ["mps"]: assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8 ) < 1e-2 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5 ) < 1e-2 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2 else: assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2 ) < 1e-2 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
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"""simple docstring""" import heapq def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" _UpperCamelCase = [] # for each node and his adjacency list add them and the rank of the node to queue # using heapq module the queue will be filled like a Priority Queue # heapq works with a min priority queue, so I used -1*len(v) to build it for key, value in graph.items(): # O(log(n)) heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] ) # chosen_vertices = set of chosen vertices _UpperCamelCase = set() # while queue isn't empty and there are still edges # (queue[0][0] is the rank of the node with max rank) while queue and queue[0][0] != 0: # extract vertex with max rank from queue and add it to chosen_vertices _UpperCamelCase = heapq.heappop(__snake_case )[1][0] chosen_vertices.add(__snake_case ) # Remove all arcs adjacent to argmax for elem in queue: # if v haven't adjacent node, skip if elem[0] == 0: continue # if argmax is reachable from elem # remove argmax from elem's adjacent list and update his rank if argmax in elem[1][1]: _UpperCamelCase = elem[1][1].index(__snake_case ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(__snake_case ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() _a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
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'''simple docstring''' from typing import Optional, Tuple, Union import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict from ..configuration_utils import ConfigMixin, flax_register_to_config from ..utils import BaseOutput from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps from .modeling_flax_utils import FlaxModelMixin from .unet_ad_blocks_flax import ( FlaxCrossAttnDownBlockaD, FlaxCrossAttnUpBlockaD, FlaxDownBlockaD, FlaxUNetMidBlockaDCrossAttn, FlaxUpBlockaD, ) @flax.struct.dataclass class A ( _a ): lowercase_ = 42 @flax_register_to_config class A ( nn.Module ,_a ,_a ): lowercase_ = 32 lowercase_ = 4 lowercase_ = 4 lowercase_ = ( 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D', ) lowercase_ = ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D') lowercase_ = False lowercase_ = (320, 640, 1280, 1280) lowercase_ = 2 lowercase_ = 8 lowercase_ = None lowercase_ = 1280 lowercase_ = 0.0 lowercase_ = False lowercase_ = jnp.floataa lowercase_ = True lowercase_ = 0 lowercase_ = False def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Optional[Any] ) -> FrozenDict: """simple docstring""" _a = (1, self.in_channels, self.sample_size, self.sample_size) _a = jnp.zeros(__a , dtype=jnp.floataa ) _a = jnp.ones((1,) , dtype=jnp.intaa ) _a = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa ) _a , _a = jax.random.split(__a ) _a = {'''params''': params_rng, '''dropout''': dropout_rng} return self.init(__a , __a , __a , __a )["params"] def __lowerCAmelCase ( self : List[Any] ) -> str: """simple docstring""" _a = self.block_out_channels _a = block_out_channels[0] * 4 if self.num_attention_heads is not None: raise ValueError( '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' ) # If `num_attention_heads` is not defined (which is the case for most models) # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. # The reason for this behavior is to correct for incorrectly named variables that were introduced # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking # which is why we correct for the naming here. _a = self.num_attention_heads or self.attention_head_dim # input _a = nn.Conv( block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , ) # time _a = FlaxTimesteps( block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift ) _a = FlaxTimestepEmbedding(__a , dtype=self.dtype ) _a = self.only_cross_attention if isinstance(__a , __a ): _a = (only_cross_attention,) * len(self.down_block_types ) if isinstance(__a , __a ): _a = (num_attention_heads,) * len(self.down_block_types ) # down _a = [] _a = block_out_channels[0] for i, down_block_type in enumerate(self.down_block_types ): _a = output_channel _a = block_out_channels[i] _a = i == len(__a ) - 1 if down_block_type == "CrossAttnDownBlock2D": _a = FlaxCrossAttnDownBlockaD( in_channels=__a , out_channels=__a , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , ) else: _a = FlaxDownBlockaD( in_channels=__a , out_channels=__a , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , ) down_blocks.append(__a ) _a = down_blocks # mid _a = FlaxUNetMidBlockaDCrossAttn( in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , ) # up _a = [] _a = list(reversed(__a ) ) _a = list(reversed(__a ) ) _a = list(reversed(__a ) ) _a = reversed_block_out_channels[0] for i, up_block_type in enumerate(self.up_block_types ): _a = output_channel _a = reversed_block_out_channels[i] _a = reversed_block_out_channels[min(i + 1 , len(__a ) - 1 )] _a = i == len(__a ) - 1 if up_block_type == "CrossAttnUpBlock2D": _a = FlaxCrossAttnUpBlockaD( in_channels=__a , out_channels=__a , prev_output_channel=__a , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , ) else: _a = FlaxUpBlockaD( in_channels=__a , out_channels=__a , prev_output_channel=__a , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , ) up_blocks.append(__a ) _a = output_channel _a = up_blocks # out _a = nn.GroupNorm(num_groups=32 , epsilon=1e-5 ) _a = nn.Conv( self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , ) def __call__( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : str = True , lowerCAmelCase_ : List[Any] = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]: """simple docstring""" if not isinstance(__a , jnp.ndarray ): _a = jnp.array([timesteps] , dtype=jnp.intaa ) elif isinstance(__a , jnp.ndarray ) and len(timesteps.shape ) == 0: _a = timesteps.astype(dtype=jnp.floataa ) _a = jnp.expand_dims(__a , 0 ) _a = self.time_proj(__a ) _a = self.time_embedding(__a ) # 2. pre-process _a = jnp.transpose(__a , (0, 2, 3, 1) ) _a = self.conv_in(__a ) # 3. down _a = (sample,) for down_block in self.down_blocks: if isinstance(__a , __a ): _a , _a = down_block(__a , __a , __a , deterministic=not train ) else: _a , _a = down_block(__a , __a , deterministic=not train ) down_block_res_samples += res_samples if down_block_additional_residuals is not None: _a = () for down_block_res_sample, down_block_additional_residual in zip( __a , __a ): down_block_res_sample += down_block_additional_residual new_down_block_res_samples += (down_block_res_sample,) _a = new_down_block_res_samples # 4. mid _a = self.mid_block(__a , __a , __a , deterministic=not train ) if mid_block_additional_residual is not None: sample += mid_block_additional_residual # 5. up for up_block in self.up_blocks: _a = down_block_res_samples[-(self.layers_per_block + 1) :] _a = down_block_res_samples[: -(self.layers_per_block + 1)] if isinstance(__a , __a ): _a = up_block( __a , temb=__a , encoder_hidden_states=__a , res_hidden_states_tuple=__a , deterministic=not train , ) else: _a = up_block(__a , temb=__a , res_hidden_states_tuple=__a , deterministic=not train ) # 6. post-process _a = self.conv_norm_out(__a ) _a = nn.silu(__a ) _a = self.conv_out(__a ) _a = jnp.transpose(__a , (0, 3, 1, 2) ) if not return_dict: return (sample,) return FlaxUNetaDConditionOutput(sample=__a )
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"""simple docstring""" from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join _UpperCamelCase = '''__test_patch_submodule_mock__''' with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os, _PatchedModuleObj ) assert isinstance(_test_patching.os.path, _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path, _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os, _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path, _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" assert _test_patching.open is open _UpperCamelCase = '''__test_patch_submodule_builtin_mock__''' # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching, '''open''', __snake_case ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def lowerCamelCase__ ( ) -> Union[str, Any]: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_mock__''' with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ): pass def lowerCamelCase__ ( ) -> Dict: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__''' # _test_patching doesn't have "len" in its globals assert getattr(_test_patching, '''len''', __snake_case ) is None with patch_submodule(_test_patching, '''len''', __snake_case ): assert _test_patching.len is mock assert _test_patching.len is len def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__''' _UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def lowerCamelCase__ ( ) -> Optional[int]: """simple docstring""" from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join _UpperCamelCase = '''__test_patch_submodule_successive_join__''' _UpperCamelCase = '''__test_patch_submodule_successive_dirname__''' _UpperCamelCase = '''__test_patch_submodule_successive_rename__''' assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def lowerCamelCase__ ( ) -> str: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__''' with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ): pass with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ): pass
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0
'''simple docstring''' def __a ( A__ , A__ ) -> int: lowerCAmelCase = "" for i in table: res += inp[i - 1] return res def __a ( A__ ) -> str: return data[1:] + data[0] def __a ( A__ , A__ ) -> Tuple: lowerCAmelCase = "" for i in range(len(__snake_case ) ): if a[i] == b[i]: res += "0" else: res += "1" return res def __a ( A__ , A__ ) -> List[Any]: lowerCAmelCase = int("0b" + data[0] + data[-1] , 2 ) lowerCAmelCase = int("0b" + data[1:3] , 2 ) return bin(s[row][col] )[2:] def __a ( A__ , A__ , A__ , A__ , A__ ) -> Dict: lowerCAmelCase = message[:4] lowerCAmelCase = message[4:] lowerCAmelCase = apply_table(__snake_case , __snake_case ) lowerCAmelCase = xor(__snake_case , __snake_case ) lowerCAmelCase = apply_sbox(__snake_case , temp[:4] ) # noqa: E741 lowerCAmelCase = apply_sbox(__snake_case , temp[4:] ) lowerCAmelCase = "0" * (2 - len(__snake_case )) + l # noqa: E741 lowerCAmelCase = "0" * (2 - len(__snake_case )) + r lowerCAmelCase = apply_table(l + r , __snake_case ) lowerCAmelCase = xor(__snake_case , __snake_case ) return temp + right if __name__ == "__main__": lowercase : str = input('Enter 10 bit key: ') lowercase : str = input('Enter 8 bit message: ') lowercase : int = [6, 3, 7, 4, 8, 5, 1_0, 9] lowercase : List[Any] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6] lowercase : int = [2, 4, 3, 1] lowercase : Dict = [2, 6, 3, 1, 4, 8, 5, 7] lowercase : Union[str, Any] = [4, 1, 3, 5, 7, 2, 8, 6] lowercase : Tuple = [4, 1, 2, 3, 2, 3, 4, 1] lowercase : Dict = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]] lowercase : Tuple = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]] # key generation lowercase : Optional[Any] = apply_table(key, paa_table) lowercase : Union[str, Any] = temp[:5] lowercase : Union[str, Any] = temp[5:] lowercase : str = left_shift(left) lowercase : Any = left_shift(right) lowercase : List[Any] = apply_table(left + right, pa_table) lowercase : str = left_shift(left) lowercase : Any = left_shift(right) lowercase : List[Any] = left_shift(left) lowercase : List[Any] = left_shift(right) lowercase : Any = apply_table(left + right, pa_table) # encryption lowercase : Tuple = apply_table(message, IP) lowercase : Union[str, Any] = function(expansion, sa, sa, keya, temp) lowercase : List[Any] = temp[4:] + temp[:4] lowercase : Optional[Any] = function(expansion, sa, sa, keya, temp) lowercase : List[Any] = apply_table(temp, IP_inv) print('Cipher text is:', CT) # decryption lowercase : str = apply_table(CT, IP) lowercase : Tuple = function(expansion, sa, sa, keya, temp) lowercase : Optional[int] = temp[4:] + temp[:4] lowercase : Any = function(expansion, sa, sa, keya, temp) lowercase : str = apply_table(temp, IP_inv) print('Plain text after decypting is:', PT)
649
"""simple docstring""" import argparse import json from collections import OrderedDict from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor from transformers.utils import logging logging.set_verbosity_info() _a = logging.get_logger(__name__) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = original_name.split('''.''' )[0] _UpperCamelCase = key.split('''.''' ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] ) _UpperCamelCase = orig_block_num - offset _UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' ) return key def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase = OrderedDict() _UpperCamelCase , _UpperCamelCase = 0, 0 for key, value in state_dict.items(): if key.startswith('''network''' ): _UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' ) if "proj" in key: # Works for the first embedding as well as the internal embedding layers if key.endswith('''bias''' ) and "patch_embed" not in key: patch_emb_offset += 1 _UpperCamelCase = key[: key.find('''proj''' )] _UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' ) _UpperCamelCase = key.replace('''proj''', '''projection''' ) if key.endswith('''bias''' ): total_embed_found += 1 if "patch_embeddings" in key: _UpperCamelCase = '''poolformer.encoder.''' + key if "mlp.fc1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' ) if "mlp.fc2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' ) if "norm1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' ) if "norm2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' ) if "layer_scale_1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' ) if "layer_scale_2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' ) if "head" in key: _UpperCamelCase = key.replace('''head''', '''classifier''' ) _UpperCamelCase = value return new_state_dict def lowerCamelCase__ ( ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg''' _UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw ) return image @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int: """simple docstring""" _UpperCamelCase = PoolFormerConfig() # set attributes based on model_name _UpperCamelCase = '''huggingface/label-files''' _UpperCamelCase = model_name[-3:] _UpperCamelCase = 10_00 _UpperCamelCase = '''imagenet-1k-id2label.json''' _UpperCamelCase = (1, 10_00) # set config attributes _UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) ) _UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()} _UpperCamelCase = idalabel _UpperCamelCase = {v: k for k, v in idalabel.items()} if size == "s12": _UpperCamelCase = [2, 2, 6, 2] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s24": _UpperCamelCase = [4, 4, 12, 4] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.9 elif size == "m36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 elif size == "m48": _UpperCamelCase = [8, 8, 24, 8] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 else: raise ValueError(F'''Size {size} not supported''' ) # load image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) # Prepare image _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values logger.info(F'''Converting model {model_name}...''' ) # load original state dict _UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) ) # rename keys _UpperCamelCase = rename_keys(__snake_case ) # create HuggingFace model and load state dict _UpperCamelCase = PoolFormerForImageClassification(__snake_case ) model.load_state_dict(__snake_case ) model.eval() # Define image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) _UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values # forward pass _UpperCamelCase = model(__snake_case ) _UpperCamelCase = outputs.logits # define expected logit slices for different models if size == "s12": _UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] ) elif size == "s24": _UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] ) elif size == "s36": _UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] ) elif size == "m36": _UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] ) elif size == "m48": _UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] ) else: raise ValueError(F'''Size {size} not supported''' ) # verify logits assert logits.shape == expected_shape assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 ) # finally, save model and image processor logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' ) Path(__snake_case ).mkdir(exist_ok=__snake_case ) model.save_pretrained(__snake_case ) print(F'''Saving image processor to {pytorch_dump_folder_path}''' ) image_processor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument( """--model_name""", default="""poolformer_s12""", type=str, help="""Name of the model you'd like to convert.""", ) parser.add_argument( """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file).""" ) parser.add_argument( """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model.""" ) _a = parser.parse_args() convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
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0
'''simple docstring''' from __future__ import annotations from functools import lru_cache from math import ceil UpperCamelCase__ : int = 100 UpperCamelCase__ : List[Any] = set(range(3, NUM_PRIMES, 2)) primes.add(2) UpperCamelCase__ : Dict = 42 for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=1_00 ) def __UpperCamelCase( _A : Optional[int] ): '''simple docstring''' if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} UpperCAmelCase__ : Optional[Any] = set() UpperCAmelCase__ : List[Any] = 42 UpperCAmelCase__ : int = 42 for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def __UpperCamelCase( _A : Optional[int] = 50_00 ): '''simple docstring''' for number_to_partition in range(1 , __snake_case ): if len(partition(__snake_case ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(f"""{solution() = }""")
614
"""simple docstring""" import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils import torch_device from diffusers.utils.testing_utils import require_torchsde from .test_schedulers import SchedulerCommonTest @require_torchsde class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DPMSolverSDEScheduler,) lowercase__ = 10 def UpperCAmelCase ( self , **__a) -> int: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 11_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''noise_sampler_seed''': 0, } config.update(**__a) return config def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' for timesteps in [10, 50, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> str: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2 assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2 assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2 assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3 else: assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2 assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2 assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 else: assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
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'''simple docstring''' import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging lowercase =logging.get_logger(__name__) lowercase ='▁' lowercase ={'vocab_file': 'sentencepiece.bpe.model'} lowercase ={ 'vocab_file': { 'facebook/mbart-large-en-ro': ( 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model' ), 'facebook/mbart-large-cc25': ( 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model' ), } } lowercase ={ 'facebook/mbart-large-en-ro': 1024, 'facebook/mbart-large-cc25': 1024, } # fmt: off lowercase =['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN'] class __magic_name__ ( lowerCAmelCase ): UpperCAmelCase =VOCAB_FILES_NAMES UpperCAmelCase =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCAmelCase =PRETRAINED_VOCAB_FILES_MAP UpperCAmelCase =["input_ids", "attention_mask"] UpperCAmelCase =[] UpperCAmelCase =[] def __init__( self , snake_case , snake_case="<s>" , snake_case="</s>" , snake_case="</s>" , snake_case="<s>" , snake_case="<unk>" , snake_case="<pad>" , snake_case="<mask>" , snake_case=None , snake_case=None , snake_case=None , snake_case = None , snake_case=None , **snake_case , ) -> Optional[Any]: '''simple docstring''' _UpperCAmelCase : Optional[int] =AddedToken(__a , lstrip=__a , rstrip=__a) if isinstance(__a , __a) else mask_token _UpperCAmelCase : List[Any] ={} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__a , eos_token=__a , unk_token=__a , sep_token=__a , cls_token=__a , pad_token=__a , mask_token=__a , tokenizer_file=__a , src_lang=__a , tgt_lang=__a , additional_special_tokens=__a , sp_model_kwargs=self.sp_model_kwargs , **__a , ) _UpperCAmelCase : str =spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(__a)) _UpperCAmelCase : Tuple =vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token _UpperCAmelCase : Tuple ={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab _UpperCAmelCase : Optional[int] =1 _UpperCAmelCase : Any =len(self.sp_model) _UpperCAmelCase : List[Any] ={ code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a) } _UpperCAmelCase : List[Any] ={v: k for k, v in self.lang_code_to_id.items()} _UpperCAmelCase : Optional[Any] =len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset self.fairseq_tokens_to_ids.update(self.lang_code_to_id) _UpperCAmelCase : List[Any] ={v: k for k, v in self.fairseq_tokens_to_ids.items()} _UpperCAmelCase : List[str] =list(self.lang_code_to_id.keys()) if additional_special_tokens is not None: # Only add those special tokens if they are not already there. self._additional_special_tokens.extend( [t for t in additional_special_tokens if t not in self._additional_special_tokens]) _UpperCAmelCase : Tuple =src_lang if src_lang is not None else 'en_XX' _UpperCAmelCase : List[Any] =self.lang_code_to_id[self._src_lang] _UpperCAmelCase : Optional[Any] =tgt_lang self.set_src_lang_special_tokens(self._src_lang) def __getstate__( self) -> Optional[int]: '''simple docstring''' _UpperCAmelCase : Dict =self.__dict__.copy() _UpperCAmelCase : Optional[int] =None _UpperCAmelCase : int =self.sp_model.serialized_model_proto() return state def __setstate__( self , snake_case) -> int: '''simple docstring''' _UpperCAmelCase : List[Any] =d # for backward compatibility if not hasattr(self , 'sp_model_kwargs'): _UpperCAmelCase : int ={} _UpperCAmelCase : int =spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @property def lowerCAmelCase ( self) -> Dict: '''simple docstring''' return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token @property def lowerCAmelCase ( self) -> str: '''simple docstring''' return self._src_lang @src_lang.setter def lowerCAmelCase ( self , snake_case) -> None: '''simple docstring''' _UpperCAmelCase : List[Any] =new_src_lang self.set_src_lang_special_tokens(self._src_lang) def lowerCAmelCase ( self , snake_case , snake_case = None , snake_case = False) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__a , token_ids_a=__a , already_has_special_tokens=__a) _UpperCAmelCase : Any =[1] * len(self.prefix_tokens) _UpperCAmelCase : Union[str, Any] =[1] * len(self.suffix_tokens) if token_ids_a is None: return prefix_ones + ([0] * len(__a)) + suffix_ones return prefix_ones + ([0] * len(__a)) + ([0] * len(__a)) + suffix_ones def lowerCAmelCase ( self , snake_case , snake_case = None) -> List[int]: '''simple docstring''' if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def lowerCAmelCase ( self , snake_case , snake_case = None) -> List[int]: '''simple docstring''' _UpperCAmelCase : List[str] =[self.sep_token_id] _UpperCAmelCase : Dict =[self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0] def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , **snake_case) -> List[Any]: '''simple docstring''' if src_lang is None or tgt_lang is None: raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model') _UpperCAmelCase : str =src_lang _UpperCAmelCase : str =self(__a , add_special_tokens=__a , return_tensors=__a , **__a) _UpperCAmelCase : Union[str, Any] =self.convert_tokens_to_ids(__a) _UpperCAmelCase : Tuple =tgt_lang_id return inputs def lowerCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCAmelCase : int ={self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def lowerCAmelCase ( self , snake_case) -> List[str]: '''simple docstring''' return self.sp_model.encode(__a , out_type=__a) def lowerCAmelCase ( self , snake_case) -> List[Any]: '''simple docstring''' if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] _UpperCAmelCase : Tuple =self.sp_model.PieceToId(__a) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def lowerCAmelCase ( self , snake_case) -> Union[str, Any]: '''simple docstring''' if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) def lowerCAmelCase ( self , snake_case) -> int: '''simple docstring''' _UpperCAmelCase : Optional[int] =''.join(__a).replace(__a , ' ').strip() return out_string def lowerCAmelCase ( self , snake_case , snake_case = None) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(__a): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return _UpperCAmelCase : Optional[Any] =os.path.join( __a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']) if os.path.abspath(self.vocab_file) != os.path.abspath(__a) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file , __a) elif not os.path.isfile(self.vocab_file): with open(__a , 'wb') as fi: _UpperCAmelCase : Tuple =self.sp_model.serialized_model_proto() fi.write(__a) return (out_vocab_file,) def lowerCAmelCase ( self , snake_case , snake_case = "en_XX" , snake_case = None , snake_case = "ro_RO" , **snake_case , ) -> BatchEncoding: '''simple docstring''' _UpperCAmelCase : Any =src_lang _UpperCAmelCase : int =tgt_lang return super().prepare_seqaseq_batch(__a , __a , **__a) def lowerCAmelCase ( self) -> str: '''simple docstring''' return self.set_src_lang_special_tokens(self.src_lang) def lowerCAmelCase ( self) -> str: '''simple docstring''' return self.set_tgt_lang_special_tokens(self.tgt_lang) def lowerCAmelCase ( self , snake_case) -> None: '''simple docstring''' _UpperCAmelCase : Optional[Any] =self.lang_code_to_id[src_lang] _UpperCAmelCase : Optional[Any] =[] _UpperCAmelCase : Dict =[self.eos_token_id, self.cur_lang_code] def lowerCAmelCase ( self , snake_case) -> None: '''simple docstring''' _UpperCAmelCase : List[str] =self.lang_code_to_id[lang] _UpperCAmelCase : Tuple =[] _UpperCAmelCase : Union[str, Any] =[self.eos_token_id, self.cur_lang_code]
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"""simple docstring""" from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images, ) from ...utils import TensorType, logging _a = logging.get_logger(__name__) class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['pixel_values'] def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None: '''simple docstring''' super().__init__(**__a) _UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a) _UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''') _UpperCamelCase = do_resize _UpperCamelCase = do_rescale _UpperCamelCase = do_normalize _UpperCamelCase = do_center_crop _UpperCamelCase = crop_size _UpperCamelCase = size _UpperCamelCase = resample _UpperCamelCase = rescale_factor _UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN _UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "shortest_edge" in size: _UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a) # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"]) elif "height" in size and "width" in size: _UpperCamelCase = (size['''height'''], size['''width''']) else: raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''') return resize(__a , size=__a , resample=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "height" not in size or "width" not in size: raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''') return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray: '''simple docstring''' return rescale(__a , scale=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' return normalize(__a , mean=__a , std=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature: '''simple docstring''' _UpperCamelCase = do_resize if do_resize is not None else self.do_resize _UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale _UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize _UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop _UpperCamelCase = crop_size if crop_size is not None else self.crop_size _UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a) _UpperCamelCase = resample if resample is not None else self.resample _UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor _UpperCamelCase = image_mean if image_mean is not None else self.image_mean _UpperCamelCase = image_std if image_std is not None else self.image_std _UpperCamelCase = size if size is not None else self.size _UpperCamelCase = get_size_dict(__a) if not is_batched(__a): _UpperCamelCase = [images] if not valid_images(__a): raise ValueError( '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, ''' '''torch.Tensor, tf.Tensor or jax.ndarray.''') if do_resize and size is None: raise ValueError('''Size must be specified if do_resize is True.''') if do_center_crop and crop_size is None: raise ValueError('''Crop size must be specified if do_center_crop is True.''') if do_rescale and rescale_factor is None: raise ValueError('''Rescale factor must be specified if do_rescale is True.''') # All transformations expect numpy arrays. _UpperCamelCase = [to_numpy_array(__a) for image in images] if do_resize: _UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images] if do_center_crop: _UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images] if do_rescale: _UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images] if do_normalize: _UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images] _UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images] _UpperCamelCase = {'''pixel_values''': images} return BatchFeature(data=__a , tensor_type=__a)
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class lowerCamelCase : """simple docstring""" def __init__( self : List[Any], _UpperCAmelCase : List[Any], _UpperCAmelCase : Optional[int] ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = name SCREAMING_SNAKE_CASE__ : Optional[int] = val def __str__( self : List[Any] ) -> Optional[int]: """simple docstring""" return F'''{self.__class__.__name__}({self.name}, {self.val})''' def __lt__( self : List[Any], _UpperCAmelCase : int ) -> Union[str, Any]: """simple docstring""" return self.val < other.val class lowerCamelCase : """simple docstring""" def __init__( self : int, _UpperCAmelCase : str ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = {} SCREAMING_SNAKE_CASE__ : Optional[int] = {} SCREAMING_SNAKE_CASE__ : Tuple = self.build_heap(__a ) def __getitem__( self : List[str], _UpperCAmelCase : Tuple ) -> Union[str, Any]: """simple docstring""" return self.get_value(__a ) def A_ ( self : Union[str, Any], _UpperCAmelCase : Dict ) -> Union[str, Any]: """simple docstring""" return (idx - 1) // 2 def A_ ( self : Optional[Any], _UpperCAmelCase : Optional[Any] ) -> Any: """simple docstring""" return idx * 2 + 1 def A_ ( self : Any, _UpperCAmelCase : str ) -> int: """simple docstring""" return idx * 2 + 2 def A_ ( self : int, _UpperCAmelCase : int ) -> Tuple: """simple docstring""" return self.heap_dict[key] def A_ ( self : Any, _UpperCAmelCase : Tuple ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(__a ) - 1 SCREAMING_SNAKE_CASE__ : str = self.get_parent_idx(__a ) for idx, i in enumerate(__a ): SCREAMING_SNAKE_CASE__ : Optional[Any] = idx SCREAMING_SNAKE_CASE__ : str = i.val for i in range(__a, -1, -1 ): self.sift_down(__a, __a ) return array def A_ ( self : Union[str, Any], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : List[Any] ) -> int: """simple docstring""" while True: SCREAMING_SNAKE_CASE__ : int = self.get_left_child_idx(__a ) # noqa: E741 SCREAMING_SNAKE_CASE__ : Tuple = self.get_right_child_idx(__a ) SCREAMING_SNAKE_CASE__ : Dict = idx if l < len(__a ) and array[l] < array[idx]: SCREAMING_SNAKE_CASE__ : str = l if r < len(__a ) and array[r] < array[smallest]: SCREAMING_SNAKE_CASE__ : int = r if smallest != idx: SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Dict = array[smallest], array[idx] ( ( SCREAMING_SNAKE_CASE__ ) ,( SCREAMING_SNAKE_CASE__ ) , ) : Union[str, Any] = ( self.idx_of_element[array[smallest]], self.idx_of_element[array[idx]], ) SCREAMING_SNAKE_CASE__ : List[str] = smallest else: break def A_ ( self : int, _UpperCAmelCase : Optional[int] ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_parent_idx(__a ) while p >= 0 and self.heap[p] > self.heap[idx]: SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Optional[int] = self.heap[idx], self.heap[p] SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Dict = ( self.idx_of_element[self.heap[idx]], self.idx_of_element[self.heap[p]], ) SCREAMING_SNAKE_CASE__ : Any = p SCREAMING_SNAKE_CASE__ : Tuple = self.get_parent_idx(__a ) def A_ ( self : List[Any] ) -> Union[str, Any]: """simple docstring""" return self.heap[0] def A_ ( self : Optional[Any] ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Optional[Any] = self.heap[-1], self.heap[0] SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Tuple = ( self.idx_of_element[self.heap[-1]], self.idx_of_element[self.heap[0]], ) SCREAMING_SNAKE_CASE__ : List[Any] = self.heap.pop() del self.idx_of_element[x] self.sift_down(0, self.heap ) return x def A_ ( self : Any, _UpperCAmelCase : List[Any] ) -> Tuple: """simple docstring""" self.heap.append(__a ) SCREAMING_SNAKE_CASE__ : int = len(self.heap ) - 1 SCREAMING_SNAKE_CASE__ : Optional[Any] = node.val self.sift_up(len(self.heap ) - 1 ) def A_ ( self : Optional[int] ) -> Optional[int]: """simple docstring""" return len(self.heap ) == 0 def A_ ( self : Dict, _UpperCAmelCase : Optional[Any], _UpperCAmelCase : int ) -> List[Any]: """simple docstring""" assert ( self.heap[self.idx_of_element[node]].val > new_value ), "newValue must be less that current value" SCREAMING_SNAKE_CASE__ : Tuple = new_value SCREAMING_SNAKE_CASE__ : List[Any] = new_value self.sift_up(self.idx_of_element[node] ) _lowerCamelCase : str = Node('''R''', -1) _lowerCamelCase : Optional[int] = Node('''B''', 6) _lowerCamelCase : int = Node('''A''', 3) _lowerCamelCase : str = Node('''X''', 1) _lowerCamelCase : List[Any] = Node('''E''', 4) # Use one of these two ways to generate Min-Heap # Generating Min-Heap from array _lowerCamelCase : Dict = MinHeap([r, b, a, x, e]) # Generating Min-Heap by Insert method # myMinHeap.insert(a) # myMinHeap.insert(b) # myMinHeap.insert(x) # myMinHeap.insert(r) # myMinHeap.insert(e) # Before print('''Min Heap - before decrease key''') for i in my_min_heap.heap: print(i) print('''Min Heap - After decrease key of node [B -> -17]''') my_min_heap.decrease_key(b, -1_7) # After for i in my_min_heap.heap: print(i) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" # Imports import numpy as np class _UpperCAmelCase: def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' if red is not None: _UpperCamelCase = red if green is not None: _UpperCamelCase = green if blue is not None: _UpperCamelCase = blue if red_edge is not None: _UpperCamelCase = red_edge if nir is not None: _UpperCamelCase = nir return True def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) _UpperCamelCase = { '''ARVI2''': self.arvaa, '''CCCI''': self.ccci, '''CVI''': self.cvi, '''GLI''': self.gli, '''NDVI''': self.ndvi, '''BNDVI''': self.bndvi, '''redEdgeNDVI''': self.red_edge_ndvi, '''GNDVI''': self.gndvi, '''GBNDVI''': self.gbndvi, '''GRNDVI''': self.grndvi, '''RBNDVI''': self.rbndvi, '''PNDVI''': self.pndvi, '''ATSAVI''': self.atsavi, '''BWDRVI''': self.bwdrvi, '''CIgreen''': self.ci_green, '''CIrededge''': self.ci_rededge, '''CI''': self.ci, '''CTVI''': self.ctvi, '''GDVI''': self.gdvi, '''EVI''': self.evi, '''GEMI''': self.gemi, '''GOSAVI''': self.gosavi, '''GSAVI''': self.gsavi, '''Hue''': self.hue, '''IVI''': self.ivi, '''IPVI''': self.ipvi, '''I''': self.i, '''RVI''': self.rvi, '''MRVI''': self.mrvi, '''MSAVI''': self.m_savi, '''NormG''': self.norm_g, '''NormNIR''': self.norm_nir, '''NormR''': self.norm_r, '''NGRDI''': self.ngrdi, '''RI''': self.ri, '''S''': self.s, '''IF''': self._if, '''DVI''': self.dvi, '''TVI''': self.tvi, '''NDRE''': self.ndre, } try: return funcs[index]() except KeyError: print('''Index not in the list!''') return False def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red))) def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / ( (self.nir - self.red) / (self.nir + self.red) ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir * (self.red / (self.green**2)) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.green - self.red - self.blue) / ( 2 * self.green + self.red + self.blue ) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - self.red) / (self.nir + self.red) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (self.nir - self.blue) / (self.nir + self.blue) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.redEdge - self.red) / (self.redEdge + self.red) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir - (self.green + self.blue)) / ( self.nir + (self.green + self.blue) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.nir - (self.green + self.red)) / ( self.nir + (self.green + self.red) ) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red)) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - (self.green + self.red + self.blue)) / ( self.nir + (self.green + self.red + self.blue) ) def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]: '''simple docstring''' return a * ( (self.nir - a * self.red - b) / (a * self.nir + self.red - a * b + x * (1 + a**2)) ) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir / self.green) - 1 def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir / self.redEdge) - 1 def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.red - self.blue) / self.red def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.ndvi() return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2)) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir - self.green def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return 2.5 * ( (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / ( self.nir + self.red + 0.5 ) return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red) def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green + y) def UpperCAmelCase ( self , __a=0.5) -> Dict: '''simple docstring''' return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return np.arctan( ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)) def UpperCAmelCase ( self , __a=None , __a=None) -> Any: '''simple docstring''' return (self.nir - b) / (a * self.red) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.red + self.green + self.blue) / 30.5 def UpperCAmelCase ( self) -> Any: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.rvi() - 1) / (self.rvi() + 1) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return ( (2 * self.nir + 1) - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2) ) / 2 def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.green / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> str: '''simple docstring''' return self.nir / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.red / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.green - self.red) / (self.green + self.red) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (self.red - self.green) / (self.red + self.green) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)]) _UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)]) return (max_value - min_value) / max_value def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.red - self.green - self.blue) / (self.green - self.blue) def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Any: '''simple docstring''' return (self.ndvi() + 0.5) ** (1 / 2) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.nir - self.redEdge) / (self.nir + self.redEdge)
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def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> int: return int(input_a == input_a == 0 ) def lowerCamelCase_() -> None: print("Truth Table of NOR Gate:" ) print("| Input 1 | Input 2 | Output |" ) print(F'| 0 | 0 | {nor_gate(0 , 0 )} |' ) print(F'| 0 | 1 | {nor_gate(0 , 1 )} |' ) print(F'| 1 | 0 | {nor_gate(1 , 0 )} |' ) print(F'| 1 | 1 | {nor_gate(1 , 1 )} |' ) if __name__ == "__main__": import doctest doctest.testmod() main()
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"""simple docstring""" import inspect import unittest from transformers import ViTHybridConfig from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = patch_size _UpperCamelCase = num_channels _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = scope _UpperCamelCase = backbone_featmap_shape # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) # the number of patches is based on the feature map of the backbone, which by default uses an output stride # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size _UpperCamelCase = (self.image_size // 32) ** 2 _UpperCamelCase = num_patches + 1 def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = { '''global_padding''': '''same''', '''layer_type''': '''bottleneck''', '''depths''': [3, 4, 9], '''out_features''': ['''stage1''', '''stage2''', '''stage3'''], '''embedding_dynamic_padding''': True, '''hidden_sizes''': [4, 8, 16, 32], '''num_groups''': 2, } return ViTHybridConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModel(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.type_sequence_label_size _UpperCamelCase = ViTHybridForImageClassification(__a) model.to(__a) model.eval() _UpperCamelCase = model(__a , labels=__a) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else () lowercase__ = ( {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification} if is_torch_available() else {} ) lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Any: '''simple docstring''' self.config_tester.run_common_tests() @unittest.skip(reason='''ViT does not use inputs_embeds''') def UpperCAmelCase ( self) -> Dict: '''simple docstring''' pass def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) self.assertIsInstance(model.get_input_embeddings() , (nn.Module)) _UpperCamelCase = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__a , nn.Linear)) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "ViTHybridPatchEmbeddings": _UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if name in backbone_params: continue self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = ViTHybridModel.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class _UpperCAmelCase( unittest.TestCase ): @cached_property def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return ( ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to( __a) _UpperCamelCase = self.default_image_processor _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a) # forward pass with torch.no_grad(): _UpperCamelCase = model(**__a) # verify the logits _UpperCamelCase = torch.Size((1, 10_00)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4)) @slow @require_accelerate def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''') _UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''') _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''') _UpperCamelCase = model(**__a) _UpperCamelCase = outputs.logits # model predicts one of the 1000 ImageNet classes _UpperCamelCase = logits.argmax(-1).item() self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
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'''simple docstring''' import logging import os import sys import warnings from dataclasses import dataclass, field from random import randint from typing import Optional import datasets import evaluate import numpy as np from datasets import DatasetDict, load_dataset import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForAudioClassification, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version __a: Optional[Any] = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("""4.31.0""") require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""") def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1_6000 ): lowercase__ : Any = int(round(sample_rate * max_length ) ) if len(__snake_case ) <= sample_length: return wav lowercase__ : List[Any] = randint(0 , len(__snake_case ) - sample_length - 1 ) return wav[random_offset : random_offset + sample_length] @dataclass class UpperCAmelCase : '''simple docstring''' SCREAMING_SNAKE_CASE = field(default=a__ , metadata={"help": "Name of a dataset from the datasets package"} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "A file containing the training audio paths and labels."} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "A file containing the validation audio paths and labels."} ) SCREAMING_SNAKE_CASE = field( default="train" , metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to \'train\'" } , ) SCREAMING_SNAKE_CASE = field( default="validation" , metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to \'validation\'" ) } , ) SCREAMING_SNAKE_CASE = field( default="audio" , metadata={"help": "The name of the dataset column containing the audio data. Defaults to \'audio\'"} , ) SCREAMING_SNAKE_CASE = field( default="label" , metadata={"help": "The name of the dataset column containing the labels. Defaults to \'label\'"} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) } , ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) } , ) SCREAMING_SNAKE_CASE = field( default=2_0 , metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."} , ) @dataclass class UpperCAmelCase : '''simple docstring''' SCREAMING_SNAKE_CASE = field( default="facebook/wav2vec2-base" , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} , ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "Pretrained config name or path if not the same as model_name"} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} ) SCREAMING_SNAKE_CASE = field( default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "Name or path of preprocessor config."} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "Whether to generate an attention mask in the feature extractor."} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) } , ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "Whether to freeze the feature extractor layers of the model."} ) SCREAMING_SNAKE_CASE = field( default=a__ , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , ) def _lowerCAmelCase( self ) -> Union[str, Any]: if not self.freeze_feature_extractor and self.freeze_feature_encoder: warnings.warn( '''The argument `--freeze_feature_extractor` is deprecated and ''' '''will be removed in a future version. Use `--freeze_feature_encoder`''' '''instead. Setting `freeze_feature_encoder==True`.''' , __a , ) if self.freeze_feature_extractor and not self.freeze_feature_encoder: raise ValueError( '''The argument `--freeze_feature_extractor` is deprecated and ''' '''should not be used in combination with `--freeze_feature_encoder`.''' '''Only make use of `--freeze_feature_encoder`.''' ) def __UpperCamelCase ( ): lowercase__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. lowercase__ , lowercase__ , lowercase__ : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: lowercase__ , lowercase__ , lowercase__ : List[Any] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry('''run_audio_classification''' , __snake_case , __snake_case ) # Setup logging logging.basicConfig( format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() lowercase__ : List[Any] = training_args.get_process_log_level() logger.setLevel(__snake_case ) transformers.utils.logging.set_verbosity(__snake_case ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """ + F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" ) logger.info(F"""Training/evaluation parameters {training_args}""" ) # Set seed before initializing model. set_seed(training_args.seed ) # Detecting last checkpoint. lowercase__ : Optional[int] = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: lowercase__ : Optional[int] = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F"""Output directory ({training_args.output_dir}) already exists and is not empty. """ '''Use --overwrite_output_dir to train from scratch.''' ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """ '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' ) # Initialize our dataset and prepare it for the audio classification task. lowercase__ : Optional[Any] = DatasetDict() lowercase__ : Optional[int] = load_dataset( data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , ) lowercase__ : Any = load_dataset( data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , ) if data_args.audio_column_name not in raw_datasets["train"].column_names: raise ValueError( F"""--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. """ '''Make sure to set `--audio_column_name` to the correct audio column - one of ''' F"""{', '.join(raw_datasets['train'].column_names )}.""" ) if data_args.label_column_name not in raw_datasets["train"].column_names: raise ValueError( F"""--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. """ '''Make sure to set `--label_column_name` to the correct text column - one of ''' F"""{', '.join(raw_datasets['train'].column_names )}.""" ) # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over # transformer outputs in the classifier, but it doesn't always lead to better accuracy lowercase__ : List[Any] = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) # `datasets` takes care of automatically loading and resampling the audio, # so we just need to set the correct target sampling rate. lowercase__ : str = raw_datasets.cast_column( data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) ) lowercase__ : Tuple = feature_extractor.model_input_names[0] def train_transforms(UpperCAmelCase ): lowercase__ : Optional[int] = [] for audio in batch[data_args.audio_column_name]: lowercase__ : str = random_subsample( audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate ) subsampled_wavs.append(__snake_case ) lowercase__ : Tuple = feature_extractor(__snake_case , sampling_rate=feature_extractor.sampling_rate ) lowercase__ : Dict = {model_input_name: inputs.get(__snake_case )} lowercase__ : List[Any] = list(batch[data_args.label_column_name] ) return output_batch def val_transforms(UpperCAmelCase ): lowercase__ : Optional[int] = [audio['''array'''] for audio in batch[data_args.audio_column_name]] lowercase__ : Tuple = feature_extractor(__snake_case , sampling_rate=feature_extractor.sampling_rate ) lowercase__ : Optional[int] = {model_input_name: inputs.get(__snake_case )} lowercase__ : Optional[int] = list(batch[data_args.label_column_name] ) return output_batch # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. lowercase__ : str = raw_datasets['''train'''].features[data_args.label_column_name].names lowercase__ , lowercase__ : Any = {}, {} for i, label in enumerate(__snake_case ): lowercase__ : int = str(__snake_case ) lowercase__ : List[str] = label # Load the accuracy metric from the datasets package lowercase__ : Any = evaluate.load('''accuracy''' ) # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with # `predictions` and `label_ids` fields) and has to return a dictionary string to float. def compute_metrics(UpperCAmelCase ): lowercase__ : List[str] = np.argmax(eval_pred.predictions , axis=1 ) return metric.compute(predictions=__snake_case , references=eval_pred.label_ids ) lowercase__ : List[Any] = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path , num_labels=len(__snake_case ) , labelaid=__snake_case , idalabel=__snake_case , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) lowercase__ : Optional[int] = AutoModelForAudioClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # freeze the convolutional waveform encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() if training_args.do_train: if data_args.max_train_samples is not None: lowercase__ : Any = ( raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) ) ) # Set the training transforms raw_datasets["train"].set_transform(__snake_case , output_all_columns=__snake_case ) if training_args.do_eval: if data_args.max_eval_samples is not None: lowercase__ : Dict = ( raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) ) ) # Set the validation transforms raw_datasets["eval"].set_transform(__snake_case , output_all_columns=__snake_case ) # Initialize our trainer lowercase__ : str = Trainer( model=__snake_case , args=__snake_case , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=__snake_case , tokenizer=__snake_case , ) # Training if training_args.do_train: lowercase__ : int = None if training_args.resume_from_checkpoint is not None: lowercase__ : Any = training_args.resume_from_checkpoint elif last_checkpoint is not None: lowercase__ : Optional[Any] = last_checkpoint lowercase__ : int = trainer.train(resume_from_checkpoint=__snake_case ) trainer.save_model() trainer.log_metrics('''train''' , train_result.metrics ) trainer.save_metrics('''train''' , train_result.metrics ) trainer.save_state() # Evaluation if training_args.do_eval: lowercase__ : Dict = trainer.evaluate() trainer.log_metrics('''eval''' , __snake_case ) trainer.save_metrics('''eval''' , __snake_case ) # Write model card and (optionally) push to hub lowercase__ : Optional[int] = { '''finetuned_from''': model_args.model_name_or_path, '''tasks''': '''audio-classification''', '''dataset''': data_args.dataset_name, '''tags''': ['''audio-classification'''], } if training_args.push_to_hub: trainer.push_to_hub(**__snake_case ) else: trainer.create_model_card(**__snake_case ) if __name__ == "__main__": main()
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"""simple docstring""" from math import acos, sin from typing import List, Tuple, Union import numpy as np import torch from PIL import Image from ...models import AutoencoderKL, UNetaDConditionModel from ...schedulers import DDIMScheduler, DDPMScheduler from ...utils import randn_tensor from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput from .mel import Mel class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['vqvae'] def __init__( self , __a , __a , __a , __a , ) -> List[str]: '''simple docstring''' super().__init__() self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' return 50 if isinstance(self.scheduler , __a) else 10_00 @torch.no_grad() def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[ Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]], ]: '''simple docstring''' _UpperCamelCase = steps or self.get_default_steps() self.scheduler.set_timesteps(__a) _UpperCamelCase = step_generator or generator # For backwards compatibility if type(self.unet.config.sample_size) == int: _UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size) if noise is None: _UpperCamelCase = randn_tensor( ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1], ) , generator=__a , device=self.device , ) _UpperCamelCase = noise _UpperCamelCase = None if audio_file is not None or raw_audio is not None: self.mel.load_audio(__a , __a) _UpperCamelCase = self.mel.audio_slice_to_image(__a) _UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape( (input_image.height, input_image.width)) _UpperCamelCase = (input_image / 2_55) * 2 - 1 _UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device) if self.vqvae is not None: _UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample( generator=__a)[0] _UpperCamelCase = self.vqvae.config.scaling_factor * input_images if start_step > 0: _UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1]) _UpperCamelCase = ( self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length ) _UpperCamelCase = int(mask_start_secs * pixels_per_second) _UpperCamelCase = int(mask_end_secs * pixels_per_second) _UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:])) for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])): if isinstance(self.unet , __a): _UpperCamelCase = self.unet(__a , __a , __a)['''sample'''] else: _UpperCamelCase = self.unet(__a , __a)['''sample'''] if isinstance(self.scheduler , __a): _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample'''] else: _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample'''] if mask is not None: if mask_start > 0: _UpperCamelCase = mask[:, step, :, :mask_start] if mask_end > 0: _UpperCamelCase = mask[:, step, :, -mask_end:] if self.vqvae is not None: # 0.18215 was scaling factor used in training to ensure unit variance _UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images _UpperCamelCase = self.vqvae.decode(__a)['''sample'''] _UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1) _UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy() _UpperCamelCase = (images * 2_55).round().astype('''uint8''') _UpperCamelCase = list( (Image.fromarray(_[:, :, 0]) for _ in images) if images.shape[3] == 1 else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images)) _UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images] if not return_dict: return images, (self.mel.get_sample_rate(), audios) return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a)) @torch.no_grad() def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray: '''simple docstring''' assert isinstance(self.scheduler , __a) self.scheduler.set_timesteps(__a) _UpperCamelCase = np.array( [np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images]) _UpperCamelCase = (sample / 2_55) * 2 - 1 _UpperCamelCase = torch.Tensor(__a).to(self.device) for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))): _UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps _UpperCamelCase = self.scheduler.alphas_cumprod[t] _UpperCamelCase = ( self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod ) _UpperCamelCase = 1 - alpha_prod_t _UpperCamelCase = self.unet(__a , __a)['''sample'''] _UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output _UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5) _UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output return sample @staticmethod def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor: '''simple docstring''' _UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a)) return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
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0
import inspect import tempfile import unittest from huggingface_hub import hf_hub_download from transformers import is_torch_available from transformers.testing_utils import is_flaky, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin _snake_case = 1E-4 if is_torch_available(): import torch from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder @require_torch class UpperCAmelCase_ : '''simple docstring''' def __init__( self , __A , __A=16 , __A=13 , __A=7 , __A=14 , __A=10 , __A=19 , __A=5 , __A=4 , __A=True , __A=16 , __A=2 , __A=4 , __A=4 , __A="gelu" , __A=0.1 , __A=0.1 , __A=[1, 2, 3, 4, 5] , __A=25 , __A=5 , ): """simple docstring""" lowerCamelCase : str = d_model lowerCamelCase : Any = parent lowerCamelCase : Any = batch_size lowerCamelCase : Union[str, Any] = prediction_length lowerCamelCase : Any = context_length lowerCamelCase : Tuple = cardinality lowerCamelCase : int = num_time_features lowerCamelCase : Optional[int] = lags_sequence lowerCamelCase : List[str] = embedding_dimension lowerCamelCase : Optional[Any] = is_training lowerCamelCase : str = hidden_size lowerCamelCase : Tuple = num_hidden_layers lowerCamelCase : List[Any] = num_attention_heads lowerCamelCase : Optional[int] = intermediate_size lowerCamelCase : Any = hidden_act lowerCamelCase : str = hidden_dropout_prob lowerCamelCase : Optional[int] = attention_probs_dropout_prob lowerCamelCase : int = context_length lowerCamelCase : Union[str, Any] = prediction_length + label_length lowerCamelCase : Optional[int] = label_length lowerCamelCase : Optional[Any] = moving_average lowerCamelCase : List[Any] = autocorrelation_factor def _snake_case ( self ): """simple docstring""" return AutoformerConfig( d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , ) def _snake_case ( self , __A ): """simple docstring""" lowerCamelCase : Optional[int] = config.context_length + max(config.lags_sequence ) lowerCamelCase : Tuple = ids_tensor([self.batch_size, 1] , config.cardinality[0] ) lowerCamelCase : Dict = floats_tensor([self.batch_size, _past_length, config.num_time_features] ) lowerCamelCase : Union[str, Any] = floats_tensor([self.batch_size, _past_length] ) lowerCamelCase : Optional[int] = floats_tensor([self.batch_size, _past_length] ) > 0.5 # decoder inputs lowerCamelCase : int = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] ) lowerCamelCase : str = floats_tensor([self.batch_size, config.prediction_length] ) lowerCamelCase : Tuple = { "past_values": past_values, "static_categorical_features": static_categorical_features, "past_time_features": past_time_features, "past_observed_mask": past_observed_mask, "future_time_features": future_time_features, "future_values": future_values, } return inputs_dict def _snake_case ( self ): """simple docstring""" lowerCamelCase : Optional[int] = self.get_config() lowerCamelCase : List[str] = self.prepare_autoformer_inputs_dict(__a ) return config, inputs_dict def _snake_case ( self ): """simple docstring""" lowerCamelCase , lowerCamelCase : Optional[int] = self.prepare_config_and_inputs() return config, inputs_dict def _snake_case ( self , __A , __A ): """simple docstring""" lowerCamelCase : Any = AutoformerModel(config=__a ).to(__a ).eval() lowerCamelCase : Optional[int] = model(**__a ) lowerCamelCase : int = outputs.encoder_last_hidden_state lowerCamelCase : List[Any] = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: lowerCamelCase : List[str] = model.get_encoder() encoder.save_pretrained(__a ) lowerCamelCase : int = AutoformerEncoder.from_pretrained(__a ).to(__a ) lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : List[str] = model.create_network_inputs(**__a ) lowerCamelCase , lowerCamelCase : Optional[Any] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] ) lowerCamelCase : Any = torch.cat( (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , ) lowerCamelCase : Optional[Any] = encoder(inputs_embeds=__a )[0] self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 ) lowerCamelCase : Tuple = ( torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 ) .unsqueeze(1 ) .repeat(1 , config.prediction_length , 1 ) ) lowerCamelCase : Optional[int] = torch.zeros( [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , ) lowerCamelCase : Dict = torch.cat( ( torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ), feature[:, config.context_length - config.label_length :, ...], ) , dim=-1 , ) lowerCamelCase : List[str] = torch.cat( ( torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ), feature[:, config.context_length - config.label_length :, ...], ) , dim=-1 , ) with tempfile.TemporaryDirectory() as tmpdirname: lowerCamelCase : Dict = model.get_decoder() decoder.save_pretrained(__a ) lowerCamelCase : Union[str, Any] = AutoformerDecoder.from_pretrained(__a ).to(__a ) lowerCamelCase : Optional[int] = decoder( trend=__a , inputs_embeds=__a , encoder_hidden_states=__a , )[0] self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 ) @require_torch class UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase , unittest.TestCase ): '''simple docstring''' __A : Tuple = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else () __A : Any = (AutoformerForPrediction,) if is_torch_available() else () __A : int = {"feature-extraction": AutoformerModel} if is_torch_available() else {} __A : int = False __A : Optional[int] = False __A : Tuple = False __A : str = False __A : str = False __A : Tuple = False def _snake_case ( self ): """simple docstring""" lowerCamelCase : str = AutoformerModelTester(self ) lowerCamelCase : int = ConfigTester(self , config_class=__a , has_text_modality=__a ) def _snake_case ( self ): """simple docstring""" self.config_tester.run_common_tests() def _snake_case ( self ): """simple docstring""" lowerCamelCase , lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: lowerCamelCase : str = model_class(__a ) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(__a ) lowerCamelCase , lowerCamelCase : Tuple = model_class.from_pretrained(__a , output_loading_info=__a ) self.assertEqual(info["missing_keys"] , [] ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*__a ) @unittest.skip(reason="Model has no tokens embeddings" ) def _snake_case ( self ): """simple docstring""" pass def _snake_case ( self ): """simple docstring""" lowerCamelCase : Optional[Any] = inspect.signature(getattr(__a , "forward" ) ) # The main input is the name of the argument after `self` lowerCamelCase : str = list(model_signature.parameters.keys() )[1] self.assertEqual(AutoformerModel.main_input_name , __a ) def _snake_case ( self ): """simple docstring""" lowerCamelCase , lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: lowerCamelCase : Tuple = model_class(__a ) lowerCamelCase : Tuple = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic lowerCamelCase : Dict = [*signature.parameters.keys()] lowerCamelCase : Dict = [ "past_values", "past_time_features", "past_observed_mask", "static_categorical_features", "static_real_features", "future_values", "future_time_features", ] if model.__class__.__name__ in ["AutoformerForPrediction"]: expected_arg_names.append("future_observed_mask" ) expected_arg_names.extend( [ "decoder_attention_mask", "head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs", "past_key_values", "output_hidden_states", "output_attentions", "use_cache", "return_dict", ] ) self.assertListEqual(arg_names[: len(__a )] , __a ) def _snake_case ( self ): """simple docstring""" lowerCamelCase , lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common() lowerCamelCase : Union[str, Any] = True lowerCamelCase : str = getattr(self.model_tester , "seq_length" , __a ) lowerCamelCase : str = getattr(self.model_tester , "decoder_seq_length" , __a ) lowerCamelCase : List[Any] = getattr(self.model_tester , "encoder_seq_length" , __a ) lowerCamelCase : Any = getattr(self.model_tester , "d_model" , __a ) lowerCamelCase : Any = getattr(self.model_tester , "num_attention_heads" , __a ) lowerCamelCase : Any = d_model // num_attention_heads for model_class in self.all_model_classes: lowerCamelCase : List[str] = True lowerCamelCase : int = False lowerCamelCase : Union[str, Any] = True lowerCamelCase : List[str] = model_class(__a ) model.to(__a ) model.eval() with torch.no_grad(): lowerCamelCase : str = model(**self._prepare_for_class(__a , __a ) ) lowerCamelCase : List[str] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) # check that output_attentions also work using config del inputs_dict["output_attentions"] lowerCamelCase : int = True lowerCamelCase : Tuple = model_class(__a ) model.to(__a ) model.eval() with torch.no_grad(): lowerCamelCase : Any = model(**self._prepare_for_class(__a , __a ) ) lowerCamelCase : Union[str, Any] = outputs.encoder_attentions self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , ) lowerCamelCase : Optional[Any] = len(__a ) lowerCamelCase : List[str] = 7 if "last_hidden_state" in outputs: correct_outlen += 1 if "trend" in outputs: correct_outlen += 1 if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned if "loss" in outputs: correct_outlen += 1 if "params" in outputs: correct_outlen += 1 self.assertEqual(__a , __a ) # decoder attentions lowerCamelCase : Union[str, Any] = outputs.decoder_attentions self.assertIsInstance(__a , (list, tuple) ) self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , ) # cross attentions lowerCamelCase : str = outputs.cross_attentions self.assertIsInstance(__a , (list, tuple) ) self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , ) # Check attention is always last and order is fine lowerCamelCase : str = True lowerCamelCase : Any = True lowerCamelCase : Optional[int] = model_class(__a ) model.to(__a ) model.eval() with torch.no_grad(): lowerCamelCase : Any = model(**self._prepare_for_class(__a , __a ) ) self.assertEqual(out_len + 2 , len(__a ) ) lowerCamelCase : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , ) @is_flaky() def _snake_case ( self ): """simple docstring""" super().test_retain_grad_hidden_states_attentions() def lowercase_( SCREAMING_SNAKE_CASE_="train-batch.pt" ): '''simple docstring''' lowerCamelCase : Any = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=__snake_case , repo_type="dataset" ) lowerCamelCase : str = torch.load(__snake_case , map_location=__snake_case ) return batch @require_torch @slow class UpperCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def _snake_case ( self ): """simple docstring""" lowerCamelCase : Any = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a ) lowerCamelCase : Optional[int] = prepare_batch() with torch.no_grad(): lowerCamelCase : Union[str, Any] = model( past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0] lowerCamelCase : int = torch.Size( (64, model.config.prediction_length + model.config.label_length, model.config.feature_size) ) self.assertEqual(output.shape , __a ) lowerCamelCase : List[str] = torch.tensor( [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=__a ) self.assertTrue(torch.allclose(output[0, :3, :3] , __a , atol=__a ) ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Optional[int] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a ) lowerCamelCase : List[str] = prepare_batch("val-batch.pt" ) with torch.no_grad(): lowerCamelCase : Any = model( past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state lowerCamelCase : Dict = torch.Size((64, model.config.context_length, model.config.d_model) ) self.assertEqual(output.shape , __a ) lowerCamelCase : Tuple = torch.tensor( [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=__a ) self.assertTrue(torch.allclose(output[0, :3, :3] , __a , atol=__a ) ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Dict = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a ) lowerCamelCase : str = prepare_batch("val-batch.pt" ) with torch.no_grad(): lowerCamelCase : Any = model.generate( static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , ) lowerCamelCase : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) ) self.assertEqual(outputs.sequences.shape , __a ) lowerCamelCase : int = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=__a ) lowerCamelCase : Optional[int] = outputs.sequences.mean(dim=1 ) self.assertTrue(torch.allclose(mean_prediction[0, -3:] , __a , rtol=1e-1 ) )
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"""simple docstring""" import copy from collections import OrderedDict from typing import Dict, Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING _a = logging.get_logger(__name__) _a = { """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""", # See all DETR models at https://huggingface.co/models?filter=detr } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'detr' lowercase__ = ['past_key_values'] lowercase__ = { 'hidden_size': 'd_model', 'num_attention_heads': 'encoder_attention_heads', } def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int: '''simple docstring''' if backbone_config is not None and use_timm_backbone: raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''') if not use_timm_backbone: if backbone_config is None: logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''') _UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']) elif isinstance(__a , __a): _UpperCamelCase = backbone_config.get('''model_type''') _UpperCamelCase = CONFIG_MAPPING[backbone_model_type] _UpperCamelCase = config_class.from_dict(__a) # set timm attributes to None _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None _UpperCamelCase = use_timm_backbone _UpperCamelCase = backbone_config _UpperCamelCase = num_channels _UpperCamelCase = num_queries _UpperCamelCase = d_model _UpperCamelCase = encoder_ffn_dim _UpperCamelCase = encoder_layers _UpperCamelCase = encoder_attention_heads _UpperCamelCase = decoder_ffn_dim _UpperCamelCase = decoder_layers _UpperCamelCase = decoder_attention_heads _UpperCamelCase = dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = activation_function _UpperCamelCase = init_std _UpperCamelCase = init_xavier_std _UpperCamelCase = encoder_layerdrop _UpperCamelCase = decoder_layerdrop _UpperCamelCase = encoder_layers _UpperCamelCase = auxiliary_loss _UpperCamelCase = position_embedding_type _UpperCamelCase = backbone _UpperCamelCase = use_pretrained_backbone _UpperCamelCase = dilation # Hungarian matcher _UpperCamelCase = class_cost _UpperCamelCase = bbox_cost _UpperCamelCase = giou_cost # Loss coefficients _UpperCamelCase = mask_loss_coefficient _UpperCamelCase = dice_loss_coefficient _UpperCamelCase = bbox_loss_coefficient _UpperCamelCase = giou_loss_coefficient _UpperCamelCase = eos_coefficient super().__init__(is_encoder_decoder=__a , **__a) @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.encoder_attention_heads @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.d_model @classmethod def UpperCAmelCase ( cls , __a , **__a) -> int: '''simple docstring''' return cls(backbone_config=__a , **__a) def UpperCAmelCase ( self) -> Dict[str, any]: '''simple docstring''' _UpperCamelCase = copy.deepcopy(self.__dict__) if output["backbone_config"] is not None: _UpperCamelCase = self.backbone_config.to_dict() _UpperCamelCase = self.__class__.model_type return output class _UpperCAmelCase( lowerCamelCase ): lowercase__ = version.parse('1.11' ) @property def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' return OrderedDict( [ ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}), ('''pixel_mask''', {0: '''batch'''}), ]) @property def UpperCAmelCase ( self) -> float: '''simple docstring''' return 1e-5 @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return 12
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import importlib import sys from argparse import REMAINDER, ArgumentParser from pathlib import Path import torch_xla.distributed.xla_multiprocessing as xmp def lowerCAmelCase__ ( ): '''simple docstring''' lowerCAmelCase__ : Optional[Any] = ArgumentParser( description=( '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes''' )) # Optional arguments for the launch helper parser.add_argument('''--num_cores''' ,type=__snake_case ,default=1 ,help='''Number of TPU cores to use (1 or 8).''') # positional parser.add_argument( '''training_script''' ,type=__snake_case ,help=( '''The full path to the single TPU training ''' '''program/script to be launched in parallel, ''' '''followed by all the arguments for the ''' '''training script''' ) ,) # rest from the training program parser.add_argument('''training_script_args''' ,nargs=__snake_case) return parser.parse_args() def lowerCAmelCase__ ( ): '''simple docstring''' lowerCAmelCase__ : Union[str, Any] = parse_args() # Import training_script as a module. lowerCAmelCase__ : Optional[int] = Path(args.training_script) sys.path.append(str(script_fpath.parent.resolve())) lowerCAmelCase__ : List[str] = script_fpath.stem lowerCAmelCase__ : List[Any] = importlib.import_module(__snake_case) # Patch sys.argv lowerCAmelCase__ : str = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores)] xmp.spawn(mod._mp_fn ,args=() ,nprocs=args.num_cores) if __name__ == "__main__": main()
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"""simple docstring""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging _a = logging.get_logger(__name__) _a = { """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""", # See all WavLM models at https://huggingface.co/models?filter=wavlm } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'wavlm' def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]: '''simple docstring''' super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a) _UpperCamelCase = hidden_size _UpperCamelCase = feat_extract_norm _UpperCamelCase = feat_extract_activation _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = conv_bias _UpperCamelCase = num_buckets _UpperCamelCase = max_bucket_distance _UpperCamelCase = num_conv_pos_embeddings _UpperCamelCase = num_conv_pos_embedding_groups _UpperCamelCase = len(self.conv_dim) _UpperCamelCase = num_hidden_layers _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = num_attention_heads _UpperCamelCase = hidden_dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = feat_proj_dropout _UpperCamelCase = final_dropout _UpperCamelCase = layerdrop _UpperCamelCase = layer_norm_eps _UpperCamelCase = initializer_range _UpperCamelCase = num_ctc_classes _UpperCamelCase = vocab_size _UpperCamelCase = do_stable_layer_norm _UpperCamelCase = use_weighted_layer_sum _UpperCamelCase = classifier_proj_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==''' ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =''' F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,''' F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''') # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _UpperCamelCase = apply_spec_augment _UpperCamelCase = mask_time_prob _UpperCamelCase = mask_time_length _UpperCamelCase = mask_time_min_masks _UpperCamelCase = mask_feature_prob _UpperCamelCase = mask_feature_length # parameters for pretraining with codevector quantized representations _UpperCamelCase = num_codevectors_per_group _UpperCamelCase = num_codevector_groups _UpperCamelCase = contrastive_logits_temperature _UpperCamelCase = num_negatives _UpperCamelCase = codevector_dim _UpperCamelCase = proj_codevector_dim _UpperCamelCase = diversity_loss_weight # ctc loss _UpperCamelCase = ctc_loss_reduction _UpperCamelCase = ctc_zero_infinity # adapter _UpperCamelCase = add_adapter _UpperCamelCase = adapter_kernel_size _UpperCamelCase = adapter_stride _UpperCamelCase = num_adapter_layers _UpperCamelCase = output_hidden_size or hidden_size # SequenceClassification-specific parameter. Feel free to ignore for other classes. _UpperCamelCase = classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = xvector_output_dim @property def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return functools.reduce(operator.mul , self.conv_stride , 1)
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'''simple docstring''' import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging __lowerCAmelCase = logging.get_logger(__name__) __lowerCAmelCase = { """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""", # See all WavLM models at https://huggingface.co/models?filter=wavlm } class UpperCAmelCase__ ( lowercase__ ): """simple docstring""" __UpperCAmelCase : str = '''wavlm''' def __init__( self : Dict ,_a : str=32 ,_a : List[str]=768 ,_a : Optional[Any]=12 ,_a : int=12 ,_a : str=3072 ,_a : List[Any]="gelu" ,_a : Any=0.1 ,_a : Optional[int]=0.1 ,_a : Optional[Any]=0.1 ,_a : List[Any]=0.0 ,_a : Dict=0.1 ,_a : Any=0.1 ,_a : Optional[Any]=0.02 ,_a : Union[str, Any]=1E-5 ,_a : str="group" ,_a : Optional[int]="gelu" ,_a : List[Any]=(512, 512, 512, 512, 512, 512, 512) ,_a : Tuple=(5, 2, 2, 2, 2, 2, 2) ,_a : List[str]=(10, 3, 3, 3, 3, 2, 2) ,_a : Tuple=False ,_a : Tuple=128 ,_a : List[str]=16 ,_a : Optional[int]=320 ,_a : Optional[int]=800 ,_a : int=False ,_a : Optional[int]=True ,_a : Optional[int]=0.05 ,_a : Optional[int]=10 ,_a : Any=2 ,_a : Tuple=0.0 ,_a : str=10 ,_a : str=320 ,_a : Tuple=2 ,_a : List[str]=0.1 ,_a : List[Any]=100 ,_a : Dict=256 ,_a : Union[str, Any]=256 ,_a : List[str]=0.1 ,_a : List[str]="mean" ,_a : Optional[int]=False ,_a : str=False ,_a : Any=256 ,_a : List[Any]=(512, 512, 512, 512, 1500) ,_a : List[Any]=(5, 3, 3, 1, 1) ,_a : Union[str, Any]=(1, 2, 3, 1, 1) ,_a : List[str]=512 ,_a : List[Any]=80 ,_a : Tuple=0 ,_a : Optional[int]=1 ,_a : List[Any]=2 ,_a : Union[str, Any]=False ,_a : str=3 ,_a : Tuple=2 ,_a : int=3 ,_a : Union[str, Any]=None ,**_a : Optional[int] ,): '''simple docstring''' super().__init__(**__a ,pad_token_id=__a ,bos_token_id=__a ,eos_token_id=__a ) _a : int = hidden_size _a : List[Any] = feat_extract_norm _a : Optional[Any] = feat_extract_activation _a : Optional[Any] = list(__a ) _a : List[str] = list(__a ) _a : Optional[int] = list(__a ) _a : str = conv_bias _a : int = num_buckets _a : Union[str, Any] = max_bucket_distance _a : Union[str, Any] = num_conv_pos_embeddings _a : Optional[Any] = num_conv_pos_embedding_groups _a : int = len(self.conv_dim ) _a : str = num_hidden_layers _a : List[str] = intermediate_size _a : Tuple = hidden_act _a : str = num_attention_heads _a : Tuple = hidden_dropout _a : int = attention_dropout _a : Optional[int] = activation_dropout _a : int = feat_proj_dropout _a : Tuple = final_dropout _a : Optional[int] = layerdrop _a : Union[str, Any] = layer_norm_eps _a : Optional[int] = initializer_range _a : Union[str, Any] = num_ctc_classes _a : Tuple = vocab_size _a : Dict = do_stable_layer_norm _a : int = use_weighted_layer_sum _a : List[Any] = classifier_proj_size if ( (len(self.conv_stride ) != self.num_feat_extract_layers) or (len(self.conv_kernel ) != self.num_feat_extract_layers) or (len(self.conv_dim ) != self.num_feat_extract_layers) ): raise ValueError( 'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==' ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =' F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,""" F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _a : List[Any] = apply_spec_augment _a : Tuple = mask_time_prob _a : Any = mask_time_length _a : Optional[Any] = mask_time_min_masks _a : Tuple = mask_feature_prob _a : int = mask_feature_length # parameters for pretraining with codevector quantized representations _a : Optional[Any] = num_codevectors_per_group _a : Optional[Any] = num_codevector_groups _a : str = contrastive_logits_temperature _a : List[Any] = num_negatives _a : Union[str, Any] = codevector_dim _a : Any = proj_codevector_dim _a : int = diversity_loss_weight # ctc loss _a : Optional[int] = ctc_loss_reduction _a : Tuple = ctc_zero_infinity # adapter _a : int = add_adapter _a : List[str] = adapter_kernel_size _a : Dict = adapter_stride _a : Optional[Any] = num_adapter_layers _a : Union[str, Any] = output_hidden_size or hidden_size # SequenceClassification-specific parameter. Feel free to ignore for other classes. _a : Optional[int] = classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. _a : Union[str, Any] = list(__a ) _a : int = list(__a ) _a : List[Any] = list(__a ) _a : Any = xvector_output_dim @property def __lowercase ( self : Tuple ): '''simple docstring''' return functools.reduce(operator.mul ,self.conv_stride ,1 )
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"""simple docstring""" import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer _a = """bart""" _a = True @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Dict: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' ) _UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' ) _UpperCamelCase = qar_model.eval() else: _UpperCamelCase , _UpperCamelCase = (None, None) if MODEL_TYPE == "bart": _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' ) _UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' ) _UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' ) sas_model.load_state_dict(save_dict['''model'''] ) _UpperCamelCase = sas_model.eval() else: _UpperCamelCase , _UpperCamelCase = make_qa_sas_model( model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' ) return (qar_tokenizer, qar_model, sas_tokenizer, sas_model) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = faiss.StandardGpuResources() _UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train'''] _UpperCamelCase = np.memmap( '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) _UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case ) wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU else: _UpperCamelCase , _UpperCamelCase = (None, None) _UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] ) return (wikiaab_passages, wikiaab_gpu_index_flat, es_client) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' ) _UpperCamelCase = elia['''train_eli5'''] _UpperCamelCase = np.memmap( '''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) eli5_train_q_index.add(__snake_case ) return (elia_train, eli5_train_q_index) _a , _a , _a = load_indexes() _a , _a , _a , _a = load_models() _a , _a = load_train_data() def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]: """simple docstring""" _UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case ) _UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case ) _UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]] return nn_examples def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]: """simple docstring""" if source == "none": _UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), []) else: if method == "dense": _UpperCamelCase , _UpperCamelCase = query_qa_dense_index( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) else: _UpperCamelCase , _UpperCamelCase = query_es_index( __snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, ) _UpperCamelCase = [ (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst ] _UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case ) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda __snake_case : None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None), } ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict: """simple docstring""" with torch.no_grad(): _UpperCamelCase = qa_sas_generate( __snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0] return (answer, support_list) st.title("""Long Form Question Answering with ELI5""") # Start sidebar _a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>""" _a = """ <html> <head> <style> .img-container { padding-left: 90px; padding-right: 90px; padding-top: 50px; padding-bottom: 50px; background-color: #f0f3f9; } </style> </head> <body> <span class=\"img-container\"> <!-- Inline parent element --> %s </span> </body> </html> """ % ( header_html, ) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia _a = """ This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html). First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset, a pre-processed fixed snapshot of Wikipedia. """ st.sidebar.markdown(description, unsafe_allow_html=True) _a = [ """Answer the question""", """View the retrieved document only""", """View the most similar ELI5 question and answer""", """Show me everything, please!""", ] _a = st.sidebar.checkbox("""Demo options""") if demo_options: _a = st.sidebar.selectbox( """""", action_list, index=3, ) _a = action_list.index(action_st) _a = st.sidebar.selectbox( """""", ["""Show full text of passages""", """Show passage section titles"""], index=0, ) _a = show_type == """Show full text of passages""" else: _a = 3 _a = True _a = st.sidebar.checkbox("""Retrieval options""") if retrieval_options: _a = """ ### Information retriever options The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs. The answer is then generated by sequence to sequence model which takes the question and retrieved document as input. """ st.sidebar.markdown(retriever_info) _a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""]) _a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""]) else: _a = """wiki40b""" _a = """dense""" _a = """beam""" _a = 2 _a = 64 _a = 256 _a = None _a = None _a = st.sidebar.checkbox("""Generation options""") if generate_options: _a = """ ### Answer generation options The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large) weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with **beam** search, or **sample** from the decoder's output probabilities. """ st.sidebar.markdown(generate_info) _a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""]) _a = st.sidebar.slider( """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None ) _a = st.sidebar.slider( """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None ) if sampled == "beam": _a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: _a = st.sidebar.slider( """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) _a = st.sidebar.slider( """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) _a = None # start main text _a = [ """<MY QUESTION>""", """How do people make chocolate?""", """Why do we get a fever when we are sick?""", """How can different animals perceive different colors?""", """What is natural language processing?""", """What's the best way to treat a sunburn?""", """What exactly are vitamins ?""", """How does nuclear energy provide electricity?""", """What's the difference between viruses and bacteria?""", """Why are flutes classified as woodwinds when most of them are made out of metal ?""", """Why do people like drinking coffee even though it tastes so bad?""", """What happens when wine ages? How does it make the wine taste better?""", """If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""", """How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""", """How does New Zealand have so many large bird predators?""", ] _a = st.selectbox( """What would you like to ask? ---- select <MY QUESTION> to enter a new query""", questions_list, index=1, ) if question_s == "<MY QUESTION>": _a = st.text_input("""Enter your question here:""", """""") else: _a = question_s if st.button("""Show me!"""): if action in [0, 1, 3]: if index_type == "mixed": _a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10) _a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10) _a = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] _a = support_list[:10] _a = """<P> """ + """ <P> """.join([res[-1] for res in support_list]) else: _a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10) if action in [0, 3]: _a , _a = answer_question( question_doc, sas_model, sas_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == """sampled"""), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown("""### The model generated answer is:""") st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""") for i, res in enumerate(support_list): _a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_""")) _a = res[1].strip() if sec_titles == "": _a = """[{}]({})""".format(res[0], wiki_url) else: _a = sec_titles.split(""" & """) _a = """ & """.join( ["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list] ) st.markdown( """{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( """> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True ) if action in [2, 3]: _a = find_nearest_training(question) _a = nn_train_list[0] st.markdown( """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""]) ) _a = [ """{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""])) for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""])) if i == 0 or sc > 2 ] st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st))) _a = """ --- **Disclaimer** *The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system. Evaluating biases of such a model and ensuring factual generations are still very much open research problems. Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.* """ st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_torch_available, ) _lowerCamelCase : List[Any] = { '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase : List[Any] = [ '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''', '''MegaForCausalLM''', '''MegaForMaskedLM''', '''MegaForMultipleChoice''', '''MegaForQuestionAnswering''', '''MegaForSequenceClassification''', '''MegaForTokenClassification''', '''MegaModel''', '''MegaPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mega import ( MEGA_PRETRAINED_MODEL_ARCHIVE_LIST, MegaForCausalLM, MegaForMaskedLM, MegaForMultipleChoice, MegaForQuestionAnswering, MegaForSequenceClassification, MegaForTokenClassification, MegaModel, MegaPreTrainedModel, ) else: import sys _lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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"""simple docstring""" import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() _a = logging.get_logger(__name__) _a = { """post_extract_proj""": """feature_projection.projection""", """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""", """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""", """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""", """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""", """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""", """self_attn_layer_norm""": """encoder.layers.*.layer_norm""", """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""", """fc2""": """encoder.layers.*.feed_forward.output_dense""", """final_layer_norm""": """encoder.layers.*.final_layer_norm""", """encoder.layer_norm""": """encoder.layer_norm""", """w2v_model.layer_norm""": """feature_projection.layer_norm""", """quantizer.weight_proj""": """quantizer.weight_proj""", """quantizer.vars""": """quantizer.codevectors""", """project_q""": """project_q""", """final_proj""": """project_hid""", """w2v_encoder.proj""": """lm_head""", """mask_emb""": """masked_spec_embed""", } _a = [ """lm_head""", """quantizer.weight_proj""", """quantizer.codevectors""", """project_q""", """project_hid""", ] def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple: """simple docstring""" for attribute in key.split('''.''' ): _UpperCamelCase = getattr(__snake_case, __snake_case ) if weight_type is not None: _UpperCamelCase = getattr(__snake_case, __snake_case ).shape else: _UpperCamelCase = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": _UpperCamelCase = value elif weight_type == "weight_g": _UpperCamelCase = value elif weight_type == "weight_v": _UpperCamelCase = value elif weight_type == "bias": _UpperCamelCase = value else: _UpperCamelCase = value logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' ) def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = [] _UpperCamelCase = fairseq_model.state_dict() _UpperCamelCase = hf_model.feature_extractor _UpperCamelCase = hf_model.adapter for name, value in fairseq_dict.items(): _UpperCamelCase = False if "conv_layers" in name: load_conv_layer( __snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', ) _UpperCamelCase = True elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ): load_adapter(__snake_case, __snake_case, __snake_case, __snake_case ) _UpperCamelCase = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: _UpperCamelCase = True if "*" in mapped_key: _UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2] _UpperCamelCase = mapped_key.replace('''*''', __snake_case ) if "weight_g" in name: _UpperCamelCase = '''weight_g''' elif "weight_v" in name: _UpperCamelCase = '''weight_v''' elif "bias" in name: _UpperCamelCase = '''bias''' elif "weight" in name: _UpperCamelCase = '''weight''' else: _UpperCamelCase = None set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) continue if not is_used: unused_weights.append(__snake_case ) logger.warning(F'''Unused weights: {unused_weights}''' ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = full_name.split('''conv_layers.''' )[-1] _UpperCamelCase = name.split('''.''' ) _UpperCamelCase = int(items[0] ) _UpperCamelCase = int(items[1] ) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict: """simple docstring""" _UpperCamelCase = full_name.split('''adaptor.''' )[-1] _UpperCamelCase = name.split('''.''' ) if items[1].isdigit(): _UpperCamelCase = int(items[1] ) else: _UpperCamelCase = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' _UpperCamelCase = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' ) elif isinstance(__snake_case, __snake_case ): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase , _UpperCamelCase = emb.weight.shape _UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case ) _UpperCamelCase = emb.weight.data return lin_layer @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = WavaVecaConfig.from_pretrained( __snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, ) _UpperCamelCase = MBartConfig.from_pretrained(__snake_case ) # load model _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={ '''config_yaml''': config_yaml_path, '''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path, '''load_pretrained_decoder_from''': None, }, ) _UpperCamelCase = model[0].eval() # load feature extractor _UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case ) # set weights for wav2vec2 encoder _UpperCamelCase = WavaVecaModel(__snake_case ) recursively_load_weights_wavaveca(model.encoder, __snake_case ) # load decoder weights _UpperCamelCase = MBartForCausalLM(__snake_case ) _UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case ) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' ) logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' ) _UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case ) _UpperCamelCase = False _UpperCamelCase = MBartaaTokenizer(__snake_case ) tokenizer.save_pretrained(__snake_case ) _UpperCamelCase = hf_wavavec.config.to_dict() _UpperCamelCase = tokenizer.pad_token_id _UpperCamelCase = tokenizer.bos_token_id _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = '''mbart50''' _UpperCamelCase = '''wav2vec2''' _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = 25_00_04 _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case ) hf_wavavec.save_pretrained(__snake_case ) feature_extractor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""") parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""") parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""") parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""") parser.add_argument( """--encoder_config_path""", default="""facebook/wav2vec2-xls-r-1b""", type=str, help="""Path to hf encoder wav2vec2 checkpoint config""", ) parser.add_argument( """--decoder_config_path""", default="""facebook/mbart-large-50-one-to-many-mmt""", type=str, help="""Path to hf decoder checkpoint config""", ) parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""") parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""") parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""") parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""") parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""") _a = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )
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'''simple docstring''' import sys from collections.abc import Mapping from typing import TYPE_CHECKING, Dict, Optional import numpy as np import pyarrow as pa from .. import config from ..utils.logging import get_logger from ..utils.py_utils import map_nested from .formatting import TensorFormatter if TYPE_CHECKING: import jax import jaxlib _snake_case : str = get_logger() _snake_case : List[Any] = None class A ( TensorFormatter[Mapping, 'jax.Array', Mapping] ): def __init__( self : Any , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Tuple=None , **lowerCAmelCase_ : Dict ) -> str: """simple docstring""" super().__init__(features=__a ) import jax from jaxlib.xla_client import Device if isinstance(__a , __a ): raise ValueError( F'Expected {device} to be a `str` not {type(__a )}, as `jaxlib.xla_extension.Device` ' '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround ''' '''the device with `str()` to get its string identifier that will be internally mapped ''' '''to the actual `jaxlib.xla_extension.Device`.''' ) _a = device if isinstance(__a , __a ) else str(jax.devices()[0] ) # using global variable since `jaxlib.xla_extension.Device` is not serializable neither # with `pickle` nor with `dill`, so we need to use a global variable instead global DEVICE_MAPPING if DEVICE_MAPPING is None: _a = self._map_devices_to_str() if self.device not in list(DEVICE_MAPPING.keys() ): logger.warning( F'Device with string identifier {self.device} not listed among the available ' F'devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default ' F'device: {str(jax.devices()[0] )}.' ) _a = str(jax.devices()[0] ) _a = jnp_array_kwargs @staticmethod def __lowerCAmelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]: """simple docstring""" import jax return {str(__a ): device for device in jax.devices()} def __lowerCAmelCase ( self : int , lowerCAmelCase_ : str ) -> Any: """simple docstring""" import jax import jax.numpy as jnp if isinstance(__a , __a ) and column: if all( isinstance(__a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ): return jnp.stack(__a , axis=0 ) return column def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : Any ) -> Dict: """simple docstring""" import jax import jax.numpy as jnp if isinstance(__a , (str, bytes, type(__a )) ): return value elif isinstance(__a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ): return value.tolist() _a = {} if isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ): # the default int precision depends on the jax config # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision if jax.config.jax_enable_xaa: _a = {'''dtype''': jnp.intaa} else: _a = {'''dtype''': jnp.intaa} elif isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ): _a = {'''dtype''': jnp.floataa} elif config.PIL_AVAILABLE and "PIL" in sys.modules: import PIL.Image if isinstance(__a , PIL.Image.Image ): _a = np.asarray(__a ) # using global variable since `jaxlib.xla_extension.Device` is not serializable neither # with `pickle` nor with `dill`, so we need to use a global variable instead global DEVICE_MAPPING if DEVICE_MAPPING is None: _a = self._map_devices_to_str() with jax.default_device(DEVICE_MAPPING[self.device] ): # calling jnp.array on a np.ndarray does copy the data # see https://github.com/google/jax/issues/4486 return jnp.array(__a , **{**default_dtype, **self.jnp_array_kwargs} ) def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : List[str] ) -> str: """simple docstring""" import jax # support for torch, tf, jax etc. if config.TORCH_AVAILABLE and "torch" in sys.modules: import torch if isinstance(__a , torch.Tensor ): return self._tensorize(data_struct.detach().cpu().numpy()[()] ) if hasattr(__a , '''__array__''' ) and not isinstance(__a , jax.Array ): _a = data_struct.__array__() # support for nested types like struct of list of struct if isinstance(__a , np.ndarray ): if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] ) elif isinstance(__a , (list, tuple) ): return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] ) return self._tensorize(__a ) def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict ) -> Optional[int]: """simple docstring""" return map_nested(self._recursive_tensorize , __a , map_list=__a ) def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : Union[str, Any] ) -> Mapping: """simple docstring""" _a = self.numpy_arrow_extractor().extract_row(__a ) _a = self.python_features_decoder.decode_row(__a ) return self.recursive_tensorize(__a ) def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict ) -> "jax.Array": """simple docstring""" _a = self.numpy_arrow_extractor().extract_column(__a ) _a = self.python_features_decoder.decode_column(__a , pa_table.column_names[0] ) _a = self.recursive_tensorize(__a ) _a = self._consolidate(__a ) return column def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] ) -> Mapping: """simple docstring""" _a = self.numpy_arrow_extractor().extract_batch(__a ) _a = self.python_features_decoder.decode_batch(__a ) _a = self.recursive_tensorize(__a ) for column_name in batch: _a = self._consolidate(batch[column_name] ) return batch
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"""simple docstring""" import fire from utils import calculate_rouge, save_json def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]: """simple docstring""" _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()] _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )] _UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case ) if save_path is not None: save_json(__snake_case, __snake_case, indent=__snake_case ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)
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'''simple docstring''' from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import torch from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available @dataclass class _lowerCAmelCase ( UpperCamelCase_ ): """simple docstring""" lowerCAmelCase = 42 try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
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"""simple docstring""" import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['image_processor', 'tokenizer'] lowercase__ = 'ViTImageProcessor' lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast') def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = None if "feature_extractor" in kwargs: warnings.warn( '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`''' ''' instead.''' , __a , ) _UpperCamelCase = kwargs.pop('''feature_extractor''') _UpperCamelCase = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError('''You need to specify an `image_processor`.''') if tokenizer is None: raise ValueError('''You need to specify a `tokenizer`.''') super().__init__(__a , __a) def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple: '''simple docstring''' if text is None and visual_prompt is None and images is None: raise ValueError('''You have to specify either text, visual prompt or images.''') if text is not None and visual_prompt is not None: raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''') if text is not None: _UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a) if visual_prompt is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if images is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if visual_prompt is not None and images is not None: _UpperCamelCase = { '''pixel_values''': image_features.pixel_values, '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding elif text is not None and images is not None: _UpperCamelCase = image_features.pixel_values return encoding elif text is not None: return encoding elif visual_prompt is not None: _UpperCamelCase = { '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding else: return BatchEncoding(data=dict(**__a) , tensor_type=__a) def UpperCAmelCase ( self , *__a , **__a) -> Any: '''simple docstring''' return self.tokenizer.batch_decode(*__a , **__a) def UpperCAmelCase ( self , *__a , **__a) -> List[str]: '''simple docstring''' return self.tokenizer.decode(*__a , **__a) @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , ) return self.image_processor_class @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , ) return self.image_processor
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'''simple docstring''' import os from bleurt import score # From: git+https://github.com/google-research/bleurt.git import datasets UpperCamelCase__ : int = datasets.logging.get_logger(__name__) UpperCamelCase__ : int = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n booktitle={ACL},\n year={2020},\n url={https://arxiv.org/abs/2004.04696}\n}\n' UpperCamelCase__ : int = '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n' UpperCamelCase__ : str = '\nBLEURT score.\n\nArgs:\n `predictions` (list of str): prediction/candidate sentences\n `references` (list of str): reference sentences\n `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n \'scores\': List of scores.\nExamples:\n\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> bleurt = datasets.load_metric(\"bleurt\")\n >>> results = bleurt.compute(predictions=predictions, references=references)\n >>> print([round(v, 2) for v in results[\"scores\"]])\n [1.03, 1.04]\n' UpperCamelCase__ : Tuple = { 'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip', 'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip', 'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip', 'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip', 'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip', 'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip', 'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip', 'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip', 'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip', 'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip', } @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION ) class _lowercase ( datasets.Metric ): '''simple docstring''' def lowerCAmelCase__ ( self ) -> str: '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION ,citation=_CITATION ,homepage='''https://github.com/google-research/bleurt''' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features( { '''predictions''': datasets.Value('''string''' ,id='''sequence''' ), '''references''': datasets.Value('''string''' ,id='''sequence''' ), } ) ,codebase_urls=['''https://github.com/google-research/bleurt'''] ,reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696'''] ,) def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Union[str, Any]: '''simple docstring''' if self.config_name == "default": logger.warning( '''Using default BLEURT-Base checkpoint for sequence maximum length 128. ''' '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').''' ) UpperCAmelCase__ : List[Any] = '''bleurt-base-128''' if self.config_name.lower() in CHECKPOINT_URLS: UpperCAmelCase__ : List[str] = self.config_name.lower() elif self.config_name.upper() in CHECKPOINT_URLS: UpperCAmelCase__ : Any = self.config_name.upper() else: raise KeyError( f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' ) # download the model checkpoint specified by self.config_name and set up the scorer UpperCAmelCase__ : Tuple = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] ) UpperCAmelCase__ : Any = score.BleurtScorer(os.path.join(__a ,__a ) ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> Dict: '''simple docstring''' UpperCAmelCase__ : List[str] = self.scorer.score(references=__a ,candidates=__a ) return {"scores": scores}
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"""simple docstring""" import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import UperNetForSemanticSegmentation from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = num_channels _UpperCamelCase = num_stages _UpperCamelCase = hidden_sizes _UpperCamelCase = depths _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = out_features _UpperCamelCase = num_labels _UpperCamelCase = scope _UpperCamelCase = num_stages def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ConvNextConfig( num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return UperNetConfig( backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = UperNetForSemanticSegmentation(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size)) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else () lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = UperNetModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*__a) @unittest.skip(reason='''UperNet does not use inputs_embeds''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not support input and output embeddings''') def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> int: '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''') def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' pass @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' def check_hidden_states_output(__a , __a , __a): _UpperCamelCase = model_class(__a) model.to(__a) model.eval() with torch.no_grad(): _UpperCamelCase = model(**self._prepare_for_class(__a , __a)) _UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states _UpperCamelCase = self.model_tester.num_stages self.assertEqual(len(__a) , expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) _UpperCamelCase = _config_zero_init(configs_no_init.backbone_config) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @unittest.skip(reason='''UperNet does not have tied weights''') def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' pass @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = hf_hub_download( repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' ) _UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' ) return image @require_torch @require_vision @slow class _UpperCAmelCase( unittest.TestCase ): def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
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'''simple docstring''' import warnings from ...utils import is_sklearn_available, requires_backends if is_sklearn_available(): from scipy.stats import pearsonr, spearmanr from sklearn.metrics import fa_score, matthews_corrcoef lowercase =( 'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate ' 'library. You can have a look at this example script for pointers: ' 'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' ) def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ): '''simple docstring''' warnings.warn(__snake_case , __snake_case ) requires_backends(__snake_case , 'sklearn' ) return (preds == labels).mean() def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : str ): '''simple docstring''' warnings.warn(__snake_case , __snake_case ) requires_backends(__snake_case , 'sklearn' ) _UpperCAmelCase : Tuple =simple_accuracy(__snake_case , __snake_case ) _UpperCAmelCase : Tuple =fa_score(y_true=__snake_case , y_pred=__snake_case ) return { "acc": acc, "f1": fa, "acc_and_f1": (acc + fa) / 2, } def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ): '''simple docstring''' warnings.warn(__snake_case , __snake_case ) requires_backends(__snake_case , 'sklearn' ) _UpperCAmelCase : Any =pearsonr(__snake_case , __snake_case )[0] _UpperCAmelCase : Union[str, Any] =spearmanr(__snake_case , __snake_case )[0] return { "pearson": pearson_corr, "spearmanr": spearman_corr, "corr": (pearson_corr + spearman_corr) / 2, } def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int ): '''simple docstring''' warnings.warn(__snake_case , __snake_case ) requires_backends(__snake_case , 'sklearn' ) assert len(__snake_case ) == len(__snake_case ), f"Predictions and labels have mismatched lengths {len(__snake_case )} and {len(__snake_case )}" if task_name == "cola": return {"mcc": matthews_corrcoef(__snake_case , __snake_case )} elif task_name == "sst-2": return {"acc": simple_accuracy(__snake_case , __snake_case )} elif task_name == "mrpc": return acc_and_fa(__snake_case , __snake_case ) elif task_name == "sts-b": return pearson_and_spearman(__snake_case , __snake_case ) elif task_name == "qqp": return acc_and_fa(__snake_case , __snake_case ) elif task_name == "mnli": return {"mnli/acc": simple_accuracy(__snake_case , __snake_case )} elif task_name == "mnli-mm": return {"mnli-mm/acc": simple_accuracy(__snake_case , __snake_case )} elif task_name == "qnli": return {"acc": simple_accuracy(__snake_case , __snake_case )} elif task_name == "rte": return {"acc": simple_accuracy(__snake_case , __snake_case )} elif task_name == "wnli": return {"acc": simple_accuracy(__snake_case , __snake_case )} elif task_name == "hans": return {"acc": simple_accuracy(__snake_case , __snake_case )} else: raise KeyError(__snake_case ) def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] ): '''simple docstring''' warnings.warn(__snake_case , __snake_case ) requires_backends(__snake_case , 'sklearn' ) if len(__snake_case ) != len(__snake_case ): raise ValueError(f"Predictions and labels have mismatched lengths {len(__snake_case )} and {len(__snake_case )}" ) if task_name == "xnli": return {"acc": simple_accuracy(__snake_case , __snake_case )} else: raise KeyError(__snake_case )
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DDPMScheduler,) def UpperCAmelCase ( self , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 10_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''variance_type''': '''fixed_small''', '''clip_sample''': True, } config.update(**__a) return config def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for timesteps in [1, 5, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.check_over_configs(thresholding=__a) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=__a , prediction_type=__a , sample_max_value=__a , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for t in [0, 5_00, 9_99]: self.check_over_forward(time_step=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 258.9606) < 1e-2 assert abs(result_mean.item() - 0.3372) < 1e-3 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 202.0296) < 1e-2 assert abs(result_mean.item() - 0.2631) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=__a) _UpperCamelCase = scheduler.timesteps for i, timestep in enumerate(__a): if i == len(__a) - 1: _UpperCamelCase = -1 else: _UpperCamelCase = timesteps[i + 1] _UpperCamelCase = scheduler.previous_timestep(__a) _UpperCamelCase = prev_t.item() self.assertEqual(__a , __a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 51, 0] with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''): scheduler.set_timesteps(timesteps=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] _UpperCamelCase = len(__a) with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''): scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [scheduler.config.num_train_timesteps] with self.assertRaises( __a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ): scheduler.set_timesteps(timesteps=__a)
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import unittest from transformers import MobileBertConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, MobileBertForMaskedLM, MobileBertForMultipleChoice, MobileBertForNextSentencePrediction, MobileBertForPreTraining, MobileBertForQuestionAnswering, MobileBertForSequenceClassification, MobileBertForTokenClassification, MobileBertModel, ) class lowerCamelCase : """simple docstring""" def __init__( self : Any, _UpperCAmelCase : int, _UpperCAmelCase : str=1_3, _UpperCAmelCase : Tuple=7, _UpperCAmelCase : int=True, _UpperCAmelCase : Any=True, _UpperCAmelCase : Optional[Any]=True, _UpperCAmelCase : List[str]=True, _UpperCAmelCase : Union[str, Any]=9_9, _UpperCAmelCase : Tuple=6_4, _UpperCAmelCase : Optional[Any]=3_2, _UpperCAmelCase : Dict=5, _UpperCAmelCase : Any=4, _UpperCAmelCase : int=3_7, _UpperCAmelCase : Union[str, Any]="gelu", _UpperCAmelCase : Optional[int]=0.1, _UpperCAmelCase : Optional[int]=0.1, _UpperCAmelCase : str=5_1_2, _UpperCAmelCase : Tuple=1_6, _UpperCAmelCase : List[str]=2, _UpperCAmelCase : int=0.02, _UpperCAmelCase : Optional[Any]=3, _UpperCAmelCase : int=4, _UpperCAmelCase : Optional[int]=None, ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = parent SCREAMING_SNAKE_CASE__ : List[Any] = batch_size SCREAMING_SNAKE_CASE__ : List[str] = seq_length SCREAMING_SNAKE_CASE__ : str = is_training SCREAMING_SNAKE_CASE__ : Tuple = use_input_mask SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids SCREAMING_SNAKE_CASE__ : Any = use_labels SCREAMING_SNAKE_CASE__ : Tuple = vocab_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size SCREAMING_SNAKE_CASE__ : int = embedding_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads SCREAMING_SNAKE_CASE__ : Dict = intermediate_size SCREAMING_SNAKE_CASE__ : str = hidden_act SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Any = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Tuple = max_position_embeddings SCREAMING_SNAKE_CASE__ : str = type_vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = type_sequence_label_size SCREAMING_SNAKE_CASE__ : Any = initializer_range SCREAMING_SNAKE_CASE__ : Tuple = num_labels SCREAMING_SNAKE_CASE__ : str = num_choices SCREAMING_SNAKE_CASE__ : Optional[Any] = scope def A_ ( self : Tuple ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length], self.vocab_size ) SCREAMING_SNAKE_CASE__ : List[str] = None if self.use_input_mask: SCREAMING_SNAKE_CASE__ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] ) SCREAMING_SNAKE_CASE__ : Tuple = None if self.use_token_type_ids: SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = None SCREAMING_SNAKE_CASE__ : Optional[int] = None SCREAMING_SNAKE_CASE__ : str = None if self.use_labels: SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size], self.type_sequence_label_size ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.num_labels ) SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size], self.num_choices ) SCREAMING_SNAKE_CASE__ : int = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A_ ( self : Dict ) -> List[str]: """simple docstring""" return MobileBertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, embedding_size=self.embedding_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=__a, initializer_range=self.initializer_range, ) def A_ ( self : Optional[int], _UpperCAmelCase : str, _UpperCAmelCase : Tuple, _UpperCAmelCase : Any, _UpperCAmelCase : Tuple, _UpperCAmelCase : Dict, _UpperCAmelCase : Dict, _UpperCAmelCase : List[str] ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = MobileBertModel(config=__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : int = model(__a, attention_mask=__a, token_type_ids=__a ) SCREAMING_SNAKE_CASE__ : List[str] = model(__a, token_type_ids=__a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model(__a ) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size) ) def A_ ( self : Optional[int], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : str, _UpperCAmelCase : Optional[int], _UpperCAmelCase : Any, _UpperCAmelCase : Optional[int], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : List[Any] ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = MobileBertForMaskedLM(config=__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : List[str] = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) ) def A_ ( self : List[str], _UpperCAmelCase : str, _UpperCAmelCase : str, _UpperCAmelCase : Dict, _UpperCAmelCase : Tuple, _UpperCAmelCase : Dict, _UpperCAmelCase : Any, _UpperCAmelCase : str ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = MobileBertForNextSentencePrediction(config=__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : Any = model( __a, attention_mask=__a, token_type_ids=__a, labels=__a, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, 2) ) def A_ ( self : Tuple, _UpperCAmelCase : Optional[int], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : str, _UpperCAmelCase : str, _UpperCAmelCase : int, _UpperCAmelCase : List[str], _UpperCAmelCase : Dict ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = MobileBertForPreTraining(config=__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : Optional[int] = model( __a, attention_mask=__a, token_type_ids=__a, labels=__a, next_sentence_label=__a, ) self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2) ) def A_ ( self : Optional[Any], _UpperCAmelCase : List[str], _UpperCAmelCase : str, _UpperCAmelCase : str, _UpperCAmelCase : List[Any], _UpperCAmelCase : Optional[Any], _UpperCAmelCase : str, _UpperCAmelCase : List[str] ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = MobileBertForQuestionAnswering(config=__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : int = model( __a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__a, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) ) def A_ ( self : Optional[Any], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : Optional[Any], _UpperCAmelCase : List[str], _UpperCAmelCase : Dict, _UpperCAmelCase : int, _UpperCAmelCase : int, _UpperCAmelCase : List[Any] ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.num_labels SCREAMING_SNAKE_CASE__ : Dict = MobileBertForSequenceClassification(__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : Any = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) ) def A_ ( self : Optional[Any], _UpperCAmelCase : Optional[int], _UpperCAmelCase : List[str], _UpperCAmelCase : List[Any], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : Tuple, _UpperCAmelCase : str, _UpperCAmelCase : List[Any] ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.num_labels SCREAMING_SNAKE_CASE__ : Any = MobileBertForTokenClassification(config=__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : List[Any] = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) ) def A_ ( self : Union[str, Any], _UpperCAmelCase : List[str], _UpperCAmelCase : Optional[int], _UpperCAmelCase : Any, _UpperCAmelCase : str, _UpperCAmelCase : Dict, _UpperCAmelCase : Optional[Any], _UpperCAmelCase : int ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.num_choices SCREAMING_SNAKE_CASE__ : Any = MobileBertForMultipleChoice(config=__a ) model.to(__a ) model.eval() SCREAMING_SNAKE_CASE__ : List[str] = input_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous() SCREAMING_SNAKE_CASE__ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous() SCREAMING_SNAKE_CASE__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous() SCREAMING_SNAKE_CASE__ : Tuple = model( __a, attention_mask=__a, token_type_ids=__a, labels=__a, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) ) def A_ ( self : Optional[int] ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs() ( ( SCREAMING_SNAKE_CASE__ ) ,( SCREAMING_SNAKE_CASE__ ) ,( SCREAMING_SNAKE_CASE__ ) ,( SCREAMING_SNAKE_CASE__ ) ,( SCREAMING_SNAKE_CASE__ ) ,( SCREAMING_SNAKE_CASE__ ) ,( SCREAMING_SNAKE_CASE__ ) , ) : Optional[int] = config_and_inputs SCREAMING_SNAKE_CASE__ : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class lowerCamelCase (__lowerCamelCase , __lowerCamelCase , unittest.TestCase ): """simple docstring""" UpperCAmelCase_ = ( ( MobileBertModel, MobileBertForMaskedLM, MobileBertForMultipleChoice, MobileBertForNextSentencePrediction, MobileBertForPreTraining, MobileBertForQuestionAnswering, MobileBertForSequenceClassification, MobileBertForTokenClassification, ) if is_torch_available() else () ) UpperCAmelCase_ = ( { "feature-extraction": MobileBertModel, "fill-mask": MobileBertForMaskedLM, "question-answering": MobileBertForQuestionAnswering, "text-classification": MobileBertForSequenceClassification, "token-classification": MobileBertForTokenClassification, "zero-shot": MobileBertForSequenceClassification, } if is_torch_available() else {} ) UpperCAmelCase_ = True def A_ ( self : Optional[Any], _UpperCAmelCase : Dict, _UpperCAmelCase : Tuple, _UpperCAmelCase : List[str]=False ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = super()._prepare_for_class(__a, __a, return_labels=__a ) if return_labels: if model_class in get_values(__a ): SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=__a ) SCREAMING_SNAKE_CASE__ : List[Any] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=__a ) return inputs_dict def A_ ( self : Optional[Any] ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = MobileBertModelTester(self ) SCREAMING_SNAKE_CASE__ : Tuple = ConfigTester(self, config_class=__a, hidden_size=3_7 ) def A_ ( self : Tuple ) -> int: """simple docstring""" self.config_tester.run_common_tests() def A_ ( self : int ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_model(*__a ) def A_ ( self : Tuple ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a ) def A_ ( self : str ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a ) def A_ ( self : Union[str, Any] ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a ) def A_ ( self : str ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_pretraining(*__a ) def A_ ( self : str ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_question_answering(*__a ) def A_ ( self : Dict ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a ) def A_ ( self : int ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mobilebert_for_token_classification(*__a ) def _a ( SCREAMING_SNAKE_CASE__ : int ) -> Tuple: '''simple docstring''' return torch.tensor( __snake_case , dtype=torch.long , device=__snake_case , ) _lowerCamelCase : Union[str, Any] = 1e-3 @require_torch @require_sentencepiece @require_tokenizers class lowerCamelCase (unittest.TestCase ): """simple docstring""" @slow def A_ ( self : Dict ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] ) with torch.no_grad(): SCREAMING_SNAKE_CASE__ : Optional[int] = model(__a )[0] SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size((1, 9, 5_1_2) ) self.assertEqual(output.shape, __a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor( [ [ [-2.4736526E07, 8.2691656E04, 1.6521838E05], [-5.7541704E-01, 3.9056022E00, 4.4011507E00], [2.6047359E00, 1.5677652E00, -1.7324188E-01], ] ], device=__a, ) # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a # ~1 difference, it's therefore not a good idea to measure using addition. # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE SCREAMING_SNAKE_CASE__ : List[str] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE ) self.assertTrue(lower_bound and upper_bound )
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"""simple docstring""" from __future__ import annotations from functools import lru_cache from math import ceil _a = 100 _a = set(range(3, NUM_PRIMES, 2)) primes.add(2) _a = 42 for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=1_00 ) def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} _UpperCamelCase = set() _UpperCamelCase = 42 _UpperCamelCase = 42 for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None: """simple docstring""" for number_to_partition in range(1, __snake_case ): if len(partition(__snake_case ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(F"""{solution() = }""")
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import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES from ...utils import logging from ..auto import CONFIG_MAPPING __lowerCamelCase : str = logging.get_logger(__name__) __lowerCamelCase : Union[str, Any] = { "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json", } class __magic_name__ ( A__ ): lowercase : int ='''instructblip_vision_model''' def __init__( self : Union[str, Any] , UpperCamelCase__ : Tuple=14_08 , UpperCamelCase__ : List[str]=61_44 , UpperCamelCase__ : List[Any]=39 , UpperCamelCase__ : Tuple=16 , UpperCamelCase__ : Dict=2_24 , UpperCamelCase__ : str=14 , UpperCamelCase__ : Optional[int]="gelu" , UpperCamelCase__ : List[Any]=1e-6 , UpperCamelCase__ : Tuple=0.0 , UpperCamelCase__ : Tuple=1e-1_0 , UpperCamelCase__ : Any=True , **UpperCamelCase__ : List[Any] , ) -> int: '''simple docstring''' super().__init__(**__a ) UpperCAmelCase = hidden_size UpperCAmelCase = intermediate_size UpperCAmelCase = num_hidden_layers UpperCAmelCase = num_attention_heads UpperCAmelCase = patch_size UpperCAmelCase = image_size UpperCAmelCase = initializer_range UpperCAmelCase = attention_dropout UpperCAmelCase = layer_norm_eps UpperCAmelCase = hidden_act UpperCAmelCase = qkv_bias @classmethod def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , UpperCamelCase__ : Any , **UpperCamelCase__ : Dict ) -> "PretrainedConfig": '''simple docstring''' cls._set_token_in_kwargs(__a ) UpperCAmelCase , UpperCAmelCase = cls.get_config_dict(__a , **__a ) # get the vision config dict if we are loading from InstructBlipConfig if config_dict.get("model_type" ) == "instructblip": UpperCAmelCase = config_dict["vision_config"] if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(__a , **__a ) class __magic_name__ ( A__ ): lowercase : List[str] ='''instructblip_qformer''' def __init__( self : str , UpperCamelCase__ : List[str]=3_05_22 , UpperCamelCase__ : Dict=7_68 , UpperCamelCase__ : Optional[Any]=12 , UpperCamelCase__ : Dict=12 , UpperCamelCase__ : Tuple=30_72 , UpperCamelCase__ : Union[str, Any]="gelu" , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : Optional[int]=5_12 , UpperCamelCase__ : Dict=0.02 , UpperCamelCase__ : int=1e-1_2 , UpperCamelCase__ : str=0 , UpperCamelCase__ : Any="absolute" , UpperCamelCase__ : Union[str, Any]=2 , UpperCamelCase__ : List[Any]=14_08 , **UpperCamelCase__ : Union[str, Any] , ) -> Dict: '''simple docstring''' super().__init__(pad_token_id=__a , **__a ) UpperCAmelCase = vocab_size UpperCAmelCase = hidden_size UpperCAmelCase = num_hidden_layers UpperCAmelCase = num_attention_heads UpperCAmelCase = hidden_act UpperCAmelCase = intermediate_size UpperCAmelCase = hidden_dropout_prob UpperCAmelCase = attention_probs_dropout_prob UpperCAmelCase = max_position_embeddings UpperCAmelCase = initializer_range UpperCAmelCase = layer_norm_eps UpperCAmelCase = position_embedding_type UpperCAmelCase = cross_attention_frequency UpperCAmelCase = encoder_hidden_size @classmethod def SCREAMING_SNAKE_CASE_ ( cls : Tuple , UpperCamelCase__ : Dict , **UpperCamelCase__ : Any ) -> "PretrainedConfig": '''simple docstring''' cls._set_token_in_kwargs(__a ) UpperCAmelCase , UpperCAmelCase = cls.get_config_dict(__a , **__a ) # get the qformer config dict if we are loading from InstructBlipConfig if config_dict.get("model_type" ) == "instructblip": UpperCAmelCase = config_dict["qformer_config"] if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(__a , **__a ) class __magic_name__ ( A__ ): lowercase : Optional[int] ='''instructblip''' lowercase : List[str] =True def __init__( self : Union[str, Any] , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Union[str, Any]=32 , **UpperCamelCase__ : Optional[int] ) -> List[str]: '''simple docstring''' super().__init__(**__a ) if vision_config is None: UpperCAmelCase = {} logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." ) if qformer_config is None: UpperCAmelCase = {} logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." ) if text_config is None: UpperCAmelCase = {} logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." ) UpperCAmelCase = InstructBlipVisionConfig(**__a ) UpperCAmelCase = InstructBlipQFormerConfig(**__a ) UpperCAmelCase = text_config["model_type"] if "model_type" in text_config else "opt" UpperCAmelCase = CONFIG_MAPPING[text_model_type](**__a ) UpperCAmelCase = self.text_config.tie_word_embeddings UpperCAmelCase = self.text_config.is_encoder_decoder UpperCAmelCase = num_query_tokens UpperCAmelCase = self.vision_config.hidden_size UpperCAmelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES UpperCAmelCase = 1.0 UpperCAmelCase = 0.02 @classmethod def SCREAMING_SNAKE_CASE_ ( cls : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , **UpperCamelCase__ : Dict , ) -> int: '''simple docstring''' return cls( vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__a , ) def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase = copy.deepcopy(self.__dict__ ) UpperCAmelCase = self.vision_config.to_dict() UpperCAmelCase = self.qformer_config.to_dict() UpperCAmelCase = self.text_config.to_dict() UpperCAmelCase = self.__class__.model_type return output
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"""simple docstring""" from collections.abc import Callable import numpy as np def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array: """simple docstring""" _UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) ) _UpperCamelCase = np.zeros((n + 1,) ) _UpperCamelCase = ya _UpperCamelCase = xa for k in range(__snake_case ): _UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] ) _UpperCamelCase = y[k] + ( (step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class UpperCAmelCase ( a__ ): '''simple docstring''' SCREAMING_SNAKE_CASE = ["image_processor", "tokenizer"] SCREAMING_SNAKE_CASE = "AutoImageProcessor" SCREAMING_SNAKE_CASE = "AutoTokenizer" def __init__( self , __lowerCAmelCase , __lowerCAmelCase ) -> Any: super().__init__(__a , __a ) lowercase__ : Union[str, Any] = self.image_processor def __call__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase ) -> Any: if text is None and images is None: raise ValueError('''You have to specify either text or images. Both cannot be none.''' ) if text is not None: lowercase__ : Optional[int] = self.tokenizer(__a , return_tensors=__a , **__a ) if images is not None: lowercase__ : List[Any] = self.image_processor(__a , return_tensors=__a , **__a ) if text is not None and images is not None: lowercase__ : Tuple = image_features.pixel_values return encoding elif text is not None: return encoding else: return BatchEncoding(data=dict(**__a ) , tensor_type=__a ) def _lowerCAmelCase( self , *__lowerCAmelCase , **__lowerCAmelCase ) -> Any: return self.tokenizer.batch_decode(*__a , **__a ) def _lowerCAmelCase( self , *__lowerCAmelCase , **__lowerCAmelCase ) -> Any: return self.tokenizer.decode(*__a , **__a ) @property def _lowerCAmelCase( self ) -> int: return ["input_ids", "attention_mask", "pixel_values"]
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"""simple docstring""" import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": _a = argparse.ArgumentParser( description=( """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned""" """ Distillation""" ) ) parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""]) parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str) parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str) parser.add_argument("""--vocab_transform""", action="""store_true""") _a = parser.parse_args() if args.model_type == "bert": _a = BertForMaskedLM.from_pretrained(args.model_name) _a = """bert""" else: raise ValueError("""args.model_type should be \"bert\".""") _a = model.state_dict() _a = {} for w in ["word_embeddings", "position_embeddings"]: _a = state_dict[F"""{prefix}.embeddings.{w}.weight"""] for w in ["weight", "bias"]: _a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""] _a = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}""" ] std_idx += 1 _a = state_dict["""cls.predictions.decoder.weight"""] _a = state_dict["""cls.predictions.bias"""] if args.vocab_transform: for w in ["weight", "bias"]: _a = state_dict[F"""cls.predictions.transform.dense.{w}"""] _a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""] print(F"""N layers selected for distillation: {std_idx}""") print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""") print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""") torch.save(compressed_sd, args.dump_checkpoint)
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_snake_case = {} def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): '''simple docstring''' if late == 3 or absent == 2: return 0 # if we have no days left, and have not failed any other rules, # we have a prize string if days == 0: return 1 # No easy solution, so now we need to do the recursive calculation # First, check if the combination is already in the cache, and # if yes, return the stored value from there since we already # know the number of possible prize strings from this point on lowerCamelCase : Any = (days, absent, late) if key in cache: return cache[key] # now we calculate the three possible ways that can unfold from # this point on, depending on our attendance today # 1) if we are late (but not absent), the "absent" counter stays as # it is, but the "late" counter increases by one lowerCamelCase : int = _calculate(days - 1 , __snake_case , late + 1 ) # 2) if we are absent, the "absent" counter increases by 1, and the # "late" counter resets to 0 lowerCamelCase : Optional[int] = _calculate(days - 1 , absent + 1 , 0 ) # 3) if we are on time, this resets the "late" counter and keeps the # absent counter lowerCamelCase : Optional[int] = _calculate(days - 1 , __snake_case , 0 ) lowerCamelCase : List[Any] = state_late + state_absent + state_ontime lowerCamelCase : Any = prizestrings return prizestrings def lowercase_( SCREAMING_SNAKE_CASE_ = 30 ): '''simple docstring''' return _calculate(__snake_case , absent=0 , late=0 ) if __name__ == "__main__": print(solution())
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"""simple docstring""" import unittest from transformers import PegasusConfig, PegasusTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html _a = """platform""" import jax import jax.numpy as jnp import numpy as np from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel @require_flax class _UpperCAmelCase: lowercase__ = PegasusConfig lowercase__ = {} lowercase__ = 'gelu' def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = eos_token_id _UpperCamelCase = pad_token_id _UpperCamelCase = bos_token_id def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size) _UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1) _UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , ) _UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a) return config, inputs_dict def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''') _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ] , axis=-1 , ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]: """simple docstring""" if attention_mask is None: _UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta ) if decoder_attention_mask is None: _UpperCamelCase = np.concatenate( [ np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ), np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ), ], axis=-1, ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_flax class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( FlaxPegasusForConditionalGeneration, FlaxPegasusModel, ) if is_flax_available() else () ) lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else () lowercase__ = True lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = FlaxPegasusModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = self._prepare_for_class(__a , __a) _UpperCamelCase = model_class(__a) @jax.jit def encode_jitted(__a , __a=None , **__a): return model.encode(input_ids=__a , attention_mask=__a) with self.subTest('''JIT Enabled'''): _UpperCamelCase = encode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = encode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = model_class(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask''']) _UpperCamelCase = { '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''], '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''], '''encoder_outputs''': encoder_outputs, } @jax.jit def decode_jitted(__a , __a , __a): return model.decode( decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , ) with self.subTest('''JIT Enabled'''): _UpperCamelCase = decode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = decode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) @slow def UpperCAmelCase ( self) -> int: '''simple docstring''' for model_class_name in self.all_model_classes: _UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a) _UpperCamelCase = np.ones((1, 1)) _UpperCamelCase = model(__a) self.assertIsNotNone(__a) @slow def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = [ ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''', ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''', ] _UpperCamelCase = [ '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''', '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''', ] _UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a) _UpperCamelCase = model.generate(**__a , num_beams=2).sequences _UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a) assert tgt_text == decoded
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import math import flax.linen as nn import jax.numpy as jnp def lowerCAmelCase__ ( lowerCamelCase_ : int ,lowerCamelCase_ : Dict ,lowerCamelCase_ : Union[str, Any] = 1 ,lowerCamelCase_ : Union[str, Any] = 1 ,lowerCamelCase_ : str = 1.0E4 ,lowerCamelCase_ : Union[str, Any] = False ,lowerCamelCase_ : Any = 1.0 ,): '''simple docstring''' assert timesteps.ndim == 1, "Timesteps should be a 1d-array" assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even""" lowerCAmelCase__ : Optional[int] = float(embedding_dim // 2) lowerCAmelCase__ : Tuple = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift) lowerCAmelCase__ : List[str] = min_timescale * jnp.exp(jnp.arange(__snake_case ,dtype=jnp.floataa) * -log_timescale_increment) lowerCAmelCase__ : List[Any] = jnp.expand_dims(__snake_case ,1) * jnp.expand_dims(__snake_case ,0) # scale embeddings lowerCAmelCase__ : List[Any] = scale * emb if flip_sin_to_cos: lowerCAmelCase__ : Dict = jnp.concatenate([jnp.cos(__snake_case), jnp.sin(__snake_case)] ,axis=1) else: lowerCAmelCase__ : int = jnp.concatenate([jnp.sin(__snake_case), jnp.cos(__snake_case)] ,axis=1) lowerCAmelCase__ : int = jnp.reshape(__snake_case ,[jnp.shape(__snake_case)[0], embedding_dim]) return signal class lowerCamelCase__ ( nn.Module): '''simple docstring''' snake_case_ =32 snake_case_ =jnp.floataa @nn.compact def __call__(self ,__lowerCamelCase ) -> Any: """simple docstring""" lowerCAmelCase__ : str = nn.Dense(self.time_embed_dim ,dtype=self.dtype ,name='''linear_1''' )(__a ) lowerCAmelCase__ : Any = nn.silu(__a ) lowerCAmelCase__ : List[str] = nn.Dense(self.time_embed_dim ,dtype=self.dtype ,name='''linear_2''' )(__a ) return temb class lowerCamelCase__ ( nn.Module): '''simple docstring''' snake_case_ =32 snake_case_ =False snake_case_ =1 @nn.compact def __call__(self ,__lowerCamelCase ) -> Optional[Any]: """simple docstring""" return get_sinusoidal_embeddings( __a ,embedding_dim=self.dim ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.freq_shift )
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_input_mask _UpperCamelCase = use_token_type_ids _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = type_vocab_size _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = num_labels _UpperCamelCase = num_choices _UpperCamelCase = scope _UpperCamelCase = projection_dim def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py _UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length]) _UpperCamelCase = None if self.use_token_type_ids: _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size) _UpperCamelCase = None _UpperCamelCase = None _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels) _UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices) _UpperCamelCase = BertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , ) _UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]: '''simple docstring''' _UpperCamelCase = TFDPRContextEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict: '''simple docstring''' _UpperCamelCase = TFDPRReader(config=__a) _UpperCamelCase = model(__a , attention_mask=__a) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,)) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''input_ids''': input_ids} return config, inputs_dict @require_tf class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__a) @slow def UpperCAmelCase ( self) -> str: '''simple docstring''' for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRReader.from_pretrained(__a) self.assertIsNotNone(__a) @require_tf class _UpperCAmelCase( unittest.TestCase ): @slow def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''') _UpperCamelCase = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP] _UpperCamelCase = model(__a)[0] # embedding shape = (1, 768) # compare the actual values for a slice. _UpperCamelCase = tf.constant( [ [ 0.0323_6253, 0.1275_3335, 0.1681_8509, 0.0027_9786, 0.389_6933, 0.2426_4945, 0.217_8971, -0.0233_5227, -0.0848_1959, -0.1432_4117, ] ]) self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
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'''simple docstring''' import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging __lowerCAmelCase = logging.get_logger(__name__) __lowerCAmelCase = {"""vocab_file""": """spiece.model"""} __lowerCAmelCase = { """vocab_file""": { """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""", } } class UpperCAmelCase__ ( lowercase__ ): """simple docstring""" def __init__( self : Dict ,_a : Any ,_a : Union[str, Any]=False ,_a : List[Any]=True ,_a : Any=False ,_a : List[str]="<s>" ,_a : Optional[Any]="</s>" ,_a : Optional[int]="<unk>" ,_a : Optional[int]="<sep>" ,_a : Optional[int]="<pad>" ,_a : Tuple="<cls>" ,_a : List[str]="<mask>" ,_a : str=["<eop>", "<eod>"] ,_a : Any = None ,**_a : Tuple ,): '''simple docstring''' _a : Any = AddedToken(__a ,lstrip=__a ,rstrip=__a ) if isinstance(__a ,__a ) else mask_token _a : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=__a ,remove_space=__a ,keep_accents=__a ,bos_token=__a ,eos_token=__a ,unk_token=__a ,sep_token=__a ,pad_token=__a ,cls_token=__a ,mask_token=__a ,additional_special_tokens=__a ,sp_model_kwargs=self.sp_model_kwargs ,**__a ,) _a : Any = 3 _a : Any = do_lower_case _a : Optional[int] = remove_space _a : List[str] = keep_accents _a : str = vocab_file _a : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(__a ) try: import jieba except ModuleNotFoundError as error: raise error.__class__( 'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. ' 'See https://pypi.org/project/jieba/ for installation.' ) _a : List[Any] = jieba _a : Optional[int] = str.maketrans(' \n' ,'\u2582\u2583' ) @property # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size def __lowercase ( self : Union[str, Any] ): '''simple docstring''' return len(self.sp_model ) def __lowercase ( self : Any ): '''simple docstring''' _a : Union[str, Any] = {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Union[str, Any] ): '''simple docstring''' _a : Optional[Any] = self.__dict__.copy() _a : Any = None return state def __setstate__( self : str ,_a : Optional[Any] ): '''simple docstring''' _a : str = d # for backward compatibility if not hasattr(self ,'sp_model_kwargs' ): _a : Tuple = {} _a : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def __lowercase ( self : Any ,_a : Any ): '''simple docstring''' if self.remove_space: _a : Any = ' '.join(inputs.strip().split() ) else: _a : int = inputs _a : List[str] = outputs.replace('``' ,'"' ).replace('\'\'' ,'"' ) if not self.keep_accents: _a : Optional[int] = unicodedata.normalize('NFKD' ,__a ) _a : Dict = ''.join([c for c in outputs if not unicodedata.combining(__a )] ) if self.do_lower_case: _a : Union[str, Any] = outputs.lower() return outputs def __lowercase ( self : int ,_a : Tuple ): '''simple docstring''' _a : int = self.preprocess_text(__a ) _a : Any = self.sp_model.encode(__a ,out_type=__a ) _a : Dict = [] for piece in pieces: if len(__a ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit(): _a : List[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(__a ,'' ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: _a : Any = cur_pieces[1:] else: _a : List[str] = cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(__a ) else: new_pieces.append(__a ) return new_pieces def __lowercase ( self : Dict ,_a : Union[str, Any] ): '''simple docstring''' return self.sp_model.PieceToId(__a ) def __lowercase ( self : Optional[int] ,_a : List[str] ): '''simple docstring''' return self.sp_model.IdToPiece(__a ) def __lowercase ( self : str ,_a : Dict ): '''simple docstring''' _a : Dict = ''.join(__a ).replace(__a ,' ' ).strip() return out_string def __lowercase ( self : Dict ,_a : str ,_a : str = None ): '''simple docstring''' _a : Union[str, Any] = [self.sep_token_id] _a : Union[str, Any] = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def __lowercase ( self : str ,_a : List[str] ,_a : Optional[Any] = None ,_a : List[str] = False ): '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__a ,token_ids_a=__a ,already_has_special_tokens=__a ) if token_ids_a is not None: return ([0] * len(__a )) + [1] + ([0] * len(__a )) + [1, 1] return ([0] * len(__a )) + [1, 1] def __lowercase ( self : Optional[Any] ,_a : int ,_a : Any = None ): '''simple docstring''' _a : Dict = [self.sep_token_id] _a : Optional[int] = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def __lowercase ( self : Optional[int] ,_a : Union[str, Any] ,_a : Any = None ): '''simple docstring''' if not os.path.isdir(__a ): logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" ) return _a : Dict = os.path.join( __a ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file ,__a ) elif not os.path.isfile(self.vocab_file ): with open(__a ,'wb' ) as fi: _a : Optional[Any] = self.sp_model.serialized_model_proto() fi.write(__a ) return (out_vocab_file,) def __lowercase ( self : Tuple ,*_a : List[str] ,**_a : Any ): '''simple docstring''' _a : Tuple = super()._decode(*__a ,**__a ) _a : int = text.replace(' ' ,'' ).replace('\u2582' ,' ' ).replace('\u2583' ,'\n' ) return text
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"""simple docstring""" import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x2_0000 and cp <= 0x2_A6DF) # or (cp >= 0x2_A700 and cp <= 0x2_B73F) # or (cp >= 0x2_B740 and cp <= 0x2_B81F) # or (cp >= 0x2_B820 and cp <= 0x2_CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2_F800 and cp <= 0x2_FA1F) # ): # return True return False def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" for char in word: _UpperCamelCase = ord(__snake_case ) if not _is_chinese_char(__snake_case ): return 0 return 1 def lowerCamelCase__ ( __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = set() for token in tokens: _UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case ) if chinese_word: word_set.add(__snake_case ) _UpperCamelCase = list(__snake_case ) return word_list def lowerCamelCase__ ( __snake_case, __snake_case ) -> int: """simple docstring""" if not chinese_word_set: return bert_tokens _UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] ) _UpperCamelCase = bert_tokens _UpperCamelCase , _UpperCamelCase = 0, len(__snake_case ) while start < end: _UpperCamelCase = True if is_chinese(bert_word[start] ): _UpperCamelCase = min(end - start, __snake_case ) for i in range(__snake_case, 1, -1 ): _UpperCamelCase = ''''''.join(bert_word[start : start + i] ) if whole_word in chinese_word_set: for j in range(start + 1, start + i ): _UpperCamelCase = '''##''' + bert_word[j] _UpperCamelCase = start + i _UpperCamelCase = False break if single_word: start += 1 return bert_word def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws _UpperCamelCase = [get_chinese_word(__snake_case ) for r in res] ltp_res.extend(__snake_case ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 ) bert_res.extend(res['''input_ids'''] ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for input_ids, chinese_word in zip(__snake_case, __snake_case ): _UpperCamelCase = [] for id in input_ids: _UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case ) input_tokens.append(__snake_case ) _UpperCamelCase = add_sub_symbol(__snake_case, __snake_case ) _UpperCamelCase = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(__snake_case ): if token[:2] == "##": _UpperCamelCase = token[2:] # save chinese tokens' pos if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ): ref_id.append(__snake_case ) ref_ids.append(__snake_case ) assert len(__snake_case ) == len(__snake_case ) return ref_ids def lowerCamelCase__ ( __snake_case ) -> Optional[int]: """simple docstring""" with open(args.file_name, '''r''', encoding='''utf-8''' ) as f: _UpperCamelCase = f.readlines() _UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029' _UpperCamelCase = LTP(args.ltp ) # faster in GPU device _UpperCamelCase = BertTokenizer.from_pretrained(args.bert ) _UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case ) with open(args.save_path, '''w''', encoding='''utf-8''' ) as f: _UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids] f.writelines(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser(description="""prepare_chinese_ref""") parser.add_argument( """--file_name""", required=False, type=str, default="""./resources/chinese-demo.txt""", help="""file need process, same as training data in lm""", ) parser.add_argument( """--ltp""", required=False, type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path""", ) parser.add_argument( """--bert""", required=False, type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""", ) parser.add_argument( """--save_path""", required=False, type=str, default="""./resources/ref.txt""", help="""path to save res""", ) _a = parser.parse_args() main(args)
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _lowerCamelCase : List[str] = { '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase : Optional[Any] = [ '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''', '''PegasusXForConditionalGeneration''', '''PegasusXModel''', '''PegasusXPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_pegasus_x import ( PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST, PegasusXForConditionalGeneration, PegasusXModel, PegasusXPreTrainedModel, ) else: import sys _lowerCamelCase : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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"""simple docstring""" import heapq def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" _UpperCamelCase = [] # for each node and his adjacency list add them and the rank of the node to queue # using heapq module the queue will be filled like a Priority Queue # heapq works with a min priority queue, so I used -1*len(v) to build it for key, value in graph.items(): # O(log(n)) heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] ) # chosen_vertices = set of chosen vertices _UpperCamelCase = set() # while queue isn't empty and there are still edges # (queue[0][0] is the rank of the node with max rank) while queue and queue[0][0] != 0: # extract vertex with max rank from queue and add it to chosen_vertices _UpperCamelCase = heapq.heappop(__snake_case )[1][0] chosen_vertices.add(__snake_case ) # Remove all arcs adjacent to argmax for elem in queue: # if v haven't adjacent node, skip if elem[0] == 0: continue # if argmax is reachable from elem # remove argmax from elem's adjacent list and update his rank if argmax in elem[1][1]: _UpperCamelCase = elem[1][1].index(__snake_case ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(__snake_case ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() _a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
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'''simple docstring''' import argparse from pathlib import Path import fairseq import torch from fairseq.models.xmod import XMODModel as FairseqXmodModel from packaging import version from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification from transformers.utils import logging if version.parse(fairseq.__version__) < version.parse('0.12.2'): raise Exception('requires fairseq >= 0.12.2') if version.parse(fairseq.__version__) > version.parse('2'): raise Exception('requires fairseq < v2') logging.set_verbosity_info() _snake_case : Optional[int] = logging.get_logger(__name__) _snake_case : int = 'Hello, World!' _snake_case : str = 'en_XX' def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : Dict , UpperCamelCase : List[Any] ): '''simple docstring''' _a = Path('''data_bin''' ) _a = FairseqXmodModel.from_pretrained( model_name_or_path=str(Path(__snake_case ).parent ) , checkpoint_file=Path(__snake_case ).name , _name='''xmod_base''' , arch='''xmod_base''' , task='''multilingual_masked_lm''' , data_name_or_path=str(__snake_case ) , bpe='''sentencepiece''' , sentencepiece_model=str(Path(__snake_case ).parent / '''sentencepiece.bpe.model''' ) , src_dict=str(data_dir / '''dict.txt''' ) , ) xmod.eval() # disable dropout print(__snake_case ) _a = xmod.model.encoder.sentence_encoder _a = XmodConfig( vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , '''bottleneck''' , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , ) if classification_head: _a = xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0] print('''Our X-MOD config:''' , __snake_case ) _a = XmodForSequenceClassification(__snake_case ) if classification_head else XmodForMaskedLM(__snake_case ) model.eval() # Now let's copy all the weights. # Embeddings _a = xmod_sent_encoder.embed_tokens.weight _a = xmod_sent_encoder.embed_positions.weight _a = torch.zeros_like( model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them. _a = xmod_sent_encoder.layernorm_embedding.weight _a = xmod_sent_encoder.layernorm_embedding.bias for i in range(config.num_hidden_layers ): # Encoder: start of layer _a = model.roberta.encoder.layer[i] _a = xmod_sent_encoder.layers[i] # self attention _a = layer.attention.self if not ( xmod_layer.self_attn.k_proj.weight.data.shape == xmod_layer.self_attn.q_proj.weight.data.shape == xmod_layer.self_attn.v_proj.weight.data.shape == torch.Size((config.hidden_size, config.hidden_size) ) ): raise AssertionError('''Dimensions of self-attention weights do not match.''' ) _a = xmod_layer.self_attn.q_proj.weight _a = xmod_layer.self_attn.q_proj.bias _a = xmod_layer.self_attn.k_proj.weight _a = xmod_layer.self_attn.k_proj.bias _a = xmod_layer.self_attn.v_proj.weight _a = xmod_layer.self_attn.v_proj.bias # self-attention output _a = layer.attention.output if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape: raise AssertionError('''Dimensions of self-attention output weights do not match.''' ) _a = xmod_layer.self_attn.out_proj.weight _a = xmod_layer.self_attn.out_proj.bias _a = xmod_layer.self_attn_layer_norm.weight _a = xmod_layer.self_attn_layer_norm.bias # intermediate _a = layer.intermediate if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape: raise AssertionError('''Dimensions of intermediate weights do not match.''' ) _a = xmod_layer.fca.weight _a = xmod_layer.fca.bias # output _a = layer.output if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape: raise AssertionError('''Dimensions of feed-forward weights do not match.''' ) _a = xmod_layer.fca.weight _a = xmod_layer.fca.bias _a = xmod_layer.final_layer_norm.weight _a = xmod_layer.final_layer_norm.bias if bert_output.adapter_layer_norm is not None: _a = xmod_layer.adapter_layer_norm.weight _a = xmod_layer.adapter_layer_norm.bias if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ): raise AssertionError('''Lists of language adapters do not match.''' ) for lang_code, adapter in xmod_layer.adapter_modules.items(): _a = bert_output.adapter_modules[lang_code] _a = xmod_layer.adapter_modules[lang_code] _a = from_adapter.fca.weight _a = from_adapter.fca.bias _a = from_adapter.fca.weight _a = from_adapter.fca.bias # end of layer if xmod_sent_encoder.layer_norm is not None: _a = xmod_sent_encoder.layer_norm.weight _a = xmod_sent_encoder.layer_norm.bias if classification_head: _a = xmod.model.classification_heads['''mnli'''].dense.weight _a = xmod.model.classification_heads['''mnli'''].dense.bias _a = xmod.model.classification_heads['''mnli'''].out_proj.weight _a = xmod.model.classification_heads['''mnli'''].out_proj.bias else: # LM Head _a = xmod.model.encoder.lm_head.dense.weight _a = xmod.model.encoder.lm_head.dense.bias _a = xmod.model.encoder.lm_head.layer_norm.weight _a = xmod.model.encoder.lm_head.layer_norm.bias _a = xmod.model.encoder.lm_head.weight _a = xmod.model.encoder.lm_head.bias # Let's check that we get the same results. _a = xmod.encode(__snake_case ).unsqueeze(0 ) # batch of size 1 model.roberta.set_default_language(__snake_case ) _a = model(__snake_case )[0] if classification_head: _a = xmod.model.classification_heads['''mnli'''](xmod.extract_features(__snake_case ) ) else: _a = xmod.model(__snake_case , lang_id=[SAMPLE_LANGUAGE] )[0] print(our_output.shape , their_output.shape ) _a = torch.max(torch.abs(our_output - their_output ) ).item() print(f'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7 _a = torch.allclose(__snake_case , __snake_case , atol=1e-3 ) print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''' ) if not success: raise Exception('''Something went wRoNg''' ) Path(__snake_case ).mkdir(parents=__snake_case , exist_ok=__snake_case ) print(f'Saving model to {pytorch_dump_folder_path}' ) model.save_pretrained(__snake_case ) if __name__ == "__main__": _snake_case : int = argparse.ArgumentParser() # Required parameters parser.add_argument( '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.' ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.' ) parser.add_argument( '--classification_head', action='store_true', help='Whether to convert a final classification head.' ) _snake_case : Optional[Any] = parser.parse_args() convert_xmod_checkpoint_to_pytorch( args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head )
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"""simple docstring""" from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join _UpperCamelCase = '''__test_patch_submodule_mock__''' with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os, _PatchedModuleObj ) assert isinstance(_test_patching.os.path, _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path, _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os, _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path, _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" assert _test_patching.open is open _UpperCamelCase = '''__test_patch_submodule_builtin_mock__''' # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching, '''open''', __snake_case ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def lowerCamelCase__ ( ) -> Union[str, Any]: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_mock__''' with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ): pass def lowerCamelCase__ ( ) -> Dict: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__''' # _test_patching doesn't have "len" in its globals assert getattr(_test_patching, '''len''', __snake_case ) is None with patch_submodule(_test_patching, '''len''', __snake_case ): assert _test_patching.len is mock assert _test_patching.len is len def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__''' _UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def lowerCamelCase__ ( ) -> Optional[int]: """simple docstring""" from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join _UpperCamelCase = '''__test_patch_submodule_successive_join__''' _UpperCamelCase = '''__test_patch_submodule_successive_dirname__''' _UpperCamelCase = '''__test_patch_submodule_successive_rename__''' assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def lowerCamelCase__ ( ) -> str: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__''' with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ): pass with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ): pass
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'''simple docstring''' def __a ( A__ = 6008_5147_5143 ) -> int: try: lowerCAmelCase = int(__snake_case ) except (TypeError, ValueError): raise TypeError("Parameter n must be int or castable to int." ) if n <= 0: raise ValueError("Parameter n must be greater than or equal to one." ) lowerCAmelCase = 2 lowerCAmelCase = 0 if n == 2: return 2 while n > 2: while n % i != 0: i += 1 lowerCAmelCase = i while n % i == 0: lowerCAmelCase = n // i i += 1 return int(__snake_case ) if __name__ == "__main__": print(f"{solution() = }")
649
"""simple docstring""" import argparse import json from collections import OrderedDict from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor from transformers.utils import logging logging.set_verbosity_info() _a = logging.get_logger(__name__) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = original_name.split('''.''' )[0] _UpperCamelCase = key.split('''.''' ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] ) _UpperCamelCase = orig_block_num - offset _UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' ) return key def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase = OrderedDict() _UpperCamelCase , _UpperCamelCase = 0, 0 for key, value in state_dict.items(): if key.startswith('''network''' ): _UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' ) if "proj" in key: # Works for the first embedding as well as the internal embedding layers if key.endswith('''bias''' ) and "patch_embed" not in key: patch_emb_offset += 1 _UpperCamelCase = key[: key.find('''proj''' )] _UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' ) _UpperCamelCase = key.replace('''proj''', '''projection''' ) if key.endswith('''bias''' ): total_embed_found += 1 if "patch_embeddings" in key: _UpperCamelCase = '''poolformer.encoder.''' + key if "mlp.fc1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' ) if "mlp.fc2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' ) if "norm1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' ) if "norm2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' ) if "layer_scale_1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' ) if "layer_scale_2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' ) if "head" in key: _UpperCamelCase = key.replace('''head''', '''classifier''' ) _UpperCamelCase = value return new_state_dict def lowerCamelCase__ ( ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg''' _UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw ) return image @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int: """simple docstring""" _UpperCamelCase = PoolFormerConfig() # set attributes based on model_name _UpperCamelCase = '''huggingface/label-files''' _UpperCamelCase = model_name[-3:] _UpperCamelCase = 10_00 _UpperCamelCase = '''imagenet-1k-id2label.json''' _UpperCamelCase = (1, 10_00) # set config attributes _UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) ) _UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()} _UpperCamelCase = idalabel _UpperCamelCase = {v: k for k, v in idalabel.items()} if size == "s12": _UpperCamelCase = [2, 2, 6, 2] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s24": _UpperCamelCase = [4, 4, 12, 4] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.9 elif size == "m36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 elif size == "m48": _UpperCamelCase = [8, 8, 24, 8] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 else: raise ValueError(F'''Size {size} not supported''' ) # load image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) # Prepare image _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values logger.info(F'''Converting model {model_name}...''' ) # load original state dict _UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) ) # rename keys _UpperCamelCase = rename_keys(__snake_case ) # create HuggingFace model and load state dict _UpperCamelCase = PoolFormerForImageClassification(__snake_case ) model.load_state_dict(__snake_case ) model.eval() # Define image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) _UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values # forward pass _UpperCamelCase = model(__snake_case ) _UpperCamelCase = outputs.logits # define expected logit slices for different models if size == "s12": _UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] ) elif size == "s24": _UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] ) elif size == "s36": _UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] ) elif size == "m36": _UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] ) elif size == "m48": _UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] ) else: raise ValueError(F'''Size {size} not supported''' ) # verify logits assert logits.shape == expected_shape assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 ) # finally, save model and image processor logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' ) Path(__snake_case ).mkdir(exist_ok=__snake_case ) model.save_pretrained(__snake_case ) print(F'''Saving image processor to {pytorch_dump_folder_path}''' ) image_processor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument( """--model_name""", default="""poolformer_s12""", type=str, help="""Name of the model you'd like to convert.""", ) parser.add_argument( """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file).""" ) parser.add_argument( """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model.""" ) _a = parser.parse_args() convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
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'''simple docstring''' import math def __UpperCamelCase( _A : Any ): '''simple docstring''' UpperCAmelCase__ : str = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 ) return exponent == int(__snake_case ) def __UpperCamelCase( _A : Union[str, Any] = 1 / 1_23_45 ): '''simple docstring''' UpperCAmelCase__ : List[Any] = 0 UpperCAmelCase__ : List[Any] = 0 UpperCAmelCase__ : Any = 3 while True: UpperCAmelCase__ : Dict = (integer**2 - 1) / 4 # if candidate is an integer, then there is a partition for k if partition_candidate == int(__snake_case ): UpperCAmelCase__ : Tuple = int(__snake_case ) total_partitions += 1 if check_partition_perfect(__snake_case ): perfect_partitions += 1 if perfect_partitions > 0: if perfect_partitions / total_partitions < max_proportion: return int(__snake_case ) integer += 1 if __name__ == "__main__": print(f"""{solution() = }""")
614
"""simple docstring""" import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils import torch_device from diffusers.utils.testing_utils import require_torchsde from .test_schedulers import SchedulerCommonTest @require_torchsde class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DPMSolverSDEScheduler,) lowercase__ = 10 def UpperCAmelCase ( self , **__a) -> int: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 11_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''noise_sampler_seed''': 0, } config.update(**__a) return config def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' for timesteps in [10, 50, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> str: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2 assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2 assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2 assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3 else: assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2 assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2 assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 else: assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging lowercase =logging.get_logger(__name__) lowercase ={ 'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json', 'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json', } class __magic_name__ ( lowerCAmelCase ): UpperCAmelCase ="markuplm" def __init__( self , snake_case=3_0_5_2_2 , snake_case=7_6_8 , snake_case=1_2 , snake_case=1_2 , snake_case=3_0_7_2 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=5_1_2 , snake_case=2 , snake_case=0.02 , snake_case=1E-1_2 , snake_case=0 , snake_case=0 , snake_case=2 , snake_case=2_5_6 , snake_case=1_0_2_4 , snake_case=2_1_6 , snake_case=1_0_0_1 , snake_case=3_2 , snake_case=5_0 , snake_case="absolute" , snake_case=True , snake_case=None , **snake_case , ) -> List[Any]: '''simple docstring''' super().__init__( pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a , ) _UpperCAmelCase : Any =vocab_size _UpperCAmelCase : Union[str, Any] =hidden_size _UpperCAmelCase : Dict =num_hidden_layers _UpperCAmelCase : int =num_attention_heads _UpperCAmelCase : List[str] =hidden_act _UpperCAmelCase : Any =intermediate_size _UpperCAmelCase : int =hidden_dropout_prob _UpperCAmelCase : List[str] =attention_probs_dropout_prob _UpperCAmelCase : Optional[Any] =max_position_embeddings _UpperCAmelCase : Optional[int] =type_vocab_size _UpperCAmelCase : List[Any] =initializer_range _UpperCAmelCase : Tuple =layer_norm_eps _UpperCAmelCase : str =position_embedding_type _UpperCAmelCase : Optional[Any] =use_cache _UpperCAmelCase : Optional[Any] =classifier_dropout # additional properties _UpperCAmelCase : Optional[int] =max_depth _UpperCAmelCase : Optional[int] =max_xpath_tag_unit_embeddings _UpperCAmelCase : Optional[int] =max_xpath_subs_unit_embeddings _UpperCAmelCase : Dict =tag_pad_id _UpperCAmelCase : List[str] =subs_pad_id _UpperCAmelCase : int =xpath_unit_hidden_size
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"""simple docstring""" from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images, ) from ...utils import TensorType, logging _a = logging.get_logger(__name__) class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['pixel_values'] def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None: '''simple docstring''' super().__init__(**__a) _UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a) _UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''') _UpperCamelCase = do_resize _UpperCamelCase = do_rescale _UpperCamelCase = do_normalize _UpperCamelCase = do_center_crop _UpperCamelCase = crop_size _UpperCamelCase = size _UpperCamelCase = resample _UpperCamelCase = rescale_factor _UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN _UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "shortest_edge" in size: _UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a) # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"]) elif "height" in size and "width" in size: _UpperCamelCase = (size['''height'''], size['''width''']) else: raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''') return resize(__a , size=__a , resample=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "height" not in size or "width" not in size: raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''') return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray: '''simple docstring''' return rescale(__a , scale=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' return normalize(__a , mean=__a , std=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature: '''simple docstring''' _UpperCamelCase = do_resize if do_resize is not None else self.do_resize _UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale _UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize _UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop _UpperCamelCase = crop_size if crop_size is not None else self.crop_size _UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a) _UpperCamelCase = resample if resample is not None else self.resample _UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor _UpperCamelCase = image_mean if image_mean is not None else self.image_mean _UpperCamelCase = image_std if image_std is not None else self.image_std _UpperCamelCase = size if size is not None else self.size _UpperCamelCase = get_size_dict(__a) if not is_batched(__a): _UpperCamelCase = [images] if not valid_images(__a): raise ValueError( '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, ''' '''torch.Tensor, tf.Tensor or jax.ndarray.''') if do_resize and size is None: raise ValueError('''Size must be specified if do_resize is True.''') if do_center_crop and crop_size is None: raise ValueError('''Crop size must be specified if do_center_crop is True.''') if do_rescale and rescale_factor is None: raise ValueError('''Rescale factor must be specified if do_rescale is True.''') # All transformations expect numpy arrays. _UpperCamelCase = [to_numpy_array(__a) for image in images] if do_resize: _UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images] if do_center_crop: _UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images] if do_rescale: _UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images] if do_normalize: _UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images] _UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images] _UpperCamelCase = {'''pixel_values''': images} return BatchFeature(data=__a , tensor_type=__a)
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from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices _lowerCamelCase : Union[str, Any] = logging.get_logger(__name__) _lowerCamelCase : Dict = { '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''', # See all Nat models at https://huggingface.co/models?filter=nat } class lowerCamelCase (__lowerCamelCase , __lowerCamelCase ): """simple docstring""" UpperCAmelCase_ = "nat" UpperCAmelCase_ = { "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers", } def __init__( self : str, _UpperCAmelCase : Union[str, Any]=4, _UpperCAmelCase : List[str]=3, _UpperCAmelCase : Dict=6_4, _UpperCAmelCase : Union[str, Any]=[3, 4, 6, 5], _UpperCAmelCase : List[str]=[2, 4, 8, 1_6], _UpperCAmelCase : Dict=7, _UpperCAmelCase : int=3.0, _UpperCAmelCase : str=True, _UpperCAmelCase : List[Any]=0.0, _UpperCAmelCase : Optional[int]=0.0, _UpperCAmelCase : Optional[int]=0.1, _UpperCAmelCase : Union[str, Any]="gelu", _UpperCAmelCase : str=0.02, _UpperCAmelCase : Any=1E-5, _UpperCAmelCase : int=0.0, _UpperCAmelCase : Optional[Any]=None, _UpperCAmelCase : Union[str, Any]=None, **_UpperCAmelCase : List[str], ) -> Tuple: """simple docstring""" super().__init__(**__a ) SCREAMING_SNAKE_CASE__ : List[Any] = patch_size SCREAMING_SNAKE_CASE__ : Dict = num_channels SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim SCREAMING_SNAKE_CASE__ : List[Any] = depths SCREAMING_SNAKE_CASE__ : Optional[int] = len(__a ) SCREAMING_SNAKE_CASE__ : Tuple = num_heads SCREAMING_SNAKE_CASE__ : Any = kernel_size SCREAMING_SNAKE_CASE__ : str = mlp_ratio SCREAMING_SNAKE_CASE__ : List[str] = qkv_bias SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Union[str, Any] = drop_path_rate SCREAMING_SNAKE_CASE__ : str = hidden_act SCREAMING_SNAKE_CASE__ : List[str] = layer_norm_eps SCREAMING_SNAKE_CASE__ : List[str] = initializer_range # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel # this indicates the channel dimension after the last stage of the model SCREAMING_SNAKE_CASE__ : Any = int(embed_dim * 2 ** (len(__a ) - 1) ) SCREAMING_SNAKE_CASE__ : Dict = layer_scale_init_value SCREAMING_SNAKE_CASE__ : Optional[Any] = ["stem"] + [F'''stage{idx}''' for idx in range(1, len(__a ) + 1 )] SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Optional[int] = get_aligned_output_features_output_indices( out_features=__a, out_indices=__a, stage_names=self.stage_names )
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"""simple docstring""" # Imports import numpy as np class _UpperCAmelCase: def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' if red is not None: _UpperCamelCase = red if green is not None: _UpperCamelCase = green if blue is not None: _UpperCamelCase = blue if red_edge is not None: _UpperCamelCase = red_edge if nir is not None: _UpperCamelCase = nir return True def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) _UpperCamelCase = { '''ARVI2''': self.arvaa, '''CCCI''': self.ccci, '''CVI''': self.cvi, '''GLI''': self.gli, '''NDVI''': self.ndvi, '''BNDVI''': self.bndvi, '''redEdgeNDVI''': self.red_edge_ndvi, '''GNDVI''': self.gndvi, '''GBNDVI''': self.gbndvi, '''GRNDVI''': self.grndvi, '''RBNDVI''': self.rbndvi, '''PNDVI''': self.pndvi, '''ATSAVI''': self.atsavi, '''BWDRVI''': self.bwdrvi, '''CIgreen''': self.ci_green, '''CIrededge''': self.ci_rededge, '''CI''': self.ci, '''CTVI''': self.ctvi, '''GDVI''': self.gdvi, '''EVI''': self.evi, '''GEMI''': self.gemi, '''GOSAVI''': self.gosavi, '''GSAVI''': self.gsavi, '''Hue''': self.hue, '''IVI''': self.ivi, '''IPVI''': self.ipvi, '''I''': self.i, '''RVI''': self.rvi, '''MRVI''': self.mrvi, '''MSAVI''': self.m_savi, '''NormG''': self.norm_g, '''NormNIR''': self.norm_nir, '''NormR''': self.norm_r, '''NGRDI''': self.ngrdi, '''RI''': self.ri, '''S''': self.s, '''IF''': self._if, '''DVI''': self.dvi, '''TVI''': self.tvi, '''NDRE''': self.ndre, } try: return funcs[index]() except KeyError: print('''Index not in the list!''') return False def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red))) def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / ( (self.nir - self.red) / (self.nir + self.red) ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir * (self.red / (self.green**2)) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.green - self.red - self.blue) / ( 2 * self.green + self.red + self.blue ) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - self.red) / (self.nir + self.red) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (self.nir - self.blue) / (self.nir + self.blue) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.redEdge - self.red) / (self.redEdge + self.red) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir - (self.green + self.blue)) / ( self.nir + (self.green + self.blue) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.nir - (self.green + self.red)) / ( self.nir + (self.green + self.red) ) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red)) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - (self.green + self.red + self.blue)) / ( self.nir + (self.green + self.red + self.blue) ) def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]: '''simple docstring''' return a * ( (self.nir - a * self.red - b) / (a * self.nir + self.red - a * b + x * (1 + a**2)) ) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir / self.green) - 1 def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir / self.redEdge) - 1 def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.red - self.blue) / self.red def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.ndvi() return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2)) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir - self.green def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return 2.5 * ( (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / ( self.nir + self.red + 0.5 ) return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red) def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green + y) def UpperCAmelCase ( self , __a=0.5) -> Dict: '''simple docstring''' return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return np.arctan( ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)) def UpperCAmelCase ( self , __a=None , __a=None) -> Any: '''simple docstring''' return (self.nir - b) / (a * self.red) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.red + self.green + self.blue) / 30.5 def UpperCAmelCase ( self) -> Any: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.rvi() - 1) / (self.rvi() + 1) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return ( (2 * self.nir + 1) - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2) ) / 2 def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.green / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> str: '''simple docstring''' return self.nir / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.red / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.green - self.red) / (self.green + self.red) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (self.red - self.green) / (self.red + self.green) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)]) _UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)]) return (max_value - min_value) / max_value def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.red - self.green - self.blue) / (self.green - self.blue) def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Any: '''simple docstring''' return (self.ndvi() + 0.5) ** (1 / 2) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.nir - self.redEdge) / (self.nir + self.redEdge)
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import argparse import os import re import packaging.version __lowerCamelCase : Dict = "examples/" __lowerCamelCase : int = { "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"), "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"), "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","), "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"), } __lowerCamelCase : Dict = { "init": "src/diffusers/__init__.py", "setup": "setup.py", } __lowerCamelCase : int = "README.md" def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]: with open(__snake_case , "r" , encoding="utf-8" , newline="\n" ) as f: UpperCAmelCase = f.read() UpperCAmelCase , UpperCAmelCase = REPLACE_PATTERNS[pattern] UpperCAmelCase = replace.replace("VERSION" , __snake_case ) UpperCAmelCase = re_pattern.sub(__snake_case , __snake_case ) with open(__snake_case , "w" , encoding="utf-8" , newline="\n" ) as f: f.write(__snake_case ) def lowerCamelCase_(lowerCamelCase_ ) -> str: for folder, directories, fnames in os.walk(__snake_case ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove("research_projects" ) if "legacy" in directories: directories.remove("legacy" ) for fname in fnames: if fname.endswith(".py" ): update_version_in_file(os.path.join(__snake_case , __snake_case ) , __snake_case , pattern="examples" ) def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_=False ) -> Dict: for pattern, fname in REPLACE_FILES.items(): update_version_in_file(__snake_case , __snake_case , __snake_case ) if not patch: update_version_in_examples(__snake_case ) def lowerCamelCase_() -> Union[str, Any]: UpperCAmelCase = "🤗 Transformers currently provides the following architectures" UpperCAmelCase = "1. Want to contribute a new model?" with open(__snake_case , "r" , encoding="utf-8" , newline="\n" ) as f: UpperCAmelCase = f.readlines() # Find the start of the list. UpperCAmelCase = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 UpperCAmelCase = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith("1." ): UpperCAmelCase = lines[index].replace( "https://huggingface.co/docs/diffusers/main/model_doc" , "https://huggingface.co/docs/diffusers/model_doc" , ) index += 1 with open(__snake_case , "w" , encoding="utf-8" , newline="\n" ) as f: f.writelines(__snake_case ) def lowerCamelCase_() -> str: with open(REPLACE_FILES["init"] , "r" ) as f: UpperCAmelCase = f.read() UpperCAmelCase = REPLACE_PATTERNS["init"][0].search(__snake_case ).groups()[0] return packaging.version.parse(__snake_case ) def lowerCamelCase_(lowerCamelCase_=False ) -> Optional[Any]: UpperCAmelCase = get_version() if patch and default_version.is_devrelease: raise ValueError("Can\'t create a patch version from the dev branch, checkout a released version!" ) if default_version.is_devrelease: UpperCAmelCase = default_version.base_version elif patch: UpperCAmelCase = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}' else: UpperCAmelCase = F'{default_version.major}.{default_version.minor + 1}.0' # Now let's ask nicely if that's the right one. UpperCAmelCase = input(F'Which version are you releasing? [{default_version}]' ) if len(__snake_case ) == 0: UpperCAmelCase = default_version print(F'Updating version to {version}.' ) global_version_update(__snake_case , patch=__snake_case ) def lowerCamelCase_() -> Any: UpperCAmelCase = get_version() UpperCAmelCase = F'{current_version.major}.{current_version.minor + 1}.0.dev0' UpperCAmelCase = current_version.base_version # Check with the user we got that right. UpperCAmelCase = input(F'Which version are we developing now? [{dev_version}]' ) if len(__snake_case ) == 0: UpperCAmelCase = dev_version print(F'Updating version to {version}.' ) global_version_update(__snake_case ) # print("Cleaning main README, don't forget to run `make fix-copies`.") # clean_main_ref_in_model_list() if __name__ == "__main__": __lowerCamelCase : str = argparse.ArgumentParser() parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") __lowerCamelCase : int = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print("Nothing to do after a patch :-)") else: post_release_work()
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"""simple docstring""" import inspect import unittest from transformers import ViTHybridConfig from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = patch_size _UpperCamelCase = num_channels _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = scope _UpperCamelCase = backbone_featmap_shape # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) # the number of patches is based on the feature map of the backbone, which by default uses an output stride # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size _UpperCamelCase = (self.image_size // 32) ** 2 _UpperCamelCase = num_patches + 1 def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = { '''global_padding''': '''same''', '''layer_type''': '''bottleneck''', '''depths''': [3, 4, 9], '''out_features''': ['''stage1''', '''stage2''', '''stage3'''], '''embedding_dynamic_padding''': True, '''hidden_sizes''': [4, 8, 16, 32], '''num_groups''': 2, } return ViTHybridConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModel(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.type_sequence_label_size _UpperCamelCase = ViTHybridForImageClassification(__a) model.to(__a) model.eval() _UpperCamelCase = model(__a , labels=__a) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else () lowercase__ = ( {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification} if is_torch_available() else {} ) lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Any: '''simple docstring''' self.config_tester.run_common_tests() @unittest.skip(reason='''ViT does not use inputs_embeds''') def UpperCAmelCase ( self) -> Dict: '''simple docstring''' pass def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) self.assertIsInstance(model.get_input_embeddings() , (nn.Module)) _UpperCamelCase = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__a , nn.Linear)) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "ViTHybridPatchEmbeddings": _UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if name in backbone_params: continue self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = ViTHybridModel.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class _UpperCAmelCase( unittest.TestCase ): @cached_property def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return ( ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to( __a) _UpperCamelCase = self.default_image_processor _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a) # forward pass with torch.no_grad(): _UpperCamelCase = model(**__a) # verify the logits _UpperCamelCase = torch.Size((1, 10_00)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4)) @slow @require_accelerate def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''') _UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''') _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''') _UpperCamelCase = model(**__a) _UpperCamelCase = outputs.logits # model predicts one of the 1000 ImageNet classes _UpperCamelCase = logits.argmax(-1).item() self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
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'''simple docstring''' from functools import lru_cache @lru_cache def __UpperCamelCase ( UpperCAmelCase ): if num < 0: raise ValueError('''Number should not be negative.''' ) return 1 if num in (0, 1) else num * factorial(num - 1 ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from math import acos, sin from typing import List, Tuple, Union import numpy as np import torch from PIL import Image from ...models import AutoencoderKL, UNetaDConditionModel from ...schedulers import DDIMScheduler, DDPMScheduler from ...utils import randn_tensor from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput from .mel import Mel class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['vqvae'] def __init__( self , __a , __a , __a , __a , ) -> List[str]: '''simple docstring''' super().__init__() self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' return 50 if isinstance(self.scheduler , __a) else 10_00 @torch.no_grad() def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[ Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]], ]: '''simple docstring''' _UpperCamelCase = steps or self.get_default_steps() self.scheduler.set_timesteps(__a) _UpperCamelCase = step_generator or generator # For backwards compatibility if type(self.unet.config.sample_size) == int: _UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size) if noise is None: _UpperCamelCase = randn_tensor( ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1], ) , generator=__a , device=self.device , ) _UpperCamelCase = noise _UpperCamelCase = None if audio_file is not None or raw_audio is not None: self.mel.load_audio(__a , __a) _UpperCamelCase = self.mel.audio_slice_to_image(__a) _UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape( (input_image.height, input_image.width)) _UpperCamelCase = (input_image / 2_55) * 2 - 1 _UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device) if self.vqvae is not None: _UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample( generator=__a)[0] _UpperCamelCase = self.vqvae.config.scaling_factor * input_images if start_step > 0: _UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1]) _UpperCamelCase = ( self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length ) _UpperCamelCase = int(mask_start_secs * pixels_per_second) _UpperCamelCase = int(mask_end_secs * pixels_per_second) _UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:])) for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])): if isinstance(self.unet , __a): _UpperCamelCase = self.unet(__a , __a , __a)['''sample'''] else: _UpperCamelCase = self.unet(__a , __a)['''sample'''] if isinstance(self.scheduler , __a): _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample'''] else: _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample'''] if mask is not None: if mask_start > 0: _UpperCamelCase = mask[:, step, :, :mask_start] if mask_end > 0: _UpperCamelCase = mask[:, step, :, -mask_end:] if self.vqvae is not None: # 0.18215 was scaling factor used in training to ensure unit variance _UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images _UpperCamelCase = self.vqvae.decode(__a)['''sample'''] _UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1) _UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy() _UpperCamelCase = (images * 2_55).round().astype('''uint8''') _UpperCamelCase = list( (Image.fromarray(_[:, :, 0]) for _ in images) if images.shape[3] == 1 else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images)) _UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images] if not return_dict: return images, (self.mel.get_sample_rate(), audios) return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a)) @torch.no_grad() def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray: '''simple docstring''' assert isinstance(self.scheduler , __a) self.scheduler.set_timesteps(__a) _UpperCamelCase = np.array( [np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images]) _UpperCamelCase = (sample / 2_55) * 2 - 1 _UpperCamelCase = torch.Tensor(__a).to(self.device) for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))): _UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps _UpperCamelCase = self.scheduler.alphas_cumprod[t] _UpperCamelCase = ( self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod ) _UpperCamelCase = 1 - alpha_prod_t _UpperCamelCase = self.unet(__a , __a)['''sample'''] _UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output _UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5) _UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output return sample @staticmethod def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor: '''simple docstring''' _UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a)) return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
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def lowercase_( SCREAMING_SNAKE_CASE_ ): '''simple docstring''' if a < 0: raise ValueError("Input value must be a positive integer" ) elif isinstance(__snake_case , __snake_case ): raise TypeError("Input value must be a \'int\' type" ) return bin(__snake_case ).count("1" ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import copy from collections import OrderedDict from typing import Dict, Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING _a = logging.get_logger(__name__) _a = { """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""", # See all DETR models at https://huggingface.co/models?filter=detr } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'detr' lowercase__ = ['past_key_values'] lowercase__ = { 'hidden_size': 'd_model', 'num_attention_heads': 'encoder_attention_heads', } def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int: '''simple docstring''' if backbone_config is not None and use_timm_backbone: raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''') if not use_timm_backbone: if backbone_config is None: logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''') _UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']) elif isinstance(__a , __a): _UpperCamelCase = backbone_config.get('''model_type''') _UpperCamelCase = CONFIG_MAPPING[backbone_model_type] _UpperCamelCase = config_class.from_dict(__a) # set timm attributes to None _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None _UpperCamelCase = use_timm_backbone _UpperCamelCase = backbone_config _UpperCamelCase = num_channels _UpperCamelCase = num_queries _UpperCamelCase = d_model _UpperCamelCase = encoder_ffn_dim _UpperCamelCase = encoder_layers _UpperCamelCase = encoder_attention_heads _UpperCamelCase = decoder_ffn_dim _UpperCamelCase = decoder_layers _UpperCamelCase = decoder_attention_heads _UpperCamelCase = dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = activation_function _UpperCamelCase = init_std _UpperCamelCase = init_xavier_std _UpperCamelCase = encoder_layerdrop _UpperCamelCase = decoder_layerdrop _UpperCamelCase = encoder_layers _UpperCamelCase = auxiliary_loss _UpperCamelCase = position_embedding_type _UpperCamelCase = backbone _UpperCamelCase = use_pretrained_backbone _UpperCamelCase = dilation # Hungarian matcher _UpperCamelCase = class_cost _UpperCamelCase = bbox_cost _UpperCamelCase = giou_cost # Loss coefficients _UpperCamelCase = mask_loss_coefficient _UpperCamelCase = dice_loss_coefficient _UpperCamelCase = bbox_loss_coefficient _UpperCamelCase = giou_loss_coefficient _UpperCamelCase = eos_coefficient super().__init__(is_encoder_decoder=__a , **__a) @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.encoder_attention_heads @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.d_model @classmethod def UpperCAmelCase ( cls , __a , **__a) -> int: '''simple docstring''' return cls(backbone_config=__a , **__a) def UpperCAmelCase ( self) -> Dict[str, any]: '''simple docstring''' _UpperCamelCase = copy.deepcopy(self.__dict__) if output["backbone_config"] is not None: _UpperCamelCase = self.backbone_config.to_dict() _UpperCamelCase = self.__class__.model_type return output class _UpperCAmelCase( lowerCamelCase ): lowercase__ = version.parse('1.11' ) @property def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' return OrderedDict( [ ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}), ('''pixel_mask''', {0: '''batch'''}), ]) @property def UpperCAmelCase ( self) -> float: '''simple docstring''' return 1e-5 @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return 12
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from __future__ import annotations class lowerCamelCase__ : '''simple docstring''' def __init__(self ,__lowerCamelCase ) -> None: """simple docstring""" lowerCAmelCase__ : List[str] = data lowerCAmelCase__ : int = None lowerCAmelCase__ : Optional[Any] = None def lowerCAmelCase__ ( lowerCamelCase_ : str): # In Order traversal of the tree '''simple docstring''' if tree: display(tree.left) print(tree.data) display(tree.right) def lowerCAmelCase__ ( lowerCamelCase_ : List[str]): '''simple docstring''' return 1 + max(depth_of_tree(tree.left) ,depth_of_tree(tree.right)) if tree else 0 def lowerCAmelCase__ ( lowerCamelCase_ : Optional[int]): '''simple docstring''' if not tree: return True if tree.left and tree.right: return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right) else: return not tree.left and not tree.right def lowerCAmelCase__ ( ): # Main function for testing. '''simple docstring''' lowerCAmelCase__ : Optional[Any] = Node(1) lowerCAmelCase__ : List[Any] = Node(2) lowerCAmelCase__ : Union[str, Any] = Node(3) lowerCAmelCase__ : Tuple = Node(4) lowerCAmelCase__ : Union[str, Any] = Node(5) lowerCAmelCase__ : Optional[int] = Node(6) lowerCAmelCase__ : Tuple = Node(7) lowerCAmelCase__ : Optional[Any] = Node(8) lowerCAmelCase__ : Optional[Any] = Node(9) print(is_full_binary_tree(__snake_case)) print(depth_of_tree(__snake_case)) print('''Tree is: ''') display(__snake_case) if __name__ == "__main__": main()
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"""simple docstring""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging _a = logging.get_logger(__name__) _a = { """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""", # See all WavLM models at https://huggingface.co/models?filter=wavlm } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'wavlm' def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]: '''simple docstring''' super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a) _UpperCamelCase = hidden_size _UpperCamelCase = feat_extract_norm _UpperCamelCase = feat_extract_activation _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = conv_bias _UpperCamelCase = num_buckets _UpperCamelCase = max_bucket_distance _UpperCamelCase = num_conv_pos_embeddings _UpperCamelCase = num_conv_pos_embedding_groups _UpperCamelCase = len(self.conv_dim) _UpperCamelCase = num_hidden_layers _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = num_attention_heads _UpperCamelCase = hidden_dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = feat_proj_dropout _UpperCamelCase = final_dropout _UpperCamelCase = layerdrop _UpperCamelCase = layer_norm_eps _UpperCamelCase = initializer_range _UpperCamelCase = num_ctc_classes _UpperCamelCase = vocab_size _UpperCamelCase = do_stable_layer_norm _UpperCamelCase = use_weighted_layer_sum _UpperCamelCase = classifier_proj_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==''' ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =''' F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,''' F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''') # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _UpperCamelCase = apply_spec_augment _UpperCamelCase = mask_time_prob _UpperCamelCase = mask_time_length _UpperCamelCase = mask_time_min_masks _UpperCamelCase = mask_feature_prob _UpperCamelCase = mask_feature_length # parameters for pretraining with codevector quantized representations _UpperCamelCase = num_codevectors_per_group _UpperCamelCase = num_codevector_groups _UpperCamelCase = contrastive_logits_temperature _UpperCamelCase = num_negatives _UpperCamelCase = codevector_dim _UpperCamelCase = proj_codevector_dim _UpperCamelCase = diversity_loss_weight # ctc loss _UpperCamelCase = ctc_loss_reduction _UpperCamelCase = ctc_zero_infinity # adapter _UpperCamelCase = add_adapter _UpperCamelCase = adapter_kernel_size _UpperCamelCase = adapter_stride _UpperCamelCase = num_adapter_layers _UpperCamelCase = output_hidden_size or hidden_size # SequenceClassification-specific parameter. Feel free to ignore for other classes. _UpperCamelCase = classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = xvector_output_dim @property def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return functools.reduce(operator.mul , self.conv_stride , 1)
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'''simple docstring''' import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def UpperCAmelCase_ (__a : Any ): """simple docstring""" if ( (cp >= 0x4E_00 and cp <= 0x9F_FF) or (cp >= 0x34_00 and cp <= 0x4D_BF) # or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF) # or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F) # or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F) # or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF) # or (cp >= 0xF9_00 and cp <= 0xFA_FF) or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F) # ): # return True return False def UpperCAmelCase_ (__a : Union[str, Any] ): """simple docstring""" for char in word: _a : Optional[int] = ord(__snake_case ) if not _is_chinese_char(__snake_case ): return 0 return 1 def UpperCAmelCase_ (__a : Dict ): """simple docstring""" _a : Optional[int] = set() for token in tokens: _a : Any = len(__snake_case ) > 1 and is_chinese(__snake_case ) if chinese_word: word_set.add(__snake_case ) _a : Dict = list(__snake_case ) return word_list def UpperCAmelCase_ (__a : Union[str, Any] , __a : Any ): """simple docstring""" if not chinese_word_set: return bert_tokens _a : str = max([len(__snake_case ) for w in chinese_word_set] ) _a : str = bert_tokens _a, _a : Dict = 0, len(__snake_case ) while start < end: _a : str = True if is_chinese(bert_word[start] ): _a : Optional[Any] = min(end - start , __snake_case ) for i in range(__snake_case , 1 , -1 ): _a : List[Any] = ''.join(bert_word[start : start + i] ) if whole_word in chinese_word_set: for j in range(start + 1 , start + i ): _a : List[str] = '##' + bert_word[j] _a : Dict = start + i _a : Union[str, Any] = False break if single_word: start += 1 return bert_word def UpperCAmelCase_ (__a : Optional[int] , __a : Union[str, Any] , __a : str ): """simple docstring""" _a : Any = [] for i in range(0 , len(__snake_case ) , 1_0_0 ): _a : Optional[Any] = ltp_tokenizer.pipeline(lines[i : i + 1_0_0] , tasks=['cws'] ).cws _a : str = [get_chinese_word(__snake_case ) for r in res] ltp_res.extend(__snake_case ) assert len(__snake_case ) == len(__snake_case ) _a : Union[str, Any] = [] for i in range(0 , len(__snake_case ) , 1_0_0 ): _a : Dict = bert_tokenizer(lines[i : i + 1_0_0] , add_special_tokens=__snake_case , truncation=__snake_case , max_length=5_1_2 ) bert_res.extend(res['input_ids'] ) assert len(__snake_case ) == len(__snake_case ) _a : Union[str, Any] = [] for input_ids, chinese_word in zip(__snake_case , __snake_case ): _a : int = [] for id in input_ids: _a : List[str] = bert_tokenizer._convert_id_to_token(__snake_case ) input_tokens.append(__snake_case ) _a : List[str] = add_sub_symbol(__snake_case , __snake_case ) _a : Union[str, Any] = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(__snake_case ): if token[:2] == "##": _a : Optional[int] = token[2:] # save chinese tokens' pos if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ): ref_id.append(__snake_case ) ref_ids.append(__snake_case ) assert len(__snake_case ) == len(__snake_case ) return ref_ids def UpperCAmelCase_ (__a : int ): """simple docstring""" with open(args.file_name , 'r' , encoding='utf-8' ) as f: _a : int = f.readlines() _a : Dict = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029' _a : Any = LTP(args.ltp ) # faster in GPU device _a : str = BertTokenizer.from_pretrained(args.bert ) _a : Union[str, Any] = prepare_ref(__snake_case , __snake_case , __snake_case ) with open(args.save_path , 'w' , encoding='utf-8' ) as f: _a : int = [json.dumps(__snake_case ) + '\n' for ref in ref_ids] f.writelines(__snake_case ) if __name__ == "__main__": __lowerCAmelCase = argparse.ArgumentParser(description="""prepare_chinese_ref""") parser.add_argument( """--file_name""", required=False, type=str, default="""./resources/chinese-demo.txt""", help="""file need process, same as training data in lm""", ) parser.add_argument( """--ltp""", required=False, type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path""", ) parser.add_argument( """--bert""", required=False, type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""", ) parser.add_argument( """--save_path""", required=False, type=str, default="""./resources/ref.txt""", help="""path to save res""", ) __lowerCAmelCase = parser.parse_args() main(args)
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"""simple docstring""" import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer _a = """bart""" _a = True @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Dict: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' ) _UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' ) _UpperCamelCase = qar_model.eval() else: _UpperCamelCase , _UpperCamelCase = (None, None) if MODEL_TYPE == "bart": _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' ) _UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' ) _UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' ) sas_model.load_state_dict(save_dict['''model'''] ) _UpperCamelCase = sas_model.eval() else: _UpperCamelCase , _UpperCamelCase = make_qa_sas_model( model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' ) return (qar_tokenizer, qar_model, sas_tokenizer, sas_model) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = faiss.StandardGpuResources() _UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train'''] _UpperCamelCase = np.memmap( '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) _UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case ) wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU else: _UpperCamelCase , _UpperCamelCase = (None, None) _UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] ) return (wikiaab_passages, wikiaab_gpu_index_flat, es_client) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' ) _UpperCamelCase = elia['''train_eli5'''] _UpperCamelCase = np.memmap( '''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) eli5_train_q_index.add(__snake_case ) return (elia_train, eli5_train_q_index) _a , _a , _a = load_indexes() _a , _a , _a , _a = load_models() _a , _a = load_train_data() def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]: """simple docstring""" _UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case ) _UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case ) _UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]] return nn_examples def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]: """simple docstring""" if source == "none": _UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), []) else: if method == "dense": _UpperCamelCase , _UpperCamelCase = query_qa_dense_index( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) else: _UpperCamelCase , _UpperCamelCase = query_es_index( __snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, ) _UpperCamelCase = [ (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst ] _UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case ) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda __snake_case : None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None), } ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict: """simple docstring""" with torch.no_grad(): _UpperCamelCase = qa_sas_generate( __snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0] return (answer, support_list) st.title("""Long Form Question Answering with ELI5""") # Start sidebar _a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>""" _a = """ <html> <head> <style> .img-container { padding-left: 90px; padding-right: 90px; padding-top: 50px; padding-bottom: 50px; background-color: #f0f3f9; } </style> </head> <body> <span class=\"img-container\"> <!-- Inline parent element --> %s </span> </body> </html> """ % ( header_html, ) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia _a = """ This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html). First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset, a pre-processed fixed snapshot of Wikipedia. """ st.sidebar.markdown(description, unsafe_allow_html=True) _a = [ """Answer the question""", """View the retrieved document only""", """View the most similar ELI5 question and answer""", """Show me everything, please!""", ] _a = st.sidebar.checkbox("""Demo options""") if demo_options: _a = st.sidebar.selectbox( """""", action_list, index=3, ) _a = action_list.index(action_st) _a = st.sidebar.selectbox( """""", ["""Show full text of passages""", """Show passage section titles"""], index=0, ) _a = show_type == """Show full text of passages""" else: _a = 3 _a = True _a = st.sidebar.checkbox("""Retrieval options""") if retrieval_options: _a = """ ### Information retriever options The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs. The answer is then generated by sequence to sequence model which takes the question and retrieved document as input. """ st.sidebar.markdown(retriever_info) _a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""]) _a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""]) else: _a = """wiki40b""" _a = """dense""" _a = """beam""" _a = 2 _a = 64 _a = 256 _a = None _a = None _a = st.sidebar.checkbox("""Generation options""") if generate_options: _a = """ ### Answer generation options The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large) weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with **beam** search, or **sample** from the decoder's output probabilities. """ st.sidebar.markdown(generate_info) _a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""]) _a = st.sidebar.slider( """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None ) _a = st.sidebar.slider( """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None ) if sampled == "beam": _a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: _a = st.sidebar.slider( """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) _a = st.sidebar.slider( """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) _a = None # start main text _a = [ """<MY QUESTION>""", """How do people make chocolate?""", """Why do we get a fever when we are sick?""", """How can different animals perceive different colors?""", """What is natural language processing?""", """What's the best way to treat a sunburn?""", """What exactly are vitamins ?""", """How does nuclear energy provide electricity?""", """What's the difference between viruses and bacteria?""", """Why are flutes classified as woodwinds when most of them are made out of metal ?""", """Why do people like drinking coffee even though it tastes so bad?""", """What happens when wine ages? How does it make the wine taste better?""", """If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""", """How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""", """How does New Zealand have so many large bird predators?""", ] _a = st.selectbox( """What would you like to ask? ---- select <MY QUESTION> to enter a new query""", questions_list, index=1, ) if question_s == "<MY QUESTION>": _a = st.text_input("""Enter your question here:""", """""") else: _a = question_s if st.button("""Show me!"""): if action in [0, 1, 3]: if index_type == "mixed": _a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10) _a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10) _a = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] _a = support_list[:10] _a = """<P> """ + """ <P> """.join([res[-1] for res in support_list]) else: _a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10) if action in [0, 3]: _a , _a = answer_question( question_doc, sas_model, sas_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == """sampled"""), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown("""### The model generated answer is:""") st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""") for i, res in enumerate(support_list): _a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_""")) _a = res[1].strip() if sec_titles == "": _a = """[{}]({})""".format(res[0], wiki_url) else: _a = sec_titles.split(""" & """) _a = """ & """.join( ["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list] ) st.markdown( """{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( """> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True ) if action in [2, 3]: _a = find_nearest_training(question) _a = nn_train_list[0] st.markdown( """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""]) ) _a = [ """{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""])) for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""])) if i == 0 or sc > 2 ] st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st))) _a = """ --- **Disclaimer** *The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system. Evaluating biases of such a model and ensuring factual generations are still very much open research problems. Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.* """ st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
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import contextlib import csv import json import os import sqlitea import tarfile import textwrap import zipfile import pyarrow as pa import pyarrow.parquet as pq import pytest import datasets import datasets.config @pytest.fixture(scope="session" ) def __lowerCamelCase (): SCREAMING_SNAKE_CASE = 1_0 SCREAMING_SNAKE_CASE = datasets.Features( { "tokens": datasets.Sequence(datasets.Value("string" ) ), "labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ), "answers": datasets.Sequence( { "text": datasets.Value("string" ), "answer_start": datasets.Value("int32" ), } ), "id": datasets.Value("int64" ), } ) SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict( { "tokens": [["foo"] * 5] * n, "labels": [[1] * 5] * n, "answers": [{"answer_start": [9_7], "text": ["1976"]}] * 1_0, "id": list(range(__snake_case ) ), } , features=__snake_case , ) return dataset @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "file.arrow" ) dataset.map(cache_file_name=__snake_case ) return filename # FILE_CONTENT + files _lowerCamelCase : Any = '''\ Text data. Second line of data.''' @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt" SCREAMING_SNAKE_CASE = FILE_CONTENT with open(__snake_case , "w" ) as f: f.write(__snake_case ) return filename @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any ): import bza SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.bz2" SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" ) with bza.open(__snake_case , "wb" ) as f: f.write(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Tuple ): import gzip SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" ) SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" ) with gzip.open(__snake_case , "wb" ) as f: f.write(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Optional[Any] ): if datasets.config.LZ4_AVAILABLE: import lza.frame SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.lz4" SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" ) with lza.frame.open(__snake_case , "wb" ) as f: f.write(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : Dict ): if datasets.config.PY7ZR_AVAILABLE: import pyazr SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.7z" with pyazr.SevenZipFile(__snake_case , "w" ) as archive: archive.write(__snake_case , arcname=os.path.basename(__snake_case ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : str ): import tarfile SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.tar" with tarfile.TarFile(__snake_case , "w" ) as f: f.add(__snake_case , arcname=os.path.basename(__snake_case ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : int ): import lzma SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.xz" SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" ) with lzma.open(__snake_case , "wb" ) as f: f.write(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] ): import zipfile SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Tuple ): if datasets.config.ZSTANDARD_AVAILABLE: import zstandard as zstd SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.zst" SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" ) with zstd.open(__snake_case , "wb" ) as f: f.write(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.xml" SCREAMING_SNAKE_CASE = textwrap.dedent( "\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" ) with open(__snake_case , "w" ) as f: f.write(__snake_case ) return filename _lowerCamelCase : Dict = [ {'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0}, {'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0}, {'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0}, {'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0}, ] _lowerCamelCase : Optional[Any] = [ {'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0}, {'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0}, ] _lowerCamelCase : Dict = { '''col_1''': ['''0''', '''1''', '''2''', '''3'''], '''col_2''': [0, 1, 2, 3], '''col_3''': [0.0, 1.0, 2.0, 3.0], } _lowerCamelCase : str = [ {'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0}, {'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1}, ] _lowerCamelCase : str = [ {'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0}, {'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0}, {'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0}, {'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0}, ] @pytest.fixture(scope="session" ) def __lowerCamelCase (): return DATA_DICT_OF_LISTS @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any ): SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict(__snake_case ) SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" ) dataset.map(cache_file_name=__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Dict ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" ) with contextlib.closing(sqlitea.connect(__snake_case ) ) as con: SCREAMING_SNAKE_CASE = con.cursor() cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" ) for item in DATA: cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)" , tuple(item.values() ) ) con.commit() return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[Any] ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" ) with open(__snake_case , "w" , newline="" ) as f: SCREAMING_SNAKE_CASE = csv.DictWriter(__snake_case , fieldnames=["col_1", "col_2", "col_3"] ) writer.writeheader() for item in DATA: writer.writerow(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" ) with open(__snake_case , "w" , newline="" ) as f: SCREAMING_SNAKE_CASE = csv.DictWriter(__snake_case , fieldnames=["col_1", "col_2", "col_3"] ) writer.writeheader() for item in DATA: writer.writerow(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : Any ): import bza SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2" with open(__snake_case , "rb" ) as f: SCREAMING_SNAKE_CASE = f.read() # data = bytes(FILE_CONTENT, "utf-8") with bza.open(__snake_case , "wb" ) as f: f.write(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Dict ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.basename(csv_path.replace(".csv" , ".CSV" ) ) ) f.write(__snake_case , arcname=os.path.basename(csva_path.replace(".csv" , ".CSV" ) ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) ) f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" ) SCREAMING_SNAKE_CASE = pa.schema( { "col_1": pa.string(), "col_2": pa.intaa(), "col_3": pa.floataa(), } ) with open(__snake_case , "wb" ) as f: SCREAMING_SNAKE_CASE = pq.ParquetWriter(__snake_case , schema=__snake_case ) SCREAMING_SNAKE_CASE = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__snake_case ) )] for k in DATA[0]} , schema=__snake_case ) writer.write_table(__snake_case ) writer.close() return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : str ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.json" ) SCREAMING_SNAKE_CASE = {"data": DATA} with open(__snake_case , "w" ) as f: json.dump(__snake_case , __snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Tuple ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.json" ) SCREAMING_SNAKE_CASE = {"data": DATA_DICT_OF_LISTS} with open(__snake_case , "w" ) as f: json.dump(__snake_case , __snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : str ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" ) with open(__snake_case , "w" ) as f: for item in DATA: f.write(json.dumps(__snake_case ) + "\n" ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Tuple ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" ) with open(__snake_case , "w" ) as f: for item in DATA: f.write(json.dumps(__snake_case ) + "\n" ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : int ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" ) with open(__snake_case , "w" ) as f: for item in DATA_312: f.write(json.dumps(__snake_case ) + "\n" ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : str ): SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" ) with open(__snake_case , "w" ) as f: for item in DATA_STR: f.write(json.dumps(__snake_case ) + "\n" ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] ): import gzip SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" ) with open(__snake_case , "rb" ) as orig_file: with gzip.open(__snake_case , "wb" ) as zipped_file: zipped_file.writelines(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] ): import gzip SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" ) with open(__snake_case , "rb" ) as orig_file: with gzip.open(__snake_case , "wb" ) as zipped_file: zipped_file.writelines(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.join("nested" , os.path.basename(__snake_case ) ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) ) f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar" with tarfile.TarFile(__snake_case , "w" ) as f: f.add(__snake_case , arcname=os.path.basename(__snake_case ) ) f.add(__snake_case , arcname=os.path.basename(__snake_case ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar" with tarfile.TarFile(__snake_case , "w" ) as f: f.add(__snake_case , arcname=os.path.join("nested" , os.path.basename(__snake_case ) ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = ["0", "1", "2", "3"] SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" ) with open(__snake_case , "w" ) as f: for item in data: f.write(item + "\n" ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ): SCREAMING_SNAKE_CASE = ["0", "1", "2", "3"] SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" ) with open(__snake_case , "w" ) as f: for item in data: f.write(item + "\n" ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ): SCREAMING_SNAKE_CASE = ["0", "1", "2", "3"] SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.abc" with open(__snake_case , "w" ) as f: for item in data: f.write(item + "\n" ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.text.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) ) f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Dict ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.ext.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.basename("unsupported.ext" ) ) f.write(__snake_case , arcname=os.path.basename("unsupported_2.ext" ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : Optional[int] ): SCREAMING_SNAKE_CASE = "\n".join(["First", "Second\u2029with Unicode new line", "Third"] ) SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" ) with open(__snake_case , "w" , encoding="utf-8" ) as f: f.write(__snake_case ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (): return os.path.join("tests" , "features" , "data" , "test_image_rgb.jpg" ) @pytest.fixture(scope="session" ) def __lowerCamelCase (): return os.path.join("tests" , "features" , "data" , "test_audio_44100.wav" ) @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.img.zip" with zipfile.ZipFile(__snake_case , "w" ) as f: f.write(__snake_case , arcname=os.path.basename(__snake_case ) ) f.write(__snake_case , arcname=os.path.basename(__snake_case ).replace(".jpg" , "2.jpg" ) ) return path @pytest.fixture(scope="session" ) def __lowerCamelCase (UpperCAmelCase__ : int ): SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data_dir" ) (data_dir / "subdir").mkdir() with open(data_dir / "subdir" / "train.txt" , "w" ) as f: f.write("foo\n" * 1_0 ) with open(data_dir / "subdir" / "test.txt" , "w" ) as f: f.write("bar\n" * 1_0 ) # hidden file with open(data_dir / "subdir" / ".test.txt" , "w" ) as f: f.write("bar\n" * 1_0 ) # hidden directory (data_dir / ".subdir").mkdir() with open(data_dir / ".subdir" / "train.txt" , "w" ) as f: f.write("foo\n" * 1_0 ) with open(data_dir / ".subdir" / "test.txt" , "w" ) as f: f.write("bar\n" * 1_0 ) return data_dir
403
"""simple docstring""" import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() _a = logging.get_logger(__name__) _a = { """post_extract_proj""": """feature_projection.projection""", """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""", """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""", """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""", """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""", """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""", """self_attn_layer_norm""": """encoder.layers.*.layer_norm""", """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""", """fc2""": """encoder.layers.*.feed_forward.output_dense""", """final_layer_norm""": """encoder.layers.*.final_layer_norm""", """encoder.layer_norm""": """encoder.layer_norm""", """w2v_model.layer_norm""": """feature_projection.layer_norm""", """quantizer.weight_proj""": """quantizer.weight_proj""", """quantizer.vars""": """quantizer.codevectors""", """project_q""": """project_q""", """final_proj""": """project_hid""", """w2v_encoder.proj""": """lm_head""", """mask_emb""": """masked_spec_embed""", } _a = [ """lm_head""", """quantizer.weight_proj""", """quantizer.codevectors""", """project_q""", """project_hid""", ] def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple: """simple docstring""" for attribute in key.split('''.''' ): _UpperCamelCase = getattr(__snake_case, __snake_case ) if weight_type is not None: _UpperCamelCase = getattr(__snake_case, __snake_case ).shape else: _UpperCamelCase = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": _UpperCamelCase = value elif weight_type == "weight_g": _UpperCamelCase = value elif weight_type == "weight_v": _UpperCamelCase = value elif weight_type == "bias": _UpperCamelCase = value else: _UpperCamelCase = value logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' ) def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = [] _UpperCamelCase = fairseq_model.state_dict() _UpperCamelCase = hf_model.feature_extractor _UpperCamelCase = hf_model.adapter for name, value in fairseq_dict.items(): _UpperCamelCase = False if "conv_layers" in name: load_conv_layer( __snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', ) _UpperCamelCase = True elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ): load_adapter(__snake_case, __snake_case, __snake_case, __snake_case ) _UpperCamelCase = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: _UpperCamelCase = True if "*" in mapped_key: _UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2] _UpperCamelCase = mapped_key.replace('''*''', __snake_case ) if "weight_g" in name: _UpperCamelCase = '''weight_g''' elif "weight_v" in name: _UpperCamelCase = '''weight_v''' elif "bias" in name: _UpperCamelCase = '''bias''' elif "weight" in name: _UpperCamelCase = '''weight''' else: _UpperCamelCase = None set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) continue if not is_used: unused_weights.append(__snake_case ) logger.warning(F'''Unused weights: {unused_weights}''' ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = full_name.split('''conv_layers.''' )[-1] _UpperCamelCase = name.split('''.''' ) _UpperCamelCase = int(items[0] ) _UpperCamelCase = int(items[1] ) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict: """simple docstring""" _UpperCamelCase = full_name.split('''adaptor.''' )[-1] _UpperCamelCase = name.split('''.''' ) if items[1].isdigit(): _UpperCamelCase = int(items[1] ) else: _UpperCamelCase = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' _UpperCamelCase = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' ) elif isinstance(__snake_case, __snake_case ): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase , _UpperCamelCase = emb.weight.shape _UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case ) _UpperCamelCase = emb.weight.data return lin_layer @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = WavaVecaConfig.from_pretrained( __snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, ) _UpperCamelCase = MBartConfig.from_pretrained(__snake_case ) # load model _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={ '''config_yaml''': config_yaml_path, '''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path, '''load_pretrained_decoder_from''': None, }, ) _UpperCamelCase = model[0].eval() # load feature extractor _UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case ) # set weights for wav2vec2 encoder _UpperCamelCase = WavaVecaModel(__snake_case ) recursively_load_weights_wavaveca(model.encoder, __snake_case ) # load decoder weights _UpperCamelCase = MBartForCausalLM(__snake_case ) _UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case ) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' ) logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' ) _UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case ) _UpperCamelCase = False _UpperCamelCase = MBartaaTokenizer(__snake_case ) tokenizer.save_pretrained(__snake_case ) _UpperCamelCase = hf_wavavec.config.to_dict() _UpperCamelCase = tokenizer.pad_token_id _UpperCamelCase = tokenizer.bos_token_id _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = '''mbart50''' _UpperCamelCase = '''wav2vec2''' _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = 25_00_04 _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case ) hf_wavavec.save_pretrained(__snake_case ) feature_extractor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""") parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""") parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""") parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""") parser.add_argument( """--encoder_config_path""", default="""facebook/wav2vec2-xls-r-1b""", type=str, help="""Path to hf encoder wav2vec2 checkpoint config""", ) parser.add_argument( """--decoder_config_path""", default="""facebook/mbart-large-50-one-to-many-mmt""", type=str, help="""Path to hf decoder checkpoint config""", ) parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""") parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""") parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""") parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""") parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""") _a = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )
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'''simple docstring''' import datasets from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py _snake_case : Tuple = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n author = \"Lin, Chin-Yew and\n Och, Franz Josef\",\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n month = \"aug 23{--}aug 27\",\n year = \"2004\",\n address = \"Geneva, Switzerland\",\n publisher = \"COLING\",\n url = \"https://www.aclweb.org/anthology/C04-1072\",\n pages = \"501--507\",\n}\n' _snake_case : Any = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n' _snake_case : Dict = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric(\"bleu\")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results[\"bleu\"])\n 1.0\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION ) class A ( datasets.Metric ): def __lowerCAmelCase ( self : List[str] ) -> List[str]: """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { '''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ), '''references''': datasets.Sequence( datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ), } ) , codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'''] , reference_urls=[ '''https://en.wikipedia.org/wiki/BLEU''', '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''', ] , ) def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : Union[str, Any]=False ) -> Dict: """simple docstring""" _a = compute_bleu( reference_corpus=__a , translation_corpus=__a , max_order=__a , smooth=__a ) ((_a) , (_a) , (_a) , (_a) , (_a) , (_a)) = score return { "bleu": bleu, "precisions": precisions, "brevity_penalty": bp, "length_ratio": ratio, "translation_length": translation_length, "reference_length": reference_length, }
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"""simple docstring""" import fire from utils import calculate_rouge, save_json def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]: """simple docstring""" _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()] _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )] _UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case ) if save_path is not None: save_json(__snake_case, __snake_case, indent=__snake_case ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)
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'''simple docstring''' from collections import defaultdict from math import gcd def __a ( A__ = 150_0000 ) -> int: lowerCAmelCase = defaultdict(__snake_case ) lowerCAmelCase = 2 while 2 * euclid_m * (euclid_m + 1) <= limit: for euclid_n in range((euclid_m % 2) + 1 , __snake_case , 2 ): if gcd(__snake_case , __snake_case ) > 1: continue lowerCAmelCase = 2 * euclid_m * (euclid_m + euclid_n) for perimeter in range(__snake_case , limit + 1 , __snake_case ): frequencies[perimeter] += 1 euclid_m += 1 return sum(1 for frequency in frequencies.values() if frequency == 1 ) if __name__ == "__main__": print(f"{solution() = }")
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"""simple docstring""" import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['image_processor', 'tokenizer'] lowercase__ = 'ViTImageProcessor' lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast') def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = None if "feature_extractor" in kwargs: warnings.warn( '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`''' ''' instead.''' , __a , ) _UpperCamelCase = kwargs.pop('''feature_extractor''') _UpperCamelCase = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError('''You need to specify an `image_processor`.''') if tokenizer is None: raise ValueError('''You need to specify a `tokenizer`.''') super().__init__(__a , __a) def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple: '''simple docstring''' if text is None and visual_prompt is None and images is None: raise ValueError('''You have to specify either text, visual prompt or images.''') if text is not None and visual_prompt is not None: raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''') if text is not None: _UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a) if visual_prompt is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if images is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if visual_prompt is not None and images is not None: _UpperCamelCase = { '''pixel_values''': image_features.pixel_values, '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding elif text is not None and images is not None: _UpperCamelCase = image_features.pixel_values return encoding elif text is not None: return encoding elif visual_prompt is not None: _UpperCamelCase = { '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding else: return BatchEncoding(data=dict(**__a) , tensor_type=__a) def UpperCAmelCase ( self , *__a , **__a) -> Any: '''simple docstring''' return self.tokenizer.batch_decode(*__a , **__a) def UpperCAmelCase ( self , *__a , **__a) -> List[str]: '''simple docstring''' return self.tokenizer.decode(*__a , **__a) @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , ) return self.image_processor_class @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , ) return self.image_processor
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'''simple docstring''' import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class _lowercase ( lowerCAmelCase ): '''simple docstring''' UpperCAmelCase_ : List[Any] = (DDPMScheduler,) def lowerCAmelCase__ ( self ,**lowerCamelCase_ ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase__ : Union[str, Any] = { '''num_train_timesteps''': 1000, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''variance_type''': '''fixed_small''', '''clip_sample''': True, } config.update(**__a ) return config def lowerCAmelCase__ ( self ) -> Dict: '''simple docstring''' for timesteps in [1, 5, 100, 1000]: self.check_over_configs(num_train_timesteps=__a ) def lowerCAmelCase__ ( self ) -> int: '''simple docstring''' for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,[0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=__a ,beta_end=__a ) def lowerCAmelCase__ ( self ) -> Tuple: '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=__a ) def lowerCAmelCase__ ( self ) -> List[str]: '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=__a ) def lowerCAmelCase__ ( self ) -> Dict: '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=__a ) def lowerCAmelCase__ ( self ) -> Optional[int]: '''simple docstring''' self.check_over_configs(thresholding=__a ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=__a ,prediction_type=__a ,sample_max_value=__a ,) def lowerCAmelCase__ ( self ) -> Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=__a ) def lowerCAmelCase__ ( self ) -> Optional[Any]: '''simple docstring''' for t in [0, 500, 999]: self.check_over_forward(time_step=__a ) def lowerCAmelCase__ ( self ) -> Tuple: '''simple docstring''' UpperCAmelCase__ : Tuple = self.scheduler_classes[0] UpperCAmelCase__ : List[str] = self.get_scheduler_config() UpperCAmelCase__ : Dict = scheduler_class(**__a ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5 def lowerCAmelCase__ ( self ) -> str: '''simple docstring''' UpperCAmelCase__ : List[Any] = self.scheduler_classes[0] UpperCAmelCase__ : Any = self.get_scheduler_config() UpperCAmelCase__ : Tuple = scheduler_class(**__a ) UpperCAmelCase__ : str = len(__a ) UpperCAmelCase__ : List[Any] = self.dummy_model() UpperCAmelCase__ : Optional[Any] = self.dummy_sample_deter UpperCAmelCase__ : List[Any] = torch.manual_seed(0 ) for t in reversed(range(__a ) ): # 1. predict noise residual UpperCAmelCase__ : Dict = model(__a ,__a ) # 2. predict previous mean of sample x_t-1 UpperCAmelCase__ : Dict = scheduler.step(__a ,__a ,__a ,generator=__a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance UpperCAmelCase__ : List[str] = pred_prev_sample UpperCAmelCase__ : Tuple = torch.sum(torch.abs(__a ) ) UpperCAmelCase__ : int = torch.mean(torch.abs(__a ) ) assert abs(result_sum.item() - 258.9606 ) < 1e-2 assert abs(result_mean.item() - 0.3372 ) < 1e-3 def lowerCAmelCase__ ( self ) -> str: '''simple docstring''' UpperCAmelCase__ : Optional[int] = self.scheduler_classes[0] UpperCAmelCase__ : Any = self.get_scheduler_config(prediction_type='''v_prediction''' ) UpperCAmelCase__ : Optional[Any] = scheduler_class(**__a ) UpperCAmelCase__ : str = len(__a ) UpperCAmelCase__ : int = self.dummy_model() UpperCAmelCase__ : Optional[int] = self.dummy_sample_deter UpperCAmelCase__ : Union[str, Any] = torch.manual_seed(0 ) for t in reversed(range(__a ) ): # 1. predict noise residual UpperCAmelCase__ : Optional[Any] = model(__a ,__a ) # 2. predict previous mean of sample x_t-1 UpperCAmelCase__ : Optional[Any] = scheduler.step(__a ,__a ,__a ,generator=__a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance UpperCAmelCase__ : Optional[Any] = pred_prev_sample UpperCAmelCase__ : List[Any] = torch.sum(torch.abs(__a ) ) UpperCAmelCase__ : Tuple = torch.mean(torch.abs(__a ) ) assert abs(result_sum.item() - 202.0296 ) < 1e-2 assert abs(result_mean.item() - 0.2631 ) < 1e-3 def lowerCAmelCase__ ( self ) -> Any: '''simple docstring''' UpperCAmelCase__ : Union[str, Any] = self.scheduler_classes[0] UpperCAmelCase__ : Dict = self.get_scheduler_config() UpperCAmelCase__ : Optional[int] = scheduler_class(**__a ) UpperCAmelCase__ : Optional[Any] = [100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=__a ) UpperCAmelCase__ : Dict = scheduler.timesteps for i, timestep in enumerate(__a ): if i == len(__a ) - 1: UpperCAmelCase__ : Tuple = -1 else: UpperCAmelCase__ : List[str] = timesteps[i + 1] UpperCAmelCase__ : Optional[Any] = scheduler.previous_timestep(__a ) UpperCAmelCase__ : Any = prev_t.item() self.assertEqual(__a ,__a ) def lowerCAmelCase__ ( self ) -> Dict: '''simple docstring''' UpperCAmelCase__ : List[str] = self.scheduler_classes[0] UpperCAmelCase__ : Optional[int] = self.get_scheduler_config() UpperCAmelCase__ : Optional[Any] = scheduler_class(**__a ) UpperCAmelCase__ : Any = [100, 87, 50, 51, 0] with self.assertRaises(__a ,msg='''`custom_timesteps` must be in descending order.''' ): scheduler.set_timesteps(timesteps=__a ) def lowerCAmelCase__ ( self ) -> List[str]: '''simple docstring''' UpperCAmelCase__ : Any = self.scheduler_classes[0] UpperCAmelCase__ : Any = self.get_scheduler_config() UpperCAmelCase__ : List[str] = scheduler_class(**__a ) UpperCAmelCase__ : str = [100, 87, 50, 1, 0] UpperCAmelCase__ : List[Any] = len(__a ) with self.assertRaises(__a ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ): scheduler.set_timesteps(num_inference_steps=__a ,timesteps=__a ) def lowerCAmelCase__ ( self ) -> Tuple: '''simple docstring''' UpperCAmelCase__ : str = self.scheduler_classes[0] UpperCAmelCase__ : Any = self.get_scheduler_config() UpperCAmelCase__ : Dict = scheduler_class(**__a ) UpperCAmelCase__ : str = [scheduler.config.num_train_timesteps] with self.assertRaises( __a ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,): scheduler.set_timesteps(timesteps=__a )
614
"""simple docstring""" import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import UperNetForSemanticSegmentation from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = num_channels _UpperCamelCase = num_stages _UpperCamelCase = hidden_sizes _UpperCamelCase = depths _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = out_features _UpperCamelCase = num_labels _UpperCamelCase = scope _UpperCamelCase = num_stages def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ConvNextConfig( num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return UperNetConfig( backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = UperNetForSemanticSegmentation(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size)) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else () lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = UperNetModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*__a) @unittest.skip(reason='''UperNet does not use inputs_embeds''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not support input and output embeddings''') def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> int: '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''') def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' pass @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' def check_hidden_states_output(__a , __a , __a): _UpperCamelCase = model_class(__a) model.to(__a) model.eval() with torch.no_grad(): _UpperCamelCase = model(**self._prepare_for_class(__a , __a)) _UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states _UpperCamelCase = self.model_tester.num_stages self.assertEqual(len(__a) , expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) _UpperCamelCase = _config_zero_init(configs_no_init.backbone_config) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @unittest.skip(reason='''UperNet does not have tied weights''') def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' pass @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = hf_hub_download( repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' ) _UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' ) return image @require_torch @require_vision @slow class _UpperCAmelCase( unittest.TestCase ): def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
19
0
'''simple docstring''' from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class __magic_name__ : def __init__( self , snake_case , snake_case=1_3 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=9_9 , snake_case=3_2 , snake_case=2 , snake_case=4 , snake_case=3_7 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=5_1_2 , snake_case=1_6 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , snake_case=0 , ) -> Any: '''simple docstring''' _UpperCAmelCase : str =parent _UpperCAmelCase : Optional[Any] =batch_size _UpperCAmelCase : Tuple =seq_length _UpperCAmelCase : Any =is_training _UpperCAmelCase : Optional[Any] =use_input_mask _UpperCAmelCase : str =use_token_type_ids _UpperCAmelCase : str =use_labels _UpperCAmelCase : str =vocab_size _UpperCAmelCase : Tuple =hidden_size _UpperCAmelCase : List[Any] =num_hidden_layers _UpperCAmelCase : int =num_attention_heads _UpperCAmelCase : Union[str, Any] =intermediate_size _UpperCAmelCase : Optional[Any] =hidden_act _UpperCAmelCase : List[Any] =hidden_dropout_prob _UpperCAmelCase : List[Any] =attention_probs_dropout_prob _UpperCAmelCase : Optional[int] =max_position_embeddings _UpperCAmelCase : Dict =type_vocab_size _UpperCAmelCase : Optional[int] =type_sequence_label_size _UpperCAmelCase : int =initializer_range _UpperCAmelCase : str =num_labels _UpperCAmelCase : Any =num_choices _UpperCAmelCase : Any =scope _UpperCAmelCase : Union[str, Any] =projection_dim def lowerCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCAmelCase : List[str] =None if self.use_input_mask: # follow test_modeling_tf_ctrl.py _UpperCAmelCase : List[Any] =random_attention_mask([self.batch_size, self.seq_length]) _UpperCAmelCase : Optional[Any] =None if self.use_token_type_ids: _UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size) _UpperCAmelCase : int =None _UpperCAmelCase : Optional[Any] =None _UpperCAmelCase : List[str] =None if self.use_labels: _UpperCAmelCase : int =ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCAmelCase : Dict =ids_tensor([self.batch_size, self.seq_length] , self.num_labels) _UpperCAmelCase : Union[str, Any] =ids_tensor([self.batch_size] , self.num_choices) _UpperCAmelCase : Union[str, Any] =BertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , ) _UpperCAmelCase : Any =DPRConfig(projection_dim=self.projection_dim , **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case) -> Optional[int]: '''simple docstring''' _UpperCAmelCase : List[str] =TFDPRContextEncoder(config=__a) _UpperCAmelCase : Union[str, Any] =model(__a , attention_mask=__a , token_type_ids=__a) _UpperCAmelCase : List[str] =model(__a , token_type_ids=__a) _UpperCAmelCase : Dict =model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case) -> Union[str, Any]: '''simple docstring''' _UpperCAmelCase : List[str] =TFDPRQuestionEncoder(config=__a) _UpperCAmelCase : str =model(__a , attention_mask=__a , token_type_ids=__a) _UpperCAmelCase : Union[str, Any] =model(__a , token_type_ids=__a) _UpperCAmelCase : int =model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case) -> Dict: '''simple docstring''' _UpperCAmelCase : Optional[Any] =TFDPRReader(config=__a) _UpperCAmelCase : str =model(__a , attention_mask=__a) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,)) def lowerCAmelCase ( self) -> Any: '''simple docstring''' _UpperCAmelCase : List[str] =self.prepare_config_and_inputs() ( ( _UpperCAmelCase ) , ( _UpperCAmelCase ) , ( _UpperCAmelCase ) , ( _UpperCAmelCase ) , ( _UpperCAmelCase ) , ( _UpperCAmelCase ) , ( _UpperCAmelCase ) , ) : Optional[int] =config_and_inputs _UpperCAmelCase : List[str] ={'input_ids': input_ids} return config, inputs_dict @require_tf class __magic_name__ ( lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase ): UpperCAmelCase =( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) UpperCAmelCase ={"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {} UpperCAmelCase =False UpperCAmelCase =False UpperCAmelCase =False UpperCAmelCase =False UpperCAmelCase =False def lowerCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCAmelCase : Dict =TFDPRModelTester(self) _UpperCAmelCase : Optional[int] =ConfigTester(self , config_class=__a , hidden_size=3_7) def lowerCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.config_tester.run_common_tests() def lowerCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCAmelCase : Optional[Any] =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__a) def lowerCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__a) def lowerCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__a) @slow def lowerCAmelCase ( self) -> str: '''simple docstring''' for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCAmelCase : Any =TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCAmelCase : int =TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCAmelCase : Union[str, Any] =TFDPRQuestionEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCAmelCase : Tuple =TFDPRReader.from_pretrained(__a) self.assertIsNotNone(__a) @require_tf class __magic_name__ ( unittest.TestCase ): @slow def lowerCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCAmelCase : int =TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base') _UpperCAmelCase : Optional[int] =tf.constant( [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]) # [CLS] hello, is my dog cute? [SEP] _UpperCAmelCase : Optional[int] =model(__a)[0] # embedding shape = (1, 768) # compare the actual values for a slice. _UpperCAmelCase : Optional[Any] =tf.constant( [ [ 0.03_23_62_53, 0.12_75_33_35, 0.16_81_85_09, 0.00_27_97_86, 0.3_89_69_33, 0.24_26_49_45, 0.2_17_89_71, -0.02_33_52_27, -0.08_48_19_59, -0.14_32_41_17, ] ]) self.assertTrue(numpy.allclose(output[:, :1_0].numpy() , expected_slice.numpy() , atol=1E-4))
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DDPMScheduler,) def UpperCAmelCase ( self , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 10_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''variance_type''': '''fixed_small''', '''clip_sample''': True, } config.update(**__a) return config def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for timesteps in [1, 5, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.check_over_configs(thresholding=__a) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=__a , prediction_type=__a , sample_max_value=__a , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for t in [0, 5_00, 9_99]: self.check_over_forward(time_step=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 258.9606) < 1e-2 assert abs(result_mean.item() - 0.3372) < 1e-3 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 202.0296) < 1e-2 assert abs(result_mean.item() - 0.2631) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=__a) _UpperCamelCase = scheduler.timesteps for i, timestep in enumerate(__a): if i == len(__a) - 1: _UpperCamelCase = -1 else: _UpperCamelCase = timesteps[i + 1] _UpperCamelCase = scheduler.previous_timestep(__a) _UpperCamelCase = prev_t.item() self.assertEqual(__a , __a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 51, 0] with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''): scheduler.set_timesteps(timesteps=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] _UpperCamelCase = len(__a) with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''): scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [scheduler.config.num_train_timesteps] with self.assertRaises( __a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ): scheduler.set_timesteps(timesteps=__a)
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _lowerCamelCase : Optional[int] = { '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''], '''tokenization_roformer''': ['''RoFormerTokenizer'''], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase : Optional[int] = ['''RoFormerTokenizerFast'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase : Any = [ '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''RoFormerForCausalLM''', '''RoFormerForMaskedLM''', '''RoFormerForMultipleChoice''', '''RoFormerForQuestionAnswering''', '''RoFormerForSequenceClassification''', '''RoFormerForTokenClassification''', '''RoFormerLayer''', '''RoFormerModel''', '''RoFormerPreTrainedModel''', '''load_tf_weights_in_roformer''', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase : Union[str, Any] = [ '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFRoFormerForCausalLM''', '''TFRoFormerForMaskedLM''', '''TFRoFormerForMultipleChoice''', '''TFRoFormerForQuestionAnswering''', '''TFRoFormerForSequenceClassification''', '''TFRoFormerForTokenClassification''', '''TFRoFormerLayer''', '''TFRoFormerModel''', '''TFRoFormerPreTrainedModel''', ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase : Union[str, Any] = [ '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''FlaxRoFormerForMaskedLM''', '''FlaxRoFormerForMultipleChoice''', '''FlaxRoFormerForQuestionAnswering''', '''FlaxRoFormerForSequenceClassification''', '''FlaxRoFormerForTokenClassification''', '''FlaxRoFormerModel''', '''FlaxRoFormerPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig from .tokenization_roformer import RoFormerTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_roformer_fast import RoFormerTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_roformer import ( ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, RoFormerForCausalLM, RoFormerForMaskedLM, RoFormerForMultipleChoice, RoFormerForQuestionAnswering, RoFormerForSequenceClassification, RoFormerForTokenClassification, RoFormerLayer, RoFormerModel, RoFormerPreTrainedModel, load_tf_weights_in_roformer, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_roformer import ( TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForMultipleChoice, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerLayer, TFRoFormerModel, TFRoFormerPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_roformer import ( FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, FlaxRoFormerForMaskedLM, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerModel, FlaxRoFormerPreTrainedModel, ) else: import sys _lowerCamelCase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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"""simple docstring""" from __future__ import annotations from functools import lru_cache from math import ceil _a = 100 _a = set(range(3, NUM_PRIMES, 2)) primes.add(2) _a = 42 for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=1_00 ) def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} _UpperCamelCase = set() _UpperCamelCase = 42 _UpperCamelCase = 42 for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None: """simple docstring""" for number_to_partition in range(1, __snake_case ): if len(partition(__snake_case ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(F"""{solution() = }""")
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import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import UperNetForSemanticSegmentation from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class __magic_name__ : def __init__( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str=13 , UpperCamelCase__ : Optional[int]=32 , UpperCamelCase__ : Tuple=3 , UpperCamelCase__ : Optional[Any]=4 , UpperCamelCase__ : str=[10, 20, 30, 40] , UpperCamelCase__ : str=[2, 2, 3, 2] , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Dict=37 , UpperCamelCase__ : str="gelu" , UpperCamelCase__ : List[str]=10 , UpperCamelCase__ : int=0.02 , UpperCamelCase__ : List[str]=["stage2", "stage3", "stage4"] , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Dict=None , ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = parent UpperCAmelCase = batch_size UpperCAmelCase = image_size UpperCAmelCase = num_channels UpperCAmelCase = num_stages UpperCAmelCase = hidden_sizes UpperCAmelCase = depths UpperCAmelCase = is_training UpperCAmelCase = use_labels UpperCAmelCase = intermediate_size UpperCAmelCase = hidden_act UpperCAmelCase = type_sequence_label_size UpperCAmelCase = initializer_range UpperCAmelCase = out_features UpperCAmelCase = num_labels UpperCAmelCase = scope UpperCAmelCase = num_stages def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[str]: '''simple docstring''' UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) UpperCAmelCase = None if self.use_labels: UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase = self.get_config() return config, pixel_values, labels def SCREAMING_SNAKE_CASE_ ( self : int ) -> Any: '''simple docstring''' return ConvNextConfig( num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , ) def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Optional[int]: '''simple docstring''' return UperNetConfig( backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = UperNetForSemanticSegmentation(config=__a ) model.to(__a ) model.eval() UpperCAmelCase = model(__a ) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) ) def SCREAMING_SNAKE_CASE_ ( self : str ) -> Dict: '''simple docstring''' UpperCAmelCase = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) = config_and_inputs UpperCAmelCase = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class __magic_name__ ( A__, A__, unittest.TestCase ): lowercase : Optional[int] =(UperNetForSemanticSegmentation,) if is_torch_available() else () lowercase : List[str] ={'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {} lowercase : List[Any] =False lowercase : Dict =False lowercase : Any =False lowercase : int =False lowercase : Optional[Any] =False lowercase : Dict =False def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase = UperNetModelTester(self ) UpperCAmelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37 ) def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Dict: '''simple docstring''' self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]: '''simple docstring''' return def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> str: '''simple docstring''' UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCAmelCase = model_class(__a ) UpperCAmelCase = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic UpperCAmelCase = [*signature.parameters.keys()] UpperCAmelCase = ["pixel_values"] self.assertListEqual(arg_names[:1] , __a ) def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Optional[int]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*__a ) @unittest.skip(reason="UperNet does not use inputs_embeds" ) def SCREAMING_SNAKE_CASE_ ( self : int ) -> Any: '''simple docstring''' pass @unittest.skip(reason="UperNet does not support input and output embeddings" ) def SCREAMING_SNAKE_CASE_ ( self : str ) -> List[Any]: '''simple docstring''' pass @unittest.skip(reason="UperNet does not have a base model" ) def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Tuple: '''simple docstring''' pass @unittest.skip(reason="UperNet does not have a base model" ) def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> int: '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip(reason="UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`" ) def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Union[str, Any]: '''simple docstring''' pass @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." ) def SCREAMING_SNAKE_CASE_ ( self : int ) -> Any: '''simple docstring''' pass def SCREAMING_SNAKE_CASE_ ( self : int ) -> List[Any]: '''simple docstring''' def check_hidden_states_output(UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str , UpperCamelCase__ : List[str] ): UpperCAmelCase = model_class(__a ) model.to(__a ) model.eval() with torch.no_grad(): UpperCAmelCase = model(**self._prepare_for_class(__a , __a ) ) UpperCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states UpperCAmelCase = self.model_tester.num_stages self.assertEqual(len(__a ) , expected_num_stages + 1 ) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCAmelCase = True check_hidden_states_output(__a , __a , __a ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] UpperCAmelCase = True check_hidden_states_output(__a , __a , __a ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common() UpperCAmelCase = _config_zero_init(__a ) UpperCAmelCase = _config_zero_init(configs_no_init.backbone_config ) for model_class in self.all_model_classes: UpperCAmelCase = model_class(config=__a ) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , ) @unittest.skip(reason="UperNet does not have tied weights" ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' pass @slow def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Optional[Any]: '''simple docstring''' for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase = UperNetForSemanticSegmentation.from_pretrained(__a ) self.assertIsNotNone(__a ) def lowerCamelCase_() -> int: UpperCAmelCase = hf_hub_download( repo_id="hf-internal-testing/fixtures_ade20k" , repo_type="dataset" , filename="ADE_val_00000001.jpg" ) UpperCAmelCase = Image.open(__snake_case ).convert("RGB" ) return image @require_torch @require_vision @slow class __magic_name__ ( unittest.TestCase ): def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Optional[int]: '''simple docstring''' UpperCAmelCase = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny" ) UpperCAmelCase = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny" ).to(__a ) UpperCAmelCase = prepare_img() UpperCAmelCase = processor(images=__a , return_tensors="pt" ).to(__a ) with torch.no_grad(): UpperCAmelCase = model(**__a ) UpperCAmelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12) ) self.assertEqual(outputs.logits.shape , __a ) UpperCAmelCase = torch.tensor( [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]] ).to(__a ) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4 ) ) def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> List[Any]: '''simple docstring''' UpperCAmelCase = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny" ) UpperCAmelCase = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny" ).to(__a ) UpperCAmelCase = prepare_img() UpperCAmelCase = processor(images=__a , return_tensors="pt" ).to(__a ) with torch.no_grad(): UpperCAmelCase = model(**__a ) UpperCAmelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12) ) self.assertEqual(outputs.logits.shape , __a ) UpperCAmelCase = torch.tensor( [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] ).to(__a ) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4 ) )
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"""simple docstring""" from collections.abc import Callable import numpy as np def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array: """simple docstring""" _UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) ) _UpperCamelCase = np.zeros((n + 1,) ) _UpperCamelCase = ya _UpperCamelCase = xa for k in range(__snake_case ): _UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] ) _UpperCamelCase = y[k] + ( (step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class UpperCAmelCase ( a__ , a__ , a__ , unittest.TestCase ): '''simple docstring''' SCREAMING_SNAKE_CASE = StableDiffusionInpaintPipeline SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS SCREAMING_SNAKE_CASE = frozenset( [] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess SCREAMING_SNAKE_CASE = frozenset([] ) def _lowerCAmelCase( self ) -> str: torch.manual_seed(0 ) lowercase__ : List[Any] = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__a , ) lowercase__ : Union[str, Any] = PNDMScheduler(skip_prk_steps=__a ) torch.manual_seed(0 ) lowercase__ : Dict = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , ) torch.manual_seed(0 ) lowercase__ : List[Any] = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=512 , ) lowercase__ : Union[str, Any] = CLIPTextModel(__a ) lowercase__ : Dict = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ) lowercase__ : Optional[int] = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Optional[int]: lowercase__ : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(__a ) ).to(__a ) lowercase__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0] lowercase__ : Optional[int] = Image.fromarray(np.uinta(__a ) ).convert('''RGB''' ).resize((64, 64) ) lowercase__ : int = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) ) if str(__a ).startswith('''mps''' ): lowercase__ : Optional[Any] = torch.manual_seed(__a ) else: lowercase__ : Tuple = torch.Generator(device=__a ).manual_seed(__a ) lowercase__ : List[str] = { '''prompt''': '''A painting of a squirrel eating a burger''', '''image''': init_image, '''mask_image''': mask_image, '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', } return inputs def _lowerCAmelCase( self ) -> List[Any]: lowercase__ : Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator lowercase__ : Tuple = self.get_dummy_components() lowercase__ : Optional[Any] = StableDiffusionInpaintPipeline(**__a ) lowercase__ : Optional[Any] = sd_pipe.to(__a ) sd_pipe.set_progress_bar_config(disable=__a ) lowercase__ : int = self.get_dummy_inputs(__a ) lowercase__ : Any = sd_pipe(**__a ).images lowercase__ : Any = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowercase__ : List[str] = np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def _lowerCAmelCase( self ) -> Any: super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) @slow @require_torch_gpu class UpperCAmelCase ( unittest.TestCase ): '''simple docstring''' def _lowerCAmelCase( self ) -> Any: super().tearDown() gc.collect() torch.cuda.empty_cache() def _lowerCAmelCase( self ) -> int: lowercase__ : Any = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/sd2-inpaint/init_image.png''' ) lowercase__ : Union[str, Any] = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' ) lowercase__ : str = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint''' '''/yellow_cat_sitting_on_a_park_bench.npy''' ) lowercase__ : int = '''stabilityai/stable-diffusion-2-inpainting''' lowercase__ : Union[str, Any] = StableDiffusionInpaintPipeline.from_pretrained(__a , safety_checker=__a ) pipe.to(__a ) pipe.set_progress_bar_config(disable=__a ) pipe.enable_attention_slicing() lowercase__ : Optional[int] = '''Face of a yellow cat, high resolution, sitting on a park bench''' lowercase__ : Dict = torch.manual_seed(0 ) lowercase__ : Union[str, Any] = pipe( prompt=__a , image=__a , mask_image=__a , generator=__a , output_type='''np''' , ) lowercase__ : Optional[int] = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image ).max() < 9E-3 def _lowerCAmelCase( self ) -> Any: lowercase__ : Tuple = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/sd2-inpaint/init_image.png''' ) lowercase__ : List[str] = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' ) lowercase__ : Optional[int] = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint''' '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' ) lowercase__ : Dict = '''stabilityai/stable-diffusion-2-inpainting''' lowercase__ : Optional[int] = StableDiffusionInpaintPipeline.from_pretrained( __a , torch_dtype=torch.floataa , safety_checker=__a , ) pipe.to(__a ) pipe.set_progress_bar_config(disable=__a ) pipe.enable_attention_slicing() lowercase__ : Optional[int] = '''Face of a yellow cat, high resolution, sitting on a park bench''' lowercase__ : Tuple = torch.manual_seed(0 ) lowercase__ : Optional[int] = pipe( prompt=__a , image=__a , mask_image=__a , generator=__a , output_type='''np''' , ) lowercase__ : Optional[int] = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image ).max() < 5E-1 def _lowerCAmelCase( self ) -> str: torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() lowercase__ : Tuple = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/sd2-inpaint/init_image.png''' ) lowercase__ : List[Any] = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' ) lowercase__ : Optional[int] = '''stabilityai/stable-diffusion-2-inpainting''' lowercase__ : Optional[Any] = PNDMScheduler.from_pretrained(__a , subfolder='''scheduler''' ) lowercase__ : List[Any] = StableDiffusionInpaintPipeline.from_pretrained( __a , safety_checker=__a , scheduler=__a , torch_dtype=torch.floataa , ) pipe.to(__a ) pipe.set_progress_bar_config(disable=__a ) pipe.enable_attention_slicing(1 ) pipe.enable_sequential_cpu_offload() lowercase__ : Union[str, Any] = '''Face of a yellow cat, high resolution, sitting on a park bench''' lowercase__ : List[str] = torch.manual_seed(0 ) lowercase__ : Optional[Any] = pipe( prompt=__a , image=__a , mask_image=__a , generator=__a , num_inference_steps=2 , output_type='''np''' , ) lowercase__ : Optional[Any] = torch.cuda.max_memory_allocated() # make sure that less than 2.65 GB is allocated assert mem_bytes < 2.6_5 * 10**9
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"""simple docstring""" import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": _a = argparse.ArgumentParser( description=( """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned""" """ Distillation""" ) ) parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""]) parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str) parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str) parser.add_argument("""--vocab_transform""", action="""store_true""") _a = parser.parse_args() if args.model_type == "bert": _a = BertForMaskedLM.from_pretrained(args.model_name) _a = """bert""" else: raise ValueError("""args.model_type should be \"bert\".""") _a = model.state_dict() _a = {} for w in ["word_embeddings", "position_embeddings"]: _a = state_dict[F"""{prefix}.embeddings.{w}.weight"""] for w in ["weight", "bias"]: _a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""] _a = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}""" ] std_idx += 1 _a = state_dict["""cls.predictions.decoder.weight"""] _a = state_dict["""cls.predictions.bias"""] if args.vocab_transform: for w in ["weight", "bias"]: _a = state_dict[F"""cls.predictions.transform.dense.{w}"""] _a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""] print(F"""N layers selected for distillation: {std_idx}""") print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""") print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""") torch.save(compressed_sd, args.dump_checkpoint)
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from __future__ import annotations import random # Maximum size of the population. Bigger could be faster but is more memory expensive. _snake_case = 2_00 # Number of elements selected in every generation of evolution. The selection takes # place from best to worst of that generation and must be smaller than N_POPULATION. _snake_case = 50 # Probability that an element of a generation can mutate, changing one of its genes. # This will guarantee that all genes will be used during evolution. _snake_case = 0.4 # Just a seed to improve randomness required by the algorithm. random.seed(random.randint(0, 10_00)) def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): '''simple docstring''' lowerCamelCase : Dict = len([g for position, g in enumerate(__snake_case ) if g == main_target[position]] ) return (item, float(__snake_case )) def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): '''simple docstring''' lowerCamelCase : int = random.randint(0 , len(__snake_case ) - 1 ) lowerCamelCase : str = parent_a[:random_slice] + parent_a[random_slice:] lowerCamelCase : int = parent_a[:random_slice] + parent_a[random_slice:] return (child_a, child_a) def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): '''simple docstring''' lowerCamelCase : int = list(__snake_case ) if random.uniform(0 , 1 ) < MUTATION_PROBABILITY: lowerCamelCase : List[Any] = random.choice(__snake_case ) return "".join(__snake_case ) def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ): '''simple docstring''' lowerCamelCase : Any = [] # Generate more children proportionally to the fitness score. lowerCamelCase : str = int(parent_a[1] * 100 ) + 1 lowerCamelCase : Optional[Any] = 10 if child_n >= 10 else child_n for _ in range(__snake_case ): lowerCamelCase : Tuple = population_score[random.randint(0 , __snake_case )][0] lowerCamelCase , lowerCamelCase : List[Any] = crossover(parent_a[0] , __snake_case ) # Append new string to the population list. pop.append(mutate(__snake_case , __snake_case ) ) pop.append(mutate(__snake_case , __snake_case ) ) return pop def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True ): '''simple docstring''' if N_POPULATION < N_SELECTED: lowerCamelCase : str = f"""{N_POPULATION} must be bigger than {N_SELECTED}""" raise ValueError(__snake_case ) # Verify that the target contains no genes besides the ones inside genes variable. lowerCamelCase : Dict = sorted({c for c in target if c not in genes} ) if not_in_genes_list: lowerCamelCase : Union[str, Any] = f"""{not_in_genes_list} is not in genes list, evolution cannot converge""" raise ValueError(__snake_case ) # Generate random starting population. lowerCamelCase : Tuple = [] for _ in range(__snake_case ): population.append("".join([random.choice(__snake_case ) for i in range(len(__snake_case ) )] ) ) # Just some logs to know what the algorithms is doing. lowerCamelCase , lowerCamelCase : Union[str, Any] = 0, 0 # This loop will end when we find a perfect match for our target. while True: generation += 1 total_population += len(__snake_case ) # Random population created. Now it's time to evaluate. # Adding a bit of concurrency can make everything faster, # # import concurrent.futures # population_score: list[tuple[str, float]] = [] # with concurrent.futures.ThreadPoolExecutor( # max_workers=NUM_WORKERS) as executor: # futures = {executor.submit(evaluate, item) for item in population} # concurrent.futures.wait(futures) # population_score = [item.result() for item in futures] # # but with a simple algorithm like this, it will probably be slower. # We just need to call evaluate for every item inside the population. lowerCamelCase : Union[str, Any] = [evaluate(__snake_case , __snake_case ) for item in population] # Check if there is a matching evolution. lowerCamelCase : Dict = sorted(__snake_case , key=lambda SCREAMING_SNAKE_CASE_ : x[1] , reverse=__snake_case ) if population_score[0][0] == target: return (generation, total_population, population_score[0][0]) # Print the best result every 10 generation. # Just to know that the algorithm is working. if debug and generation % 10 == 0: print( f"""\nGeneration: {generation}""" f"""\nTotal Population:{total_population}""" f"""\nBest score: {population_score[0][1]}""" f"""\nBest string: {population_score[0][0]}""" ) # Flush the old population, keeping some of the best evolutions. # Keeping this avoid regression of evolution. lowerCamelCase : Tuple = population[: int(N_POPULATION / 3 )] population.clear() population.extend(__snake_case ) # Normalize population score to be between 0 and 1. lowerCamelCase : Tuple = [ (item, score / len(__snake_case )) for item, score in population_score ] # This is selection for i in range(__snake_case ): population.extend(select(population_score[int(__snake_case )] , __snake_case , __snake_case ) ) # Check if the population has already reached the maximum value and if so, # break the cycle. If this check is disabled, the algorithm will take # forever to compute large strings, but will also calculate small strings in # a far fewer generations. if len(__snake_case ) > N_POPULATION: break if __name__ == "__main__": _snake_case = ( '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!''' ) _snake_case = list( ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm''' '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\''' ) _snake_case , _snake_case , _snake_case = basic(target_str, genes_list) print( f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}''' )
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"""simple docstring""" import unittest from transformers import PegasusConfig, PegasusTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html _a = """platform""" import jax import jax.numpy as jnp import numpy as np from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel @require_flax class _UpperCAmelCase: lowercase__ = PegasusConfig lowercase__ = {} lowercase__ = 'gelu' def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = eos_token_id _UpperCamelCase = pad_token_id _UpperCamelCase = bos_token_id def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size) _UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1) _UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , ) _UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a) return config, inputs_dict def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''') _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ] , axis=-1 , ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]: """simple docstring""" if attention_mask is None: _UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta ) if decoder_attention_mask is None: _UpperCamelCase = np.concatenate( [ np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ), np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ), ], axis=-1, ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_flax class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( FlaxPegasusForConditionalGeneration, FlaxPegasusModel, ) if is_flax_available() else () ) lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else () lowercase__ = True lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = FlaxPegasusModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = self._prepare_for_class(__a , __a) _UpperCamelCase = model_class(__a) @jax.jit def encode_jitted(__a , __a=None , **__a): return model.encode(input_ids=__a , attention_mask=__a) with self.subTest('''JIT Enabled'''): _UpperCamelCase = encode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = encode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = model_class(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask''']) _UpperCamelCase = { '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''], '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''], '''encoder_outputs''': encoder_outputs, } @jax.jit def decode_jitted(__a , __a , __a): return model.decode( decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , ) with self.subTest('''JIT Enabled'''): _UpperCamelCase = decode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = decode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) @slow def UpperCAmelCase ( self) -> int: '''simple docstring''' for model_class_name in self.all_model_classes: _UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a) _UpperCamelCase = np.ones((1, 1)) _UpperCamelCase = model(__a) self.assertIsNotNone(__a) @slow def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = [ ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''', ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''', ] _UpperCamelCase = [ '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''', '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''', ] _UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a) _UpperCamelCase = model.generate(**__a , num_beams=2).sequences _UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a) assert tgt_text == decoded
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def lowerCAmelCase__ ( lowerCamelCase_ : str): '''simple docstring''' lowerCAmelCase__ : int = 1 for i in range(1 ,num + 1): fact *= i return fact def lowerCAmelCase__ ( lowerCamelCase_ : Tuple): '''simple docstring''' lowerCAmelCase__ : Dict = 0 while number > 0: lowerCAmelCase__ : List[str] = number % 10 sum_of_digits += last_digit lowerCAmelCase__ : Any = number // 10 # Removing the last_digit from the given number return sum_of_digits def lowerCAmelCase__ ( lowerCamelCase_ : Optional[int] = 100): '''simple docstring''' lowerCAmelCase__ : Union[str, Any] = factorial(__snake_case) lowerCAmelCase__ : int = split_and_add(__snake_case) return result if __name__ == "__main__": print(solution(int(input('Enter the Number: ').strip())))
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_input_mask _UpperCamelCase = use_token_type_ids _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = type_vocab_size _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = num_labels _UpperCamelCase = num_choices _UpperCamelCase = scope _UpperCamelCase = projection_dim def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py _UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length]) _UpperCamelCase = None if self.use_token_type_ids: _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size) _UpperCamelCase = None _UpperCamelCase = None _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels) _UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices) _UpperCamelCase = BertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , ) _UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]: '''simple docstring''' _UpperCamelCase = TFDPRContextEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict: '''simple docstring''' _UpperCamelCase = TFDPRReader(config=__a) _UpperCamelCase = model(__a , attention_mask=__a) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,)) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''input_ids''': input_ids} return config, inputs_dict @require_tf class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__a) @slow def UpperCAmelCase ( self) -> str: '''simple docstring''' for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRReader.from_pretrained(__a) self.assertIsNotNone(__a) @require_tf class _UpperCAmelCase( unittest.TestCase ): @slow def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''') _UpperCamelCase = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP] _UpperCamelCase = model(__a)[0] # embedding shape = (1, 768) # compare the actual values for a slice. _UpperCamelCase = tf.constant( [ [ 0.0323_6253, 0.1275_3335, 0.1681_8509, 0.0027_9786, 0.389_6933, 0.2426_4945, 0.217_8971, -0.0233_5227, -0.0848_1959, -0.1432_4117, ] ]) self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
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'''simple docstring''' def UpperCAmelCase_ (__a : List[str] , __a : Optional[Any] ): """simple docstring""" return int((input_a, input_a).count(0 ) != 0 ) def UpperCAmelCase_ (): """simple docstring""" assert nand_gate(0 , 0 ) == 1 assert nand_gate(0 , 1 ) == 1 assert nand_gate(1 , 0 ) == 1 assert nand_gate(1 , 1 ) == 0 if __name__ == "__main__": print(nand_gate(0, 0)) print(nand_gate(0, 1)) print(nand_gate(1, 0)) print(nand_gate(1, 1))
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"""simple docstring""" import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x2_0000 and cp <= 0x2_A6DF) # or (cp >= 0x2_A700 and cp <= 0x2_B73F) # or (cp >= 0x2_B740 and cp <= 0x2_B81F) # or (cp >= 0x2_B820 and cp <= 0x2_CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2_F800 and cp <= 0x2_FA1F) # ): # return True return False def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" for char in word: _UpperCamelCase = ord(__snake_case ) if not _is_chinese_char(__snake_case ): return 0 return 1 def lowerCamelCase__ ( __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = set() for token in tokens: _UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case ) if chinese_word: word_set.add(__snake_case ) _UpperCamelCase = list(__snake_case ) return word_list def lowerCamelCase__ ( __snake_case, __snake_case ) -> int: """simple docstring""" if not chinese_word_set: return bert_tokens _UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] ) _UpperCamelCase = bert_tokens _UpperCamelCase , _UpperCamelCase = 0, len(__snake_case ) while start < end: _UpperCamelCase = True if is_chinese(bert_word[start] ): _UpperCamelCase = min(end - start, __snake_case ) for i in range(__snake_case, 1, -1 ): _UpperCamelCase = ''''''.join(bert_word[start : start + i] ) if whole_word in chinese_word_set: for j in range(start + 1, start + i ): _UpperCamelCase = '''##''' + bert_word[j] _UpperCamelCase = start + i _UpperCamelCase = False break if single_word: start += 1 return bert_word def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws _UpperCamelCase = [get_chinese_word(__snake_case ) for r in res] ltp_res.extend(__snake_case ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 ) bert_res.extend(res['''input_ids'''] ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for input_ids, chinese_word in zip(__snake_case, __snake_case ): _UpperCamelCase = [] for id in input_ids: _UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case ) input_tokens.append(__snake_case ) _UpperCamelCase = add_sub_symbol(__snake_case, __snake_case ) _UpperCamelCase = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(__snake_case ): if token[:2] == "##": _UpperCamelCase = token[2:] # save chinese tokens' pos if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ): ref_id.append(__snake_case ) ref_ids.append(__snake_case ) assert len(__snake_case ) == len(__snake_case ) return ref_ids def lowerCamelCase__ ( __snake_case ) -> Optional[int]: """simple docstring""" with open(args.file_name, '''r''', encoding='''utf-8''' ) as f: _UpperCamelCase = f.readlines() _UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029' _UpperCamelCase = LTP(args.ltp ) # faster in GPU device _UpperCamelCase = BertTokenizer.from_pretrained(args.bert ) _UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case ) with open(args.save_path, '''w''', encoding='''utf-8''' ) as f: _UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids] f.writelines(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser(description="""prepare_chinese_ref""") parser.add_argument( """--file_name""", required=False, type=str, default="""./resources/chinese-demo.txt""", help="""file need process, same as training data in lm""", ) parser.add_argument( """--ltp""", required=False, type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path""", ) parser.add_argument( """--bert""", required=False, type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""", ) parser.add_argument( """--save_path""", required=False, type=str, default="""./resources/ref.txt""", help="""path to save res""", ) _a = parser.parse_args() main(args)
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import math def __lowerCamelCase (UpperCAmelCase__ : Dict ): assert isinstance(__snake_case , __snake_case ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or not number % 2: # Negatives, 0, 1 and all even numbers are not primes return False SCREAMING_SNAKE_CASE = range(3 , int(math.sqrt(__snake_case ) + 1 ) , 2 ) return not any(not number % i for i in odd_numbers ) def __lowerCamelCase (UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any=1 , **UpperCAmelCase__ : List[Any] ): SCREAMING_SNAKE_CASE = factor * value SCREAMING_SNAKE_CASE = value while not is_prime(__snake_case ): value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1 if value == first_value_val: return next_prime(value + 1 , **__snake_case ) return value
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"""simple docstring""" import heapq def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" _UpperCamelCase = [] # for each node and his adjacency list add them and the rank of the node to queue # using heapq module the queue will be filled like a Priority Queue # heapq works with a min priority queue, so I used -1*len(v) to build it for key, value in graph.items(): # O(log(n)) heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] ) # chosen_vertices = set of chosen vertices _UpperCamelCase = set() # while queue isn't empty and there are still edges # (queue[0][0] is the rank of the node with max rank) while queue and queue[0][0] != 0: # extract vertex with max rank from queue and add it to chosen_vertices _UpperCamelCase = heapq.heappop(__snake_case )[1][0] chosen_vertices.add(__snake_case ) # Remove all arcs adjacent to argmax for elem in queue: # if v haven't adjacent node, skip if elem[0] == 0: continue # if argmax is reachable from elem # remove argmax from elem's adjacent list and update his rank if argmax in elem[1][1]: _UpperCamelCase = elem[1][1].index(__snake_case ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(__snake_case ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() _a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
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'''simple docstring''' import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...utils import logging _snake_case : Dict = logging.get_logger(__name__) _snake_case : str = { 'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json', 'BridgeTower/bridgetower-base-itm-mlm': ( 'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json' ), } class A ( _a ): lowercase_ = 'bridgetower_vision_model' def __init__( self : Tuple , lowerCAmelCase_ : Tuple=7_68 , lowerCAmelCase_ : Dict=12 , lowerCAmelCase_ : Tuple=3 , lowerCAmelCase_ : Optional[Any]=16 , lowerCAmelCase_ : Any=2_88 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[int]=1e-05 , lowerCAmelCase_ : str=False , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : str=False , **lowerCAmelCase_ : List[str] , ) -> Union[str, Any]: """simple docstring""" super().__init__(**__a ) _a = hidden_size _a = num_hidden_layers _a = num_channels _a = patch_size _a = image_size _a = initializer_factor _a = layer_norm_eps _a = stop_gradient _a = share_layernorm _a = remove_last_layer @classmethod def __lowerCAmelCase ( cls : str , lowerCAmelCase_ : List[str] , **lowerCAmelCase_ : Optional[Any] ) -> "PretrainedConfig": """simple docstring""" _a , _a = cls.get_config_dict(__a , **__a ) if config_dict.get('''model_type''' ) == "bridgetower": _a = config_dict['''text_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(__a , **__a ) class A ( _a ): lowercase_ = 'bridgetower_text_model' def __init__( self : List[Any] , lowerCAmelCase_ : Any=5_02_65 , lowerCAmelCase_ : int=7_68 , lowerCAmelCase_ : str=12 , lowerCAmelCase_ : Tuple=12 , lowerCAmelCase_ : Union[str, Any]=1 , lowerCAmelCase_ : Union[str, Any]=30_72 , lowerCAmelCase_ : Union[str, Any]="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : int=5_14 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : Any=1e-05 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[Any]=0 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : int="absolute" , lowerCAmelCase_ : Dict=True , **lowerCAmelCase_ : str , ) -> Any: """simple docstring""" super().__init__(**__a ) _a = vocab_size _a = hidden_size _a = num_hidden_layers _a = num_attention_heads _a = hidden_act _a = initializer_factor _a = intermediate_size _a = hidden_dropout_prob _a = attention_probs_dropout_prob _a = max_position_embeddings _a = type_vocab_size _a = layer_norm_eps _a = position_embedding_type _a = use_cache _a = pad_token_id _a = bos_token_id _a = eos_token_id @classmethod def __lowerCAmelCase ( cls : Tuple , lowerCAmelCase_ : List[str] , **lowerCAmelCase_ : Optional[int] ) -> "PretrainedConfig": """simple docstring""" _a , _a = cls.get_config_dict(__a , **__a ) if config_dict.get('''model_type''' ) == "bridgetower": _a = config_dict['''text_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(__a , **__a ) class A ( _a ): lowercase_ = 'bridgetower' def __init__( self : int , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Union[str, Any]=7_68 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : int=1e-05 , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : Optional[Any]="add" , lowerCAmelCase_ : List[str]=12 , lowerCAmelCase_ : Any=6 , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Dict=None , **lowerCAmelCase_ : Any , ) -> Optional[int]: """simple docstring""" _a = kwargs.pop('''text_config_dict''' , __a ) _a = kwargs.pop('''vision_config_dict''' , __a ) super().__init__(**__a ) _a = share_cross_modal_transformer_layers _a = hidden_act _a = hidden_size _a = initializer_factor _a = layer_norm_eps _a = share_link_tower_layers _a = link_tower_type _a = num_attention_heads _a = num_hidden_layers _a = tie_word_embeddings _a = init_layernorm_from_vision_encoder if text_config is None: _a = {} logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.''' ) if vision_config is None: _a = {} logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.''' ) _a = BridgeTowerTextConfig(**__a ) _a = BridgeTowerVisionConfig(**__a ) @classmethod def __lowerCAmelCase ( cls : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : str ) -> str: """simple docstring""" return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__a ) def __lowerCAmelCase ( self : List[str] ) -> Any: """simple docstring""" _a = copy.deepcopy(self.__dict__ ) _a = self.text_config.to_dict() _a = self.vision_config.to_dict() _a = self.__class__.model_type return output
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"""simple docstring""" from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join _UpperCamelCase = '''__test_patch_submodule_mock__''' with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os, _PatchedModuleObj ) assert isinstance(_test_patching.os.path, _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path, _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os, _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path, _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" assert _test_patching.open is open _UpperCamelCase = '''__test_patch_submodule_builtin_mock__''' # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching, '''open''', __snake_case ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def lowerCamelCase__ ( ) -> Union[str, Any]: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_mock__''' with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ): pass def lowerCamelCase__ ( ) -> Dict: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__''' # _test_patching doesn't have "len" in its globals assert getattr(_test_patching, '''len''', __snake_case ) is None with patch_submodule(_test_patching, '''len''', __snake_case ): assert _test_patching.len is mock assert _test_patching.len is len def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__''' _UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def lowerCamelCase__ ( ) -> Optional[int]: """simple docstring""" from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join _UpperCamelCase = '''__test_patch_submodule_successive_join__''' _UpperCamelCase = '''__test_patch_submodule_successive_dirname__''' _UpperCamelCase = '''__test_patch_submodule_successive_rename__''' assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def lowerCamelCase__ ( ) -> str: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__''' with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ): pass with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ): pass
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'''simple docstring''' import random def __a ( A__ , A__ , A__ ) -> Dict: lowerCAmelCase = a[left_index] lowerCAmelCase = left_index + 1 for j in range(left_index + 1 , __snake_case ): if a[j] < pivot: lowerCAmelCase , lowerCAmelCase = a[i], a[j] i += 1 lowerCAmelCase , lowerCAmelCase = a[i - 1], a[left_index] return i - 1 def __a ( A__ , A__ , A__ ) -> Tuple: if left < right: lowerCAmelCase = random.randint(__snake_case , right - 1 ) lowerCAmelCase , lowerCAmelCase = ( a[left], a[pivot], ) # switches the pivot with the left most bound lowerCAmelCase = partition(__snake_case , __snake_case , __snake_case ) quick_sort_random( __snake_case , __snake_case , __snake_case ) # recursive quicksort to the left of the pivot point quick_sort_random( __snake_case , pivot_index + 1 , __snake_case ) # recursive quicksort to the right of the pivot point def __a ( ) -> str: lowerCAmelCase = input("Enter numbers separated by a comma:\n" ).strip() lowerCAmelCase = [int(__snake_case ) for item in user_input.split("," )] quick_sort_random(__snake_case , 0 , len(__snake_case ) ) print(__snake_case ) if __name__ == "__main__": main()
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"""simple docstring""" import argparse import json from collections import OrderedDict from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor from transformers.utils import logging logging.set_verbosity_info() _a = logging.get_logger(__name__) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = original_name.split('''.''' )[0] _UpperCamelCase = key.split('''.''' ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] ) _UpperCamelCase = orig_block_num - offset _UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' ) return key def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase = OrderedDict() _UpperCamelCase , _UpperCamelCase = 0, 0 for key, value in state_dict.items(): if key.startswith('''network''' ): _UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' ) if "proj" in key: # Works for the first embedding as well as the internal embedding layers if key.endswith('''bias''' ) and "patch_embed" not in key: patch_emb_offset += 1 _UpperCamelCase = key[: key.find('''proj''' )] _UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' ) _UpperCamelCase = key.replace('''proj''', '''projection''' ) if key.endswith('''bias''' ): total_embed_found += 1 if "patch_embeddings" in key: _UpperCamelCase = '''poolformer.encoder.''' + key if "mlp.fc1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' ) if "mlp.fc2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' ) if "norm1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' ) if "norm2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' ) if "layer_scale_1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' ) if "layer_scale_2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' ) if "head" in key: _UpperCamelCase = key.replace('''head''', '''classifier''' ) _UpperCamelCase = value return new_state_dict def lowerCamelCase__ ( ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg''' _UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw ) return image @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int: """simple docstring""" _UpperCamelCase = PoolFormerConfig() # set attributes based on model_name _UpperCamelCase = '''huggingface/label-files''' _UpperCamelCase = model_name[-3:] _UpperCamelCase = 10_00 _UpperCamelCase = '''imagenet-1k-id2label.json''' _UpperCamelCase = (1, 10_00) # set config attributes _UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) ) _UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()} _UpperCamelCase = idalabel _UpperCamelCase = {v: k for k, v in idalabel.items()} if size == "s12": _UpperCamelCase = [2, 2, 6, 2] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s24": _UpperCamelCase = [4, 4, 12, 4] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.9 elif size == "m36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 elif size == "m48": _UpperCamelCase = [8, 8, 24, 8] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 else: raise ValueError(F'''Size {size} not supported''' ) # load image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) # Prepare image _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values logger.info(F'''Converting model {model_name}...''' ) # load original state dict _UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) ) # rename keys _UpperCamelCase = rename_keys(__snake_case ) # create HuggingFace model and load state dict _UpperCamelCase = PoolFormerForImageClassification(__snake_case ) model.load_state_dict(__snake_case ) model.eval() # Define image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) _UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values # forward pass _UpperCamelCase = model(__snake_case ) _UpperCamelCase = outputs.logits # define expected logit slices for different models if size == "s12": _UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] ) elif size == "s24": _UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] ) elif size == "s36": _UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] ) elif size == "m36": _UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] ) elif size == "m48": _UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] ) else: raise ValueError(F'''Size {size} not supported''' ) # verify logits assert logits.shape == expected_shape assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 ) # finally, save model and image processor logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' ) Path(__snake_case ).mkdir(exist_ok=__snake_case ) model.save_pretrained(__snake_case ) print(F'''Saving image processor to {pytorch_dump_folder_path}''' ) image_processor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument( """--model_name""", default="""poolformer_s12""", type=str, help="""Name of the model you'd like to convert.""", ) parser.add_argument( """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file).""" ) parser.add_argument( """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model.""" ) _a = parser.parse_args() convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
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0
'''simple docstring''' import numpy as np from transformers import BatchFeature from transformers.testing_utils import require_tf, require_torch from .test_feature_extraction_common import FeatureExtractionSavingTestMixin class _lowercase ( lowerCAmelCase ): '''simple docstring''' UpperCAmelCase_ : List[Any] = None UpperCAmelCase_ : Tuple = None @property def lowerCAmelCase__ ( self ) -> List[str]: '''simple docstring''' return self.feat_extract_tester.prepare_feat_extract_dict() def lowerCAmelCase__ ( self ) -> List[str]: '''simple docstring''' UpperCAmelCase__ : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict ) self.assertTrue(hasattr(__a ,'''feature_size''' ) ) self.assertTrue(hasattr(__a ,'''sampling_rate''' ) ) self.assertTrue(hasattr(__a ,'''padding_value''' ) ) def lowerCAmelCase__ ( self ) -> Optional[int]: '''simple docstring''' UpperCAmelCase__ : int = self.feat_extract_tester.prepare_inputs_for_common() UpperCAmelCase__ : Any = self.feature_extraction_class(**self.feat_extract_dict ) UpperCAmelCase__ : Optional[Any] = feat_extract.model_input_names[0] UpperCAmelCase__ : List[str] = BatchFeature({input_name: speech_inputs} ) self.assertTrue(all(len(__a ) == len(__a ) for x, y in zip(__a ,processed_features[input_name] ) ) ) UpperCAmelCase__ : int = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a ) UpperCAmelCase__ : int = BatchFeature({input_name: speech_inputs} ,tensor_type='''np''' ) UpperCAmelCase__ : Optional[int] = processed_features[input_name] if len(batch_features_input.shape ) < 3: UpperCAmelCase__ : Tuple = batch_features_input[:, :, None] self.assertTrue( batch_features_input.shape == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) ) @require_torch def lowerCAmelCase__ ( self ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a ) UpperCAmelCase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict ) UpperCAmelCase__ : Any = feat_extract.model_input_names[0] UpperCAmelCase__ : List[str] = BatchFeature({input_name: speech_inputs} ,tensor_type='''pt''' ) UpperCAmelCase__ : Optional[Any] = processed_features[input_name] if len(batch_features_input.shape ) < 3: UpperCAmelCase__ : List[Any] = batch_features_input[:, :, None] self.assertTrue( batch_features_input.shape == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) ) @require_tf def lowerCAmelCase__ ( self ) -> Tuple: '''simple docstring''' UpperCAmelCase__ : List[Any] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a ) UpperCAmelCase__ : str = self.feature_extraction_class(**self.feat_extract_dict ) UpperCAmelCase__ : Optional[int] = feat_extract.model_input_names[0] UpperCAmelCase__ : List[Any] = BatchFeature({input_name: speech_inputs} ,tensor_type='''tf''' ) UpperCAmelCase__ : Optional[int] = processed_features[input_name] if len(batch_features_input.shape ) < 3: UpperCAmelCase__ : Tuple = batch_features_input[:, :, None] self.assertTrue( batch_features_input.shape == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) ) def lowerCAmelCase__ ( self ,lowerCamelCase_=False ) -> Union[str, Any]: '''simple docstring''' def _inputs_have_equal_length(lowerCamelCase_ ): UpperCAmelCase__ : Optional[int] = len(input[0] ) for input_slice in input[1:]: if len(__a ) != length: return False return True def _inputs_are_equal(lowerCamelCase_ ,lowerCamelCase_ ): if len(__a ) != len(__a ): return False for input_slice_a, input_slice_a in zip(__a ,__a ): if not np.allclose(np.asarray(__a ) ,np.asarray(__a ) ,atol=1e-3 ): return False return True UpperCAmelCase__ : List[str] = self.feature_extraction_class(**self.feat_extract_dict ) UpperCAmelCase__ : List[str] = self.feat_extract_tester.prepare_inputs_for_common(numpify=__a ) UpperCAmelCase__ : Dict = feat_extract.model_input_names[0] UpperCAmelCase__ : Dict = BatchFeature({input_name: speech_inputs} ) UpperCAmelCase__ : List[Any] = self.feat_extract_tester.seq_length_diff UpperCAmelCase__ : Tuple = self.feat_extract_tester.max_seq_length + pad_diff UpperCAmelCase__ : List[str] = self.feat_extract_tester.min_seq_length UpperCAmelCase__ : Dict = self.feat_extract_tester.batch_size UpperCAmelCase__ : List[Any] = self.feat_extract_tester.feature_size # test padding for List[int] + numpy UpperCAmelCase__ : Tuple = feat_extract.pad(__a ,padding=__a ) UpperCAmelCase__ : List[Any] = input_a[input_name] UpperCAmelCase__ : Dict = feat_extract.pad(__a ,padding='''longest''' ) UpperCAmelCase__ : List[str] = input_a[input_name] UpperCAmelCase__ : str = feat_extract.pad(__a ,padding='''max_length''' ,max_length=len(speech_inputs[-1] ) ) UpperCAmelCase__ : Optional[Any] = input_a[input_name] UpperCAmelCase__ : Union[str, Any] = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' ) UpperCAmelCase__ : List[str] = input_a[input_name] # max_length parameter has to be provided when setting `padding="max_length"` with self.assertRaises(__a ): feat_extract.pad(__a ,padding='''max_length''' )[input_name] UpperCAmelCase__ : List[str] = feat_extract.pad( __a ,padding='''max_length''' ,max_length=__a ,return_tensors='''np''' ) UpperCAmelCase__ : Optional[int] = input_a[input_name] self.assertFalse(_inputs_have_equal_length(__a ) ) self.assertTrue(_inputs_have_equal_length(__a ) ) self.assertTrue(_inputs_have_equal_length(__a ) ) self.assertTrue(_inputs_are_equal(__a ,__a ) ) self.assertTrue(len(input_a[0] ) == pad_min_length ) self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff ) self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) ) self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) ) if feature_size > 1: self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size ) # test padding for `pad_to_multiple_of` for List[int] + numpy UpperCAmelCase__ : List[Any] = feat_extract.pad(__a ,pad_to_multiple_of=10 ) UpperCAmelCase__ : List[str] = input_a[input_name] UpperCAmelCase__ : Optional[int] = feat_extract.pad(__a ,padding='''longest''' ,pad_to_multiple_of=10 ) UpperCAmelCase__ : Tuple = input_a[input_name] UpperCAmelCase__ : Optional[Any] = feat_extract.pad( __a ,padding='''max_length''' ,pad_to_multiple_of=10 ,max_length=__a ) UpperCAmelCase__ : List[str] = input_a[input_name] UpperCAmelCase__ : int = feat_extract.pad( __a ,padding='''max_length''' ,pad_to_multiple_of=10 ,max_length=__a ,return_tensors='''np''' ,) UpperCAmelCase__ : Union[str, Any] = input_a[input_name] self.assertTrue(all(len(__a ) % 10 == 0 for x in input_a ) ) self.assertTrue(_inputs_are_equal(__a ,__a ) ) UpperCAmelCase__ : Union[str, Any] = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10 self.assertTrue(all(len(__a ) == expected_mult_pad_length for x in input_a ) ) self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) ) if feature_size > 1: self.assertTrue(input_a.shape[2] == feature_size ) # Check padding value is correct UpperCAmelCase__ : Union[str, Any] = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum() self.assertTrue( abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 ) self.assertTrue( abs( np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum() - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) ) < 1e-3 ) self.assertTrue( abs( np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum() - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) ) < 1e-3 ) self.assertTrue( abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 ) self.assertTrue( abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) ) < 1e-3 ) def lowerCAmelCase__ ( self ,lowerCamelCase_=False ) -> List[Any]: '''simple docstring''' def _inputs_have_equal_length(lowerCamelCase_ ): UpperCAmelCase__ : Dict = len(input[0] ) for input_slice in input[1:]: if len(__a ) != length: return False return True def _inputs_are_equal(lowerCamelCase_ ,lowerCamelCase_ ): if len(__a ) != len(__a ): return False for input_slice_a, input_slice_a in zip(__a ,__a ): if not np.allclose(np.asarray(__a ) ,np.asarray(__a ) ,atol=1e-3 ): return False return True UpperCAmelCase__ : Any = self.feature_extraction_class(**self.feat_extract_dict ) UpperCAmelCase__ : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common(numpify=__a ) UpperCAmelCase__ : int = feat_extract.model_input_names[0] UpperCAmelCase__ : Optional[Any] = BatchFeature({input_name: speech_inputs} ) # truncate to smallest UpperCAmelCase__ : List[str] = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,truncation=__a ) UpperCAmelCase__ : List[Any] = input_a[input_name] UpperCAmelCase__ : str = feat_extract.pad(__a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ) UpperCAmelCase__ : Tuple = input_a[input_name] self.assertTrue(_inputs_have_equal_length(__a ) ) self.assertFalse(_inputs_have_equal_length(__a ) ) # truncate to smallest with np UpperCAmelCase__ : Dict = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,return_tensors='''np''' ,truncation=__a ,) UpperCAmelCase__ : Tuple = input_a[input_name] UpperCAmelCase__ : Union[str, Any] = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,return_tensors='''np''' ) UpperCAmelCase__ : Optional[Any] = input_a[input_name] self.assertTrue(_inputs_have_equal_length(__a ) ) self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) ) # since truncation forces padding to be smaller than longest input # function can't return `np.ndarray`, but has to return list self.assertFalse(_inputs_have_equal_length(__a ) ) # truncate to middle UpperCAmelCase__ : int = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[1] ) ,truncation=__a ,return_tensors='''np''' ,) UpperCAmelCase__ : Dict = input_a[input_name] UpperCAmelCase__ : Optional[int] = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[1] ) ,truncation=__a ) UpperCAmelCase__ : str = input_a[input_name] UpperCAmelCase__ : List[str] = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[1] ) ,return_tensors='''np''' ) UpperCAmelCase__ : Union[str, Any] = input_a[input_name] self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) ) self.assertTrue(_inputs_have_equal_length(__a ) ) self.assertTrue(_inputs_have_equal_length(__a ) ) self.assertTrue(_inputs_are_equal(__a ,__a ) ) # since truncation forces padding to be smaller than longest input # function can't return `np.ndarray`, but has to return list self.assertFalse(_inputs_have_equal_length(__a ) ) self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) ) # padding has to be max_length when setting `truncation=True` with self.assertRaises(__a ): feat_extract.pad(__a ,truncation=__a )[input_name] # padding has to be max_length when setting `truncation=True` with self.assertRaises(__a ): feat_extract.pad(__a ,padding='''longest''' ,truncation=__a )[input_name] # padding has to be max_length when setting `truncation=True` with self.assertRaises(__a ): feat_extract.pad(__a ,padding='''longest''' ,truncation=__a )[input_name] # max_length parameter has to be provided when setting `truncation=True` and padding="max_length" with self.assertRaises(__a ): feat_extract.pad(__a ,padding='''max_length''' ,truncation=__a )[input_name] # test truncation for `pad_to_multiple_of` for List[int] + numpy UpperCAmelCase__ : str = 12 UpperCAmelCase__ : Any = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=__a ,truncation=__a ,) UpperCAmelCase__ : Tuple = input_a[input_name] UpperCAmelCase__ : str = feat_extract.pad( __a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=__a ,) UpperCAmelCase__ : Optional[Any] = input_a[input_name] # retrieve expected_length as multiple of pad_to_multiple_of UpperCAmelCase__ : str = len(speech_inputs[0] ) if expected_length % pad_to_multiple_of != 0: UpperCAmelCase__ : Any = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of self.assertTrue(len(input_a[0] ) == expected_length ) self.assertTrue(_inputs_have_equal_length(__a ) ) self.assertFalse(_inputs_have_equal_length(__a ) ) def lowerCAmelCase__ ( self ) -> str: '''simple docstring''' self._check_padding(numpify=__a ) def lowerCAmelCase__ ( self ) -> List[str]: '''simple docstring''' self._check_padding(numpify=__a ) def lowerCAmelCase__ ( self ) -> str: '''simple docstring''' self._check_truncation(numpify=__a ) def lowerCAmelCase__ ( self ) -> Any: '''simple docstring''' self._check_truncation(numpify=__a ) @require_torch def lowerCAmelCase__ ( self ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict ) UpperCAmelCase__ : int = self.feat_extract_tester.prepare_inputs_for_common() UpperCAmelCase__ : Any = feat_extract.model_input_names[0] UpperCAmelCase__ : List[Any] = BatchFeature({input_name: speech_inputs} ) UpperCAmelCase__ : int = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' )[input_name] UpperCAmelCase__ : str = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''pt''' )[input_name] self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 ) @require_tf def lowerCAmelCase__ ( self ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase__ : str = self.feature_extraction_class(**self.feat_extract_dict ) UpperCAmelCase__ : Dict = self.feat_extract_tester.prepare_inputs_for_common() UpperCAmelCase__ : Tuple = feat_extract.model_input_names[0] UpperCAmelCase__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} ) UpperCAmelCase__ : Any = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' )[input_name] UpperCAmelCase__ : Union[str, Any] = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''tf''' )[input_name] self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 ) def lowerCAmelCase__ ( self ) -> Any: '''simple docstring''' UpperCAmelCase__ : Union[str, Any] = self.feat_extract_dict UpperCAmelCase__ : str = True UpperCAmelCase__ : Union[str, Any] = self.feature_extraction_class(**__a ) UpperCAmelCase__ : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common() UpperCAmelCase__ : Dict = [len(__a ) for x in speech_inputs] UpperCAmelCase__ : Optional[Any] = feat_extract.model_input_names[0] UpperCAmelCase__ : int = BatchFeature({input_name: speech_inputs} ) UpperCAmelCase__ : Optional[Any] = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' ) self.assertIn('''attention_mask''' ,__a ) self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) ) self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,__a ) def lowerCAmelCase__ ( self ) -> List[str]: '''simple docstring''' UpperCAmelCase__ : Union[str, Any] = self.feat_extract_dict UpperCAmelCase__ : Union[str, Any] = True UpperCAmelCase__ : int = self.feature_extraction_class(**__a ) UpperCAmelCase__ : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common() UpperCAmelCase__ : Optional[int] = [len(__a ) for x in speech_inputs] UpperCAmelCase__ : Union[str, Any] = feat_extract.model_input_names[0] UpperCAmelCase__ : Optional[Any] = BatchFeature({input_name: speech_inputs} ) UpperCAmelCase__ : List[str] = min(__a ) UpperCAmelCase__ : Optional[Any] = feat_extract.pad( __a ,padding='''max_length''' ,max_length=__a ,truncation=__a ,return_tensors='''np''' ) self.assertIn('''attention_mask''' ,__a ) self.assertListEqual( list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] ) self.assertListEqual( processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
614
"""simple docstring""" import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils import torch_device from diffusers.utils.testing_utils import require_torchsde from .test_schedulers import SchedulerCommonTest @require_torchsde class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DPMSolverSDEScheduler,) lowercase__ = 10 def UpperCAmelCase ( self , **__a) -> int: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 11_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''noise_sampler_seed''': 0, } config.update(**__a) return config def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' for timesteps in [10, 50, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> str: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2 assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2 assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2 assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3 else: assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2 assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2 assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 else: assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
19
0
'''simple docstring''' lowercase =[0, 2, 4, 6, 8] lowercase =[1, 3, 5, 7, 9] def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any] , __lowerCamelCase : int ): '''simple docstring''' if remaining_length == 0: if digits[0] == 0 or digits[-1] == 0: return 0 for i in range(length // 2 - 1 , -1 , -1 ): remainder += digits[i] + digits[length - i - 1] if remainder % 2 == 0: return 0 remainder //= 1_0 return 1 if remaining_length == 1: if remainder % 2 == 0: return 0 _UpperCAmelCase : Any =0 for digit in range(1_0 ): _UpperCAmelCase : List[str] =digit result += reversible_numbers( 0 , (remainder + 2 * digit) // 1_0 , __snake_case , __snake_case ) return result _UpperCAmelCase : str =0 for digita in range(1_0 ): _UpperCAmelCase : List[Any] =digita if (remainder + digita) % 2 == 0: _UpperCAmelCase : str =ODD_DIGITS else: _UpperCAmelCase : Tuple =EVEN_DIGITS for digita in other_parity_digits: _UpperCAmelCase : Any =digita result += reversible_numbers( remaining_length - 2 , (remainder + digita + digita) // 1_0 , __snake_case , __snake_case , ) return result def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] = 9 ): '''simple docstring''' _UpperCAmelCase : Union[str, Any] =0 for length in range(1 , max_power + 1 ): result += reversible_numbers(__snake_case , 0 , [0] * length , __snake_case ) return result if __name__ == "__main__": print(F"""{solution() = }""")
446
"""simple docstring""" from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images, ) from ...utils import TensorType, logging _a = logging.get_logger(__name__) class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['pixel_values'] def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None: '''simple docstring''' super().__init__(**__a) _UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a) _UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''') _UpperCamelCase = do_resize _UpperCamelCase = do_rescale _UpperCamelCase = do_normalize _UpperCamelCase = do_center_crop _UpperCamelCase = crop_size _UpperCamelCase = size _UpperCamelCase = resample _UpperCamelCase = rescale_factor _UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN _UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "shortest_edge" in size: _UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a) # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"]) elif "height" in size and "width" in size: _UpperCamelCase = (size['''height'''], size['''width''']) else: raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''') return resize(__a , size=__a , resample=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "height" not in size or "width" not in size: raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''') return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray: '''simple docstring''' return rescale(__a , scale=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' return normalize(__a , mean=__a , std=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature: '''simple docstring''' _UpperCamelCase = do_resize if do_resize is not None else self.do_resize _UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale _UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize _UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop _UpperCamelCase = crop_size if crop_size is not None else self.crop_size _UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a) _UpperCamelCase = resample if resample is not None else self.resample _UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor _UpperCamelCase = image_mean if image_mean is not None else self.image_mean _UpperCamelCase = image_std if image_std is not None else self.image_std _UpperCamelCase = size if size is not None else self.size _UpperCamelCase = get_size_dict(__a) if not is_batched(__a): _UpperCamelCase = [images] if not valid_images(__a): raise ValueError( '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, ''' '''torch.Tensor, tf.Tensor or jax.ndarray.''') if do_resize and size is None: raise ValueError('''Size must be specified if do_resize is True.''') if do_center_crop and crop_size is None: raise ValueError('''Crop size must be specified if do_center_crop is True.''') if do_rescale and rescale_factor is None: raise ValueError('''Rescale factor must be specified if do_rescale is True.''') # All transformations expect numpy arrays. _UpperCamelCase = [to_numpy_array(__a) for image in images] if do_resize: _UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images] if do_center_crop: _UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images] if do_rescale: _UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images] if do_normalize: _UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images] _UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images] _UpperCamelCase = {'''pixel_values''': images} return BatchFeature(data=__a , tensor_type=__a)
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from __future__ import annotations from fractions import Fraction from math import gcd, sqrt def _a ( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> bool: '''simple docstring''' SCREAMING_SNAKE_CASE__ : int = int(number**0.5 ) return number == sq * sq def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> tuple[int, int]: '''simple docstring''' SCREAMING_SNAKE_CASE__ : Dict = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den SCREAMING_SNAKE_CASE__ : Optional[int] = x_den * y_den * z_den SCREAMING_SNAKE_CASE__ : Dict = gcd(__snake_case , __snake_case ) top //= hcf bottom //= hcf return top, bottom def _a ( SCREAMING_SNAKE_CASE__ : str = 35 ) -> int: '''simple docstring''' SCREAMING_SNAKE_CASE__ : Union[str, Any] = set() SCREAMING_SNAKE_CASE__ : Tuple = 42 SCREAMING_SNAKE_CASE__ : Optional[int] = Fraction(0 ) SCREAMING_SNAKE_CASE__ : str = 42 for x_num in range(1 , order + 1 ): for x_den in range(x_num + 1 , order + 1 ): for y_num in range(1 , order + 1 ): for y_den in range(y_num + 1 , order + 1 ): # n=1 SCREAMING_SNAKE_CASE__ : Tuple = x_num * y_den + x_den * y_num SCREAMING_SNAKE_CASE__ : Optional[int] = x_den * y_den SCREAMING_SNAKE_CASE__ : Tuple = gcd(__snake_case , __snake_case ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE__ : Optional[int] = add_three( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) unique_s.add(__snake_case ) # n=2 SCREAMING_SNAKE_CASE__ : str = ( x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num ) SCREAMING_SNAKE_CASE__ : Optional[Any] = x_den * x_den * y_den * y_den if is_sq(__snake_case ) and is_sq(__snake_case ): SCREAMING_SNAKE_CASE__ : List[str] = int(sqrt(__snake_case ) ) SCREAMING_SNAKE_CASE__ : Tuple = int(sqrt(__snake_case ) ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = gcd(__snake_case , __snake_case ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE__ : Optional[int] = add_three( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) unique_s.add(__snake_case ) # n=-1 SCREAMING_SNAKE_CASE__ : int = x_num * y_num SCREAMING_SNAKE_CASE__ : List[str] = x_den * y_num + x_num * y_den SCREAMING_SNAKE_CASE__ : int = gcd(__snake_case , __snake_case ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE__ : Any = add_three( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) unique_s.add(__snake_case ) # n=2 SCREAMING_SNAKE_CASE__ : Dict = x_num * x_num * y_num * y_num SCREAMING_SNAKE_CASE__ : Dict = ( x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den ) if is_sq(__snake_case ) and is_sq(__snake_case ): SCREAMING_SNAKE_CASE__ : int = int(sqrt(__snake_case ) ) SCREAMING_SNAKE_CASE__ : int = int(sqrt(__snake_case ) ) SCREAMING_SNAKE_CASE__ : str = gcd(__snake_case , __snake_case ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE__ : str = add_three( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) unique_s.add(__snake_case ) for num, den in unique_s: total += Fraction(__snake_case , __snake_case ) return total.denominator + total.numerator if __name__ == "__main__": print(f"{solution() = }")
663
"""simple docstring""" # Imports import numpy as np class _UpperCAmelCase: def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' if red is not None: _UpperCamelCase = red if green is not None: _UpperCamelCase = green if blue is not None: _UpperCamelCase = blue if red_edge is not None: _UpperCamelCase = red_edge if nir is not None: _UpperCamelCase = nir return True def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) _UpperCamelCase = { '''ARVI2''': self.arvaa, '''CCCI''': self.ccci, '''CVI''': self.cvi, '''GLI''': self.gli, '''NDVI''': self.ndvi, '''BNDVI''': self.bndvi, '''redEdgeNDVI''': self.red_edge_ndvi, '''GNDVI''': self.gndvi, '''GBNDVI''': self.gbndvi, '''GRNDVI''': self.grndvi, '''RBNDVI''': self.rbndvi, '''PNDVI''': self.pndvi, '''ATSAVI''': self.atsavi, '''BWDRVI''': self.bwdrvi, '''CIgreen''': self.ci_green, '''CIrededge''': self.ci_rededge, '''CI''': self.ci, '''CTVI''': self.ctvi, '''GDVI''': self.gdvi, '''EVI''': self.evi, '''GEMI''': self.gemi, '''GOSAVI''': self.gosavi, '''GSAVI''': self.gsavi, '''Hue''': self.hue, '''IVI''': self.ivi, '''IPVI''': self.ipvi, '''I''': self.i, '''RVI''': self.rvi, '''MRVI''': self.mrvi, '''MSAVI''': self.m_savi, '''NormG''': self.norm_g, '''NormNIR''': self.norm_nir, '''NormR''': self.norm_r, '''NGRDI''': self.ngrdi, '''RI''': self.ri, '''S''': self.s, '''IF''': self._if, '''DVI''': self.dvi, '''TVI''': self.tvi, '''NDRE''': self.ndre, } try: return funcs[index]() except KeyError: print('''Index not in the list!''') return False def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red))) def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / ( (self.nir - self.red) / (self.nir + self.red) ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir * (self.red / (self.green**2)) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.green - self.red - self.blue) / ( 2 * self.green + self.red + self.blue ) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - self.red) / (self.nir + self.red) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (self.nir - self.blue) / (self.nir + self.blue) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.redEdge - self.red) / (self.redEdge + self.red) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir - (self.green + self.blue)) / ( self.nir + (self.green + self.blue) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.nir - (self.green + self.red)) / ( self.nir + (self.green + self.red) ) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red)) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - (self.green + self.red + self.blue)) / ( self.nir + (self.green + self.red + self.blue) ) def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]: '''simple docstring''' return a * ( (self.nir - a * self.red - b) / (a * self.nir + self.red - a * b + x * (1 + a**2)) ) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir / self.green) - 1 def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir / self.redEdge) - 1 def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.red - self.blue) / self.red def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.ndvi() return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2)) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir - self.green def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return 2.5 * ( (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / ( self.nir + self.red + 0.5 ) return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red) def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green + y) def UpperCAmelCase ( self , __a=0.5) -> Dict: '''simple docstring''' return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return np.arctan( ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)) def UpperCAmelCase ( self , __a=None , __a=None) -> Any: '''simple docstring''' return (self.nir - b) / (a * self.red) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.red + self.green + self.blue) / 30.5 def UpperCAmelCase ( self) -> Any: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.rvi() - 1) / (self.rvi() + 1) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return ( (2 * self.nir + 1) - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2) ) / 2 def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.green / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> str: '''simple docstring''' return self.nir / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.red / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.green - self.red) / (self.green + self.red) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (self.red - self.green) / (self.red + self.green) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)]) _UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)]) return (max_value - min_value) / max_value def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.red - self.green - self.blue) / (self.green - self.blue) def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Any: '''simple docstring''' return (self.ndvi() + 0.5) ** (1 / 2) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.nir - self.redEdge) / (self.nir + self.redEdge)
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import json import os import tempfile import unittest import numpy as np from datasets import load_dataset from transformers.testing_utils import require_torch, require_vision, slow from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ImageGPTImageProcessor class __magic_name__ ( unittest.TestCase ): def __init__( self : int , UpperCamelCase__ : Dict , UpperCamelCase__ : int=7 , UpperCamelCase__ : Optional[int]=3 , UpperCamelCase__ : Optional[int]=18 , UpperCamelCase__ : List[Any]=30 , UpperCamelCase__ : Dict=4_00 , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Any=None , UpperCamelCase__ : Optional[Any]=True , ) -> Any: '''simple docstring''' UpperCAmelCase = size if size is not None else {"height": 18, "width": 18} UpperCAmelCase = parent UpperCAmelCase = batch_size UpperCAmelCase = num_channels UpperCAmelCase = image_size UpperCAmelCase = min_resolution UpperCAmelCase = max_resolution UpperCAmelCase = do_resize UpperCAmelCase = size UpperCAmelCase = do_normalize def SCREAMING_SNAKE_CASE_ ( self : int ) -> int: '''simple docstring''' return { # here we create 2 clusters for the sake of simplicity "clusters": np.asarray( [ [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04], [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96], ] ), "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, } @require_torch @require_vision class __magic_name__ ( A__, unittest.TestCase ): lowercase : str =ImageGPTImageProcessor if is_vision_available() else None def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = ImageGPTImageProcessingTester(self ) @property def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Dict: '''simple docstring''' return self.image_processor_tester.prepare_image_processor_dict() def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(__a , "clusters" ) ) self.assertTrue(hasattr(__a , "do_resize" ) ) self.assertTrue(hasattr(__a , "size" ) ) self.assertTrue(hasattr(__a , "do_normalize" ) ) def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Dict: '''simple docstring''' UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size , {"height": 18, "width": 18} ) UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 ) self.assertEqual(image_processor.size , {"height": 42, "width": 42} ) def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> List[Any]: '''simple docstring''' UpperCAmelCase = self.image_processing_class(**self.image_processor_dict ) UpperCAmelCase = json.loads(image_processor.to_json_string() ) for key, value in self.image_processor_dict.items(): if key == "clusters": self.assertTrue(np.array_equal(__a , obj[key] ) ) else: self.assertEqual(obj[key] , __a ) def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> List[Any]: '''simple docstring''' UpperCAmelCase = self.image_processing_class(**self.image_processor_dict ) with tempfile.TemporaryDirectory() as tmpdirname: UpperCAmelCase = os.path.join(__a , "image_processor.json" ) image_processor_first.to_json_file(__a ) UpperCAmelCase = self.image_processing_class.from_json_file(__a ).to_dict() UpperCAmelCase = image_processor_first.to_dict() for key, value in image_processor_first.items(): if key == "clusters": self.assertTrue(np.array_equal(__a , image_processor_second[key] ) ) else: self.assertEqual(image_processor_first[key] , __a ) def SCREAMING_SNAKE_CASE_ ( self : str ) -> Tuple: '''simple docstring''' UpperCAmelCase = self.image_processing_class(**self.image_processor_dict ) with tempfile.TemporaryDirectory() as tmpdirname: image_processor_first.save_pretrained(__a ) UpperCAmelCase = self.image_processing_class.from_pretrained(__a ).to_dict() UpperCAmelCase = image_processor_first.to_dict() for key, value in image_processor_first.items(): if key == "clusters": self.assertTrue(np.array_equal(__a , image_processor_second[key] ) ) else: self.assertEqual(image_processor_first[key] , __a ) @unittest.skip("ImageGPT requires clusters at initialization" ) def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]: '''simple docstring''' pass def lowerCamelCase_() -> Any: UpperCAmelCase = load_dataset("hf-internal-testing/fixtures_image_utils" , split="test" ) UpperCAmelCase = Image.open(dataset[4]["file"] ) UpperCAmelCase = Image.open(dataset[5]["file"] ) UpperCAmelCase = [imagea, imagea] return images @require_vision @require_torch class __magic_name__ ( unittest.TestCase ): @slow def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Tuple: '''simple docstring''' UpperCAmelCase = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" ) UpperCAmelCase = prepare_images() # test non-batched UpperCAmelCase = image_processing(images[0] , return_tensors="pt" ) self.assertIsInstance(encoding.input_ids , torch.LongTensor ) self.assertEqual(encoding.input_ids.shape , (1, 10_24) ) UpperCAmelCase = [3_06, 1_91, 1_91] self.assertEqual(encoding.input_ids[0, :3].tolist() , __a ) # test batched UpperCAmelCase = image_processing(__a , return_tensors="pt" ) self.assertIsInstance(encoding.input_ids , torch.LongTensor ) self.assertEqual(encoding.input_ids.shape , (2, 10_24) ) UpperCAmelCase = [3_03, 13, 13] self.assertEqual(encoding.input_ids[1, -3:].tolist() , __a )
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"""simple docstring""" import inspect import unittest from transformers import ViTHybridConfig from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = patch_size _UpperCamelCase = num_channels _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = scope _UpperCamelCase = backbone_featmap_shape # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) # the number of patches is based on the feature map of the backbone, which by default uses an output stride # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size _UpperCamelCase = (self.image_size // 32) ** 2 _UpperCamelCase = num_patches + 1 def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = { '''global_padding''': '''same''', '''layer_type''': '''bottleneck''', '''depths''': [3, 4, 9], '''out_features''': ['''stage1''', '''stage2''', '''stage3'''], '''embedding_dynamic_padding''': True, '''hidden_sizes''': [4, 8, 16, 32], '''num_groups''': 2, } return ViTHybridConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModel(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.type_sequence_label_size _UpperCamelCase = ViTHybridForImageClassification(__a) model.to(__a) model.eval() _UpperCamelCase = model(__a , labels=__a) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else () lowercase__ = ( {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification} if is_torch_available() else {} ) lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Any: '''simple docstring''' self.config_tester.run_common_tests() @unittest.skip(reason='''ViT does not use inputs_embeds''') def UpperCAmelCase ( self) -> Dict: '''simple docstring''' pass def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) self.assertIsInstance(model.get_input_embeddings() , (nn.Module)) _UpperCamelCase = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__a , nn.Linear)) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "ViTHybridPatchEmbeddings": _UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if name in backbone_params: continue self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = ViTHybridModel.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class _UpperCAmelCase( unittest.TestCase ): @cached_property def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return ( ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to( __a) _UpperCamelCase = self.default_image_processor _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a) # forward pass with torch.no_grad(): _UpperCamelCase = model(**__a) # verify the logits _UpperCamelCase = torch.Size((1, 10_00)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4)) @slow @require_accelerate def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''') _UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''') _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''') _UpperCamelCase = model(**__a) _UpperCamelCase = outputs.logits # model predicts one of the 1000 ImageNet classes _UpperCamelCase = logits.argmax(-1).item() self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
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'''simple docstring''' def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ): if graph[path[curr_ind - 1]][next_ver] == 0: return False # 2. Validate that next vertex is not already in path return not any(vertex == next_ver for vertex in path ) def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ): if curr_ind == len(__snake_case ): # return whether path exists between current and starting vertices return graph[path[curr_ind - 1]][path[0]] == 1 # Recursive Step for next_ver in range(0 , len(__snake_case ) ): if valid_connection(__snake_case , __snake_case , __snake_case , __snake_case ): # Insert current vertex into path as next transition lowercase__ : Any = next_ver # Validate created path if util_hamilton_cycle(__snake_case , __snake_case , curr_ind + 1 ): return True # Backtrack lowercase__ : List[str] = -1 return False def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase = 0 ): lowercase__ : List[Any] = [-1] * (len(__snake_case ) + 1) # initialize start and end of path with starting index lowercase__ : List[Any] = start_index # evaluate and if we find answer return path either return empty array return path if util_hamilton_cycle(__snake_case , __snake_case , 1 ) else []
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"""simple docstring""" from math import acos, sin from typing import List, Tuple, Union import numpy as np import torch from PIL import Image from ...models import AutoencoderKL, UNetaDConditionModel from ...schedulers import DDIMScheduler, DDPMScheduler from ...utils import randn_tensor from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput from .mel import Mel class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['vqvae'] def __init__( self , __a , __a , __a , __a , ) -> List[str]: '''simple docstring''' super().__init__() self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' return 50 if isinstance(self.scheduler , __a) else 10_00 @torch.no_grad() def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[ Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]], ]: '''simple docstring''' _UpperCamelCase = steps or self.get_default_steps() self.scheduler.set_timesteps(__a) _UpperCamelCase = step_generator or generator # For backwards compatibility if type(self.unet.config.sample_size) == int: _UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size) if noise is None: _UpperCamelCase = randn_tensor( ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1], ) , generator=__a , device=self.device , ) _UpperCamelCase = noise _UpperCamelCase = None if audio_file is not None or raw_audio is not None: self.mel.load_audio(__a , __a) _UpperCamelCase = self.mel.audio_slice_to_image(__a) _UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape( (input_image.height, input_image.width)) _UpperCamelCase = (input_image / 2_55) * 2 - 1 _UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device) if self.vqvae is not None: _UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample( generator=__a)[0] _UpperCamelCase = self.vqvae.config.scaling_factor * input_images if start_step > 0: _UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1]) _UpperCamelCase = ( self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length ) _UpperCamelCase = int(mask_start_secs * pixels_per_second) _UpperCamelCase = int(mask_end_secs * pixels_per_second) _UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:])) for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])): if isinstance(self.unet , __a): _UpperCamelCase = self.unet(__a , __a , __a)['''sample'''] else: _UpperCamelCase = self.unet(__a , __a)['''sample'''] if isinstance(self.scheduler , __a): _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample'''] else: _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample'''] if mask is not None: if mask_start > 0: _UpperCamelCase = mask[:, step, :, :mask_start] if mask_end > 0: _UpperCamelCase = mask[:, step, :, -mask_end:] if self.vqvae is not None: # 0.18215 was scaling factor used in training to ensure unit variance _UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images _UpperCamelCase = self.vqvae.decode(__a)['''sample'''] _UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1) _UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy() _UpperCamelCase = (images * 2_55).round().astype('''uint8''') _UpperCamelCase = list( (Image.fromarray(_[:, :, 0]) for _ in images) if images.shape[3] == 1 else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images)) _UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images] if not return_dict: return images, (self.mel.get_sample_rate(), audios) return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a)) @torch.no_grad() def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray: '''simple docstring''' assert isinstance(self.scheduler , __a) self.scheduler.set_timesteps(__a) _UpperCamelCase = np.array( [np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images]) _UpperCamelCase = (sample / 2_55) * 2 - 1 _UpperCamelCase = torch.Tensor(__a).to(self.device) for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))): _UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps _UpperCamelCase = self.scheduler.alphas_cumprod[t] _UpperCamelCase = ( self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod ) _UpperCamelCase = 1 - alpha_prod_t _UpperCamelCase = self.unet(__a , __a)['''sample'''] _UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output _UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5) _UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output return sample @staticmethod def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor: '''simple docstring''' _UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a)) return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
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import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch if is_torch_available(): import torch from transformers.activations import gelu_new, gelu_python, get_activation @require_torch class UpperCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def _snake_case ( self ): """simple docstring""" lowerCamelCase : Any = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] ) lowerCamelCase : Tuple = get_activation("gelu" ) self.assertTrue(torch.allclose(gelu_python(__a ) , torch_builtin(__a ) ) ) self.assertFalse(torch.allclose(gelu_python(__a ) , gelu_new(__a ) ) ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : int = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] ) lowerCamelCase : Optional[int] = get_activation("gelu" ) lowerCamelCase : Optional[Any] = get_activation("gelu_10" ) lowerCamelCase : str = torch_builtin(__a ) lowerCamelCase : List[Any] = geluaa(__a ) lowerCamelCase : Optional[Any] = torch.where(y_gelu_aa < 10.0 , 1 , 0 ) self.assertTrue(torch.max(__a ).item() == 10.0 ) self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) ) def _snake_case ( self ): """simple docstring""" get_activation("gelu" ) get_activation("gelu_10" ) get_activation("gelu_fast" ) get_activation("gelu_new" ) get_activation("gelu_python" ) get_activation("gelu_pytorch_tanh" ) get_activation("linear" ) get_activation("mish" ) get_activation("quick_gelu" ) get_activation("relu" ) get_activation("sigmoid" ) get_activation("silu" ) get_activation("swish" ) get_activation("tanh" ) with self.assertRaises(__a ): get_activation("bogus" ) with self.assertRaises(__a ): get_activation(__a ) def _snake_case ( self ): """simple docstring""" lowerCamelCase : Tuple = get_activation("gelu" ) lowerCamelCase : Optional[int] = 1 lowerCamelCase : List[str] = get_activation("gelu" ) self.assertEqual(acta.a , 1 ) with self.assertRaises(__a ): lowerCamelCase : List[str] = acta.a
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"""simple docstring""" import copy from collections import OrderedDict from typing import Dict, Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING _a = logging.get_logger(__name__) _a = { """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""", # See all DETR models at https://huggingface.co/models?filter=detr } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'detr' lowercase__ = ['past_key_values'] lowercase__ = { 'hidden_size': 'd_model', 'num_attention_heads': 'encoder_attention_heads', } def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int: '''simple docstring''' if backbone_config is not None and use_timm_backbone: raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''') if not use_timm_backbone: if backbone_config is None: logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''') _UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']) elif isinstance(__a , __a): _UpperCamelCase = backbone_config.get('''model_type''') _UpperCamelCase = CONFIG_MAPPING[backbone_model_type] _UpperCamelCase = config_class.from_dict(__a) # set timm attributes to None _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None _UpperCamelCase = use_timm_backbone _UpperCamelCase = backbone_config _UpperCamelCase = num_channels _UpperCamelCase = num_queries _UpperCamelCase = d_model _UpperCamelCase = encoder_ffn_dim _UpperCamelCase = encoder_layers _UpperCamelCase = encoder_attention_heads _UpperCamelCase = decoder_ffn_dim _UpperCamelCase = decoder_layers _UpperCamelCase = decoder_attention_heads _UpperCamelCase = dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = activation_function _UpperCamelCase = init_std _UpperCamelCase = init_xavier_std _UpperCamelCase = encoder_layerdrop _UpperCamelCase = decoder_layerdrop _UpperCamelCase = encoder_layers _UpperCamelCase = auxiliary_loss _UpperCamelCase = position_embedding_type _UpperCamelCase = backbone _UpperCamelCase = use_pretrained_backbone _UpperCamelCase = dilation # Hungarian matcher _UpperCamelCase = class_cost _UpperCamelCase = bbox_cost _UpperCamelCase = giou_cost # Loss coefficients _UpperCamelCase = mask_loss_coefficient _UpperCamelCase = dice_loss_coefficient _UpperCamelCase = bbox_loss_coefficient _UpperCamelCase = giou_loss_coefficient _UpperCamelCase = eos_coefficient super().__init__(is_encoder_decoder=__a , **__a) @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.encoder_attention_heads @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.d_model @classmethod def UpperCAmelCase ( cls , __a , **__a) -> int: '''simple docstring''' return cls(backbone_config=__a , **__a) def UpperCAmelCase ( self) -> Dict[str, any]: '''simple docstring''' _UpperCamelCase = copy.deepcopy(self.__dict__) if output["backbone_config"] is not None: _UpperCamelCase = self.backbone_config.to_dict() _UpperCamelCase = self.__class__.model_type return output class _UpperCAmelCase( lowerCamelCase ): lowercase__ = version.parse('1.11' ) @property def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' return OrderedDict( [ ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}), ('''pixel_mask''', {0: '''batch'''}), ]) @property def UpperCAmelCase ( self) -> float: '''simple docstring''' return 1e-5 @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return 12
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import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Audio, Features, Value from .base import TaskTemplate @dataclass(frozen=lowerCamelCase__) class lowerCamelCase__ ( lowerCamelCase__): '''simple docstring''' snake_case_ =field(default="""automatic-speech-recognition""" , metadata={"""include_in_asdict_even_if_is_default""": True}) snake_case_ =Features({"""audio""": Audio()}) snake_case_ =Features({"""transcription""": Value("""string""")}) snake_case_ ="""audio""" snake_case_ ="""transcription""" def lowerCAmelCase__ (self ,__lowerCamelCase ) -> int: """simple docstring""" if self.audio_column not in features: raise ValueError(f"""Column {self.audio_column} is not present in features.""" ) if not isinstance(features[self.audio_column] ,__a ): raise ValueError(f"""Column {self.audio_column} is not an Audio type.""" ) lowerCAmelCase__ : Optional[int] = copy.deepcopy(self ) lowerCAmelCase__ : Optional[int] = self.input_schema.copy() lowerCAmelCase__ : Optional[Any] = features[self.audio_column] lowerCAmelCase__ : Optional[int] = input_schema return task_template @property def lowerCAmelCase__ (self ) -> Dict[str, str]: """simple docstring""" return {self.audio_column: "audio", self.transcription_column: "transcription"}
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"""simple docstring""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging _a = logging.get_logger(__name__) _a = { """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""", # See all WavLM models at https://huggingface.co/models?filter=wavlm } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'wavlm' def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]: '''simple docstring''' super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a) _UpperCamelCase = hidden_size _UpperCamelCase = feat_extract_norm _UpperCamelCase = feat_extract_activation _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = conv_bias _UpperCamelCase = num_buckets _UpperCamelCase = max_bucket_distance _UpperCamelCase = num_conv_pos_embeddings _UpperCamelCase = num_conv_pos_embedding_groups _UpperCamelCase = len(self.conv_dim) _UpperCamelCase = num_hidden_layers _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = num_attention_heads _UpperCamelCase = hidden_dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = feat_proj_dropout _UpperCamelCase = final_dropout _UpperCamelCase = layerdrop _UpperCamelCase = layer_norm_eps _UpperCamelCase = initializer_range _UpperCamelCase = num_ctc_classes _UpperCamelCase = vocab_size _UpperCamelCase = do_stable_layer_norm _UpperCamelCase = use_weighted_layer_sum _UpperCamelCase = classifier_proj_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==''' ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =''' F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,''' F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''') # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _UpperCamelCase = apply_spec_augment _UpperCamelCase = mask_time_prob _UpperCamelCase = mask_time_length _UpperCamelCase = mask_time_min_masks _UpperCamelCase = mask_feature_prob _UpperCamelCase = mask_feature_length # parameters for pretraining with codevector quantized representations _UpperCamelCase = num_codevectors_per_group _UpperCamelCase = num_codevector_groups _UpperCamelCase = contrastive_logits_temperature _UpperCamelCase = num_negatives _UpperCamelCase = codevector_dim _UpperCamelCase = proj_codevector_dim _UpperCamelCase = diversity_loss_weight # ctc loss _UpperCamelCase = ctc_loss_reduction _UpperCamelCase = ctc_zero_infinity # adapter _UpperCamelCase = add_adapter _UpperCamelCase = adapter_kernel_size _UpperCamelCase = adapter_stride _UpperCamelCase = num_adapter_layers _UpperCamelCase = output_hidden_size or hidden_size # SequenceClassification-specific parameter. Feel free to ignore for other classes. _UpperCamelCase = classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = xvector_output_dim @property def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return functools.reduce(operator.mul , self.conv_stride , 1)
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'''simple docstring''' from collections import defaultdict from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst def UpperCAmelCase_ (): """simple docstring""" _a, _a : int = 9, 1_4 # noqa: F841 _a : Optional[int] = [ [0, 1, 4], [0, 7, 8], [1, 2, 8], [7, 8, 7], [7, 6, 1], [2, 8, 2], [8, 6, 6], [2, 3, 7], [2, 5, 4], [6, 5, 2], [3, 5, 1_4], [3, 4, 9], [5, 4, 1_0], [1, 7, 1_1], ] _a : List[Any] = defaultdict(__snake_case ) for nodea, nodea, cost in edges: adjancency[nodea].append([nodea, cost] ) adjancency[nodea].append([nodea, cost] ) _a : Optional[Any] = mst(__snake_case ) _a : Optional[int] = [ [7, 6, 1], [2, 8, 2], [6, 5, 2], [0, 1, 4], [2, 5, 4], [2, 3, 7], [0, 7, 8], [3, 4, 9], ] for answer in expected: _a : Any = tuple(answer[:2] ) _a : int = tuple(edge[::-1] ) assert edge in result or reverse in result
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"""simple docstring""" import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer _a = """bart""" _a = True @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Dict: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' ) _UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' ) _UpperCamelCase = qar_model.eval() else: _UpperCamelCase , _UpperCamelCase = (None, None) if MODEL_TYPE == "bart": _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' ) _UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' ) _UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' ) sas_model.load_state_dict(save_dict['''model'''] ) _UpperCamelCase = sas_model.eval() else: _UpperCamelCase , _UpperCamelCase = make_qa_sas_model( model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' ) return (qar_tokenizer, qar_model, sas_tokenizer, sas_model) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = faiss.StandardGpuResources() _UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train'''] _UpperCamelCase = np.memmap( '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) _UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case ) wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU else: _UpperCamelCase , _UpperCamelCase = (None, None) _UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] ) return (wikiaab_passages, wikiaab_gpu_index_flat, es_client) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' ) _UpperCamelCase = elia['''train_eli5'''] _UpperCamelCase = np.memmap( '''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) eli5_train_q_index.add(__snake_case ) return (elia_train, eli5_train_q_index) _a , _a , _a = load_indexes() _a , _a , _a , _a = load_models() _a , _a = load_train_data() def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]: """simple docstring""" _UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case ) _UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case ) _UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]] return nn_examples def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]: """simple docstring""" if source == "none": _UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), []) else: if method == "dense": _UpperCamelCase , _UpperCamelCase = query_qa_dense_index( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) else: _UpperCamelCase , _UpperCamelCase = query_es_index( __snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, ) _UpperCamelCase = [ (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst ] _UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case ) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda __snake_case : None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None), } ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict: """simple docstring""" with torch.no_grad(): _UpperCamelCase = qa_sas_generate( __snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0] return (answer, support_list) st.title("""Long Form Question Answering with ELI5""") # Start sidebar _a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>""" _a = """ <html> <head> <style> .img-container { padding-left: 90px; padding-right: 90px; padding-top: 50px; padding-bottom: 50px; background-color: #f0f3f9; } </style> </head> <body> <span class=\"img-container\"> <!-- Inline parent element --> %s </span> </body> </html> """ % ( header_html, ) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia _a = """ This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html). First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset, a pre-processed fixed snapshot of Wikipedia. """ st.sidebar.markdown(description, unsafe_allow_html=True) _a = [ """Answer the question""", """View the retrieved document only""", """View the most similar ELI5 question and answer""", """Show me everything, please!""", ] _a = st.sidebar.checkbox("""Demo options""") if demo_options: _a = st.sidebar.selectbox( """""", action_list, index=3, ) _a = action_list.index(action_st) _a = st.sidebar.selectbox( """""", ["""Show full text of passages""", """Show passage section titles"""], index=0, ) _a = show_type == """Show full text of passages""" else: _a = 3 _a = True _a = st.sidebar.checkbox("""Retrieval options""") if retrieval_options: _a = """ ### Information retriever options The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs. The answer is then generated by sequence to sequence model which takes the question and retrieved document as input. """ st.sidebar.markdown(retriever_info) _a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""]) _a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""]) else: _a = """wiki40b""" _a = """dense""" _a = """beam""" _a = 2 _a = 64 _a = 256 _a = None _a = None _a = st.sidebar.checkbox("""Generation options""") if generate_options: _a = """ ### Answer generation options The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large) weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with **beam** search, or **sample** from the decoder's output probabilities. """ st.sidebar.markdown(generate_info) _a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""]) _a = st.sidebar.slider( """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None ) _a = st.sidebar.slider( """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None ) if sampled == "beam": _a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: _a = st.sidebar.slider( """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) _a = st.sidebar.slider( """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) _a = None # start main text _a = [ """<MY QUESTION>""", """How do people make chocolate?""", """Why do we get a fever when we are sick?""", """How can different animals perceive different colors?""", """What is natural language processing?""", """What's the best way to treat a sunburn?""", """What exactly are vitamins ?""", """How does nuclear energy provide electricity?""", """What's the difference between viruses and bacteria?""", """Why are flutes classified as woodwinds when most of them are made out of metal ?""", """Why do people like drinking coffee even though it tastes so bad?""", """What happens when wine ages? How does it make the wine taste better?""", """If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""", """How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""", """How does New Zealand have so many large bird predators?""", ] _a = st.selectbox( """What would you like to ask? ---- select <MY QUESTION> to enter a new query""", questions_list, index=1, ) if question_s == "<MY QUESTION>": _a = st.text_input("""Enter your question here:""", """""") else: _a = question_s if st.button("""Show me!"""): if action in [0, 1, 3]: if index_type == "mixed": _a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10) _a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10) _a = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] _a = support_list[:10] _a = """<P> """ + """ <P> """.join([res[-1] for res in support_list]) else: _a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10) if action in [0, 3]: _a , _a = answer_question( question_doc, sas_model, sas_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == """sampled"""), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown("""### The model generated answer is:""") st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""") for i, res in enumerate(support_list): _a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_""")) _a = res[1].strip() if sec_titles == "": _a = """[{}]({})""".format(res[0], wiki_url) else: _a = sec_titles.split(""" & """) _a = """ & """.join( ["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list] ) st.markdown( """{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( """> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True ) if action in [2, 3]: _a = find_nearest_training(question) _a = nn_train_list[0] st.markdown( """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""]) ) _a = [ """{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""])) for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""])) if i == 0 or sc > 2 ] st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st))) _a = """ --- **Disclaimer** *The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system. Evaluating biases of such a model and ensuring factual generations are still very much open research problems. Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.* """ st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
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import re import warnings from contextlib import contextmanager from ...processing_utils import ProcessorMixin class lowercase ( a ): lowercase__ : Tuple = ["""image_processor""", """tokenizer"""] lowercase__ : Dict = """AutoImageProcessor""" lowercase__ : Union[str, Any] = """AutoTokenizer""" def __init__( self : int , _UpperCamelCase : Any=None , _UpperCamelCase : List[Any]=None , **_UpperCamelCase : Optional[int] ) -> Optional[Any]: '''simple docstring''' SCREAMING_SNAKE_CASE = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead." , __a , ) SCREAMING_SNAKE_CASE = kwargs.pop("feature_extractor" ) SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`." ) if tokenizer is None: raise ValueError("You need to specify a `tokenizer`." ) super().__init__(__a , __a ) SCREAMING_SNAKE_CASE = self.image_processor SCREAMING_SNAKE_CASE = False def __call__( self : Union[str, Any] , *_UpperCamelCase : int , **_UpperCamelCase : List[str] ) -> Optional[int]: '''simple docstring''' if self._in_target_context_manager: return self.current_processor(*__a , **__a ) SCREAMING_SNAKE_CASE = kwargs.pop("images" , __a ) SCREAMING_SNAKE_CASE = kwargs.pop("text" , __a ) if len(__a ) > 0: SCREAMING_SNAKE_CASE = args[0] SCREAMING_SNAKE_CASE = args[1:] if images is None and text is None: raise ValueError("You need to specify either an `images` or `text` input to process." ) if images is not None: SCREAMING_SNAKE_CASE = self.image_processor(__a , *__a , **__a ) if text is not None: SCREAMING_SNAKE_CASE = self.tokenizer(__a , **__a ) if text is None: return inputs elif images is None: return encodings else: SCREAMING_SNAKE_CASE = encodings["input_ids"] return inputs def __snake_case( self : Dict , *_UpperCamelCase : Union[str, Any] , **_UpperCamelCase : Dict ) -> List[Any]: '''simple docstring''' return self.tokenizer.batch_decode(*__a , **__a ) def __snake_case( self : List[str] , *_UpperCamelCase : Dict , **_UpperCamelCase : Any ) -> List[str]: '''simple docstring''' return self.tokenizer.decode(*__a , **__a ) @contextmanager def __snake_case( self : List[str] ) -> Any: '''simple docstring''' warnings.warn( "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your " "labels by using the argument `text` of the regular `__call__` method (either in the same call as " "your images inputs, or in a separate call." ) SCREAMING_SNAKE_CASE = True SCREAMING_SNAKE_CASE = self.tokenizer yield SCREAMING_SNAKE_CASE = self.image_processor SCREAMING_SNAKE_CASE = False def __snake_case( self : Optional[int] , _UpperCamelCase : Any , _UpperCamelCase : int=False , _UpperCamelCase : int=None ) -> Any: '''simple docstring''' if added_vocab is None: SCREAMING_SNAKE_CASE = self.tokenizer.get_added_vocab() SCREAMING_SNAKE_CASE = {} while tokens: SCREAMING_SNAKE_CASE = re.search(R"<s_(.*?)>" , __a , re.IGNORECASE ) if start_token is None: break SCREAMING_SNAKE_CASE = start_token.group(1 ) SCREAMING_SNAKE_CASE = re.search(RF"</s_{key}>" , __a , re.IGNORECASE ) SCREAMING_SNAKE_CASE = start_token.group() if end_token is None: SCREAMING_SNAKE_CASE = tokens.replace(__a , "" ) else: SCREAMING_SNAKE_CASE = end_token.group() SCREAMING_SNAKE_CASE = re.escape(__a ) SCREAMING_SNAKE_CASE = re.escape(__a ) SCREAMING_SNAKE_CASE = re.search(F"{start_token_escaped}(.*?){end_token_escaped}" , __a , re.IGNORECASE ) if content is not None: SCREAMING_SNAKE_CASE = content.group(1 ).strip() if r"<s_" in content and r"</s_" in content: # non-leaf node SCREAMING_SNAKE_CASE = self.tokenajson(__a , is_inner_value=__a , added_vocab=__a ) if value: if len(__a ) == 1: SCREAMING_SNAKE_CASE = value[0] SCREAMING_SNAKE_CASE = value else: # leaf nodes SCREAMING_SNAKE_CASE = [] for leaf in content.split(R"<sep/>" ): SCREAMING_SNAKE_CASE = leaf.strip() if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>": SCREAMING_SNAKE_CASE = leaf[1:-2] # for categorical special tokens output[key].append(__a ) if len(output[key] ) == 1: SCREAMING_SNAKE_CASE = output[key][0] SCREAMING_SNAKE_CASE = tokens[tokens.find(__a ) + len(__a ) :].strip() if tokens[:6] == r"<sep/>": # non-leaf nodes return [output] + self.tokenajson(tokens[6:] , is_inner_value=__a , added_vocab=__a ) if len(__a ): return [output] if is_inner_value else output else: return [] if is_inner_value else {"text_sequence": tokens} @property def __snake_case( self : Union[str, Any] ) -> Tuple: '''simple docstring''' warnings.warn( "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __a , ) return self.image_processor_class @property def __snake_case( self : Tuple ) -> Tuple: '''simple docstring''' warnings.warn( "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __a , ) return self.image_processor
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"""simple docstring""" import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() _a = logging.get_logger(__name__) _a = { """post_extract_proj""": """feature_projection.projection""", """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""", """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""", """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""", """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""", """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""", """self_attn_layer_norm""": """encoder.layers.*.layer_norm""", """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""", """fc2""": """encoder.layers.*.feed_forward.output_dense""", """final_layer_norm""": """encoder.layers.*.final_layer_norm""", """encoder.layer_norm""": """encoder.layer_norm""", """w2v_model.layer_norm""": """feature_projection.layer_norm""", """quantizer.weight_proj""": """quantizer.weight_proj""", """quantizer.vars""": """quantizer.codevectors""", """project_q""": """project_q""", """final_proj""": """project_hid""", """w2v_encoder.proj""": """lm_head""", """mask_emb""": """masked_spec_embed""", } _a = [ """lm_head""", """quantizer.weight_proj""", """quantizer.codevectors""", """project_q""", """project_hid""", ] def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple: """simple docstring""" for attribute in key.split('''.''' ): _UpperCamelCase = getattr(__snake_case, __snake_case ) if weight_type is not None: _UpperCamelCase = getattr(__snake_case, __snake_case ).shape else: _UpperCamelCase = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": _UpperCamelCase = value elif weight_type == "weight_g": _UpperCamelCase = value elif weight_type == "weight_v": _UpperCamelCase = value elif weight_type == "bias": _UpperCamelCase = value else: _UpperCamelCase = value logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' ) def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = [] _UpperCamelCase = fairseq_model.state_dict() _UpperCamelCase = hf_model.feature_extractor _UpperCamelCase = hf_model.adapter for name, value in fairseq_dict.items(): _UpperCamelCase = False if "conv_layers" in name: load_conv_layer( __snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', ) _UpperCamelCase = True elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ): load_adapter(__snake_case, __snake_case, __snake_case, __snake_case ) _UpperCamelCase = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: _UpperCamelCase = True if "*" in mapped_key: _UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2] _UpperCamelCase = mapped_key.replace('''*''', __snake_case ) if "weight_g" in name: _UpperCamelCase = '''weight_g''' elif "weight_v" in name: _UpperCamelCase = '''weight_v''' elif "bias" in name: _UpperCamelCase = '''bias''' elif "weight" in name: _UpperCamelCase = '''weight''' else: _UpperCamelCase = None set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) continue if not is_used: unused_weights.append(__snake_case ) logger.warning(F'''Unused weights: {unused_weights}''' ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = full_name.split('''conv_layers.''' )[-1] _UpperCamelCase = name.split('''.''' ) _UpperCamelCase = int(items[0] ) _UpperCamelCase = int(items[1] ) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict: """simple docstring""" _UpperCamelCase = full_name.split('''adaptor.''' )[-1] _UpperCamelCase = name.split('''.''' ) if items[1].isdigit(): _UpperCamelCase = int(items[1] ) else: _UpperCamelCase = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' _UpperCamelCase = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' ) elif isinstance(__snake_case, __snake_case ): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase , _UpperCamelCase = emb.weight.shape _UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case ) _UpperCamelCase = emb.weight.data return lin_layer @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = WavaVecaConfig.from_pretrained( __snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, ) _UpperCamelCase = MBartConfig.from_pretrained(__snake_case ) # load model _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={ '''config_yaml''': config_yaml_path, '''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path, '''load_pretrained_decoder_from''': None, }, ) _UpperCamelCase = model[0].eval() # load feature extractor _UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case ) # set weights for wav2vec2 encoder _UpperCamelCase = WavaVecaModel(__snake_case ) recursively_load_weights_wavaveca(model.encoder, __snake_case ) # load decoder weights _UpperCamelCase = MBartForCausalLM(__snake_case ) _UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case ) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' ) logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' ) _UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case ) _UpperCamelCase = False _UpperCamelCase = MBartaaTokenizer(__snake_case ) tokenizer.save_pretrained(__snake_case ) _UpperCamelCase = hf_wavavec.config.to_dict() _UpperCamelCase = tokenizer.pad_token_id _UpperCamelCase = tokenizer.bos_token_id _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = '''mbart50''' _UpperCamelCase = '''wav2vec2''' _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = 25_00_04 _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case ) hf_wavavec.save_pretrained(__snake_case ) feature_extractor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""") parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""") parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""") parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""") parser.add_argument( """--encoder_config_path""", default="""facebook/wav2vec2-xls-r-1b""", type=str, help="""Path to hf encoder wav2vec2 checkpoint config""", ) parser.add_argument( """--decoder_config_path""", default="""facebook/mbart-large-50-one-to-many-mmt""", type=str, help="""Path to hf decoder checkpoint config""", ) parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""") parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""") parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""") parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""") parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""") _a = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )
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'''simple docstring''' import argparse import logging import os import datasets import tensorflow as tf from transformers import AutoTokenizer _snake_case : int = logging.getLogger(__name__) def snake_case_ (): '''simple docstring''' _a = argparse.ArgumentParser( description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.''' ) parser.add_argument( '''--dataset_name''' , type=__snake_case , default='''wikitext''' , help='''Name of the training. Explore datasets at: hf.co/datasets.''' , ) parser.add_argument( '''--dataset_config''' , type=__snake_case , default='''wikitext-103-raw-v1''' , help='''Configuration name of the dataset.''' ) parser.add_argument( '''--tokenizer_name_or_path''' , type=__snake_case , default='''sayakpaul/unigram-tokenizer-wikitext''' , help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''' , ) parser.add_argument( '''--shard_size''' , type=__snake_case , default=1000 , help='''Number of entries to go in a single shard.''' , ) parser.add_argument('''--split''' , type=__snake_case , default='''train''' , choices=['''train''', '''test''', '''validation'''] ) parser.add_argument( '''--limit''' , default=__snake_case , type=__snake_case , help='''Limit the number of shards (used for debugging).''' , ) parser.add_argument( '''--max_length''' , type=__snake_case , default=512 , help='''Maximum sequence length. For training on TPUs, it helps to have a maximum''' ''' sequence length that is a multiple of 8.''' , ) parser.add_argument( '''--output_dir''' , default='''tf-tpu''' , type=__snake_case , help='''Output directory where the TFRecord shards will be saved. If the''' ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord''' ''' shards will be directly saved to a Google Cloud Storage bucket.''' , ) _a = parser.parse_args() return args def snake_case_ (UpperCamelCase : List[Any] ): '''simple docstring''' def fn(UpperCamelCase : List[str] ): return tokenizer(examples['''text'''] ) return fn def snake_case_ (UpperCamelCase : Dict ): '''simple docstring''' _a = [] for i in range(len(tokenized_data['''input_ids'''] ) ): _a = { '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i] ) ), '''attention_mask''': tf.train.Feature( intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i] ) ), } _a = tf.train.Features(feature=__snake_case ) _a = tf.train.Example(features=__snake_case ) _a = example.SerializeToString() records.append(__snake_case ) return records def snake_case_ (UpperCamelCase : str ): '''simple docstring''' _a = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split ) if args.limit is not None: _a = min(len(__snake_case ) , args.limit ) _a = dataset.select(range(__snake_case ) ) print(f'Limiting the dataset to {args.limit} entries.' ) _a = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path ) # Handle output directory creation. # For serializing into a Google Cloud Storage Bucket, one needs to first # create a bucket. if "gs" not in args.output_dir: if not os.path.exists(args.output_dir ): os.makedirs(args.output_dir ) _a = os.path.join(args.output_dir , args.split ) if not os.path.exists(__snake_case ): os.makedirs(__snake_case ) else: _a = os.path.join(args.output_dir , args.split ) # Tokenize the whole dataset at once. _a = tokenize_function(__snake_case ) _a = dataset.map(__snake_case , batched=__snake_case , num_proc=4 , remove_columns=['''text'''] ) # We need to concatenate all our texts together, and then split the result # into chunks of a fixed size, which we will call block_size. To do this, we # will use the map method again, with the option batched=True. When we use batched=True, # the function we pass to map() will be passed multiple inputs at once, allowing us # to group them into more or fewer examples than we had in the input. # This allows us to create our new fixed-length samples. The advantage of this # method is that we don't lose a whole lot of content from the dataset compared to the # case where we simply tokenize with a pre-defined max_length. def group_texts(UpperCamelCase : Optional[Any] ): # Concatenate all texts. _a = {k: sum(examples[k] , [] ) for k in examples.keys()} _a = len(concatenated_examples[list(examples.keys() )[0]] ) # We drop the small remainder, though you could add padding instead if the model supports it # In this, as in all things, we advise you to follow your heart 🫀 _a = (total_length // args.max_length) * args.max_length # Split by chunks of max_len. _a = { k: [t[i : i + args.max_length] for i in range(0 , __snake_case , args.max_length )] for k, t in concatenated_examples.items() } return result _a = dataset_tokenized.map(__snake_case , batched=__snake_case , batch_size=1000 , num_proc=4 ) _a = 0 _a = 0 for shard in range(0 , len(__snake_case ) , args.shard_size ): _a = grouped_dataset[shard : shard + args.shard_size] _a = len(dataset_snapshot['''input_ids'''] ) _a = os.path.join(__snake_case , f'dataset-{shard_count}-{records_containing}.tfrecord' ) _a = get_serialized_examples(__snake_case ) with tf.io.TFRecordWriter(__snake_case ) as out_file: for i in range(len(__snake_case ) ): _a = serialized_examples[i] out_file.write(__snake_case ) print('''Wrote file {} containing {} records'''.format(__snake_case , __snake_case ) ) shard_count += 1 total_records += records_containing with open(f'split-{args.split}-records-count.txt' , '''w''' ) as f: print(f'Total {args.split} records: {total_records}' , file=__snake_case ) if __name__ == "__main__": _snake_case : Optional[int] = parse_args() main(args)
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"""simple docstring""" import fire from utils import calculate_rouge, save_json def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]: """simple docstring""" _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()] _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )] _UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case ) if save_path is not None: save_json(__snake_case, __snake_case, indent=__snake_case ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)
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'''simple docstring''' from ...utils import deprecate from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401 deprecate( 'stable diffusion controlnet', '0.22.0', 'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.', standard_warn=False, stacklevel=3, )
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"""simple docstring""" import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['image_processor', 'tokenizer'] lowercase__ = 'ViTImageProcessor' lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast') def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = None if "feature_extractor" in kwargs: warnings.warn( '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`''' ''' instead.''' , __a , ) _UpperCamelCase = kwargs.pop('''feature_extractor''') _UpperCamelCase = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError('''You need to specify an `image_processor`.''') if tokenizer is None: raise ValueError('''You need to specify a `tokenizer`.''') super().__init__(__a , __a) def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple: '''simple docstring''' if text is None and visual_prompt is None and images is None: raise ValueError('''You have to specify either text, visual prompt or images.''') if text is not None and visual_prompt is not None: raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''') if text is not None: _UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a) if visual_prompt is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if images is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if visual_prompt is not None and images is not None: _UpperCamelCase = { '''pixel_values''': image_features.pixel_values, '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding elif text is not None and images is not None: _UpperCamelCase = image_features.pixel_values return encoding elif text is not None: return encoding elif visual_prompt is not None: _UpperCamelCase = { '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding else: return BatchEncoding(data=dict(**__a) , tensor_type=__a) def UpperCAmelCase ( self , *__a , **__a) -> Any: '''simple docstring''' return self.tokenizer.batch_decode(*__a , **__a) def UpperCAmelCase ( self , *__a , **__a) -> List[str]: '''simple docstring''' return self.tokenizer.decode(*__a , **__a) @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , ) return self.image_processor_class @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , ) return self.image_processor
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'''simple docstring''' from typing import Dict, List from nltk.translate import gleu_score import datasets from datasets import MetricInfo UpperCamelCase__ : Tuple = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n' UpperCamelCase__ : List[Any] = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n' UpperCamelCase__ : Dict = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION ) class _lowercase ( datasets.Metric ): '''simple docstring''' def lowerCAmelCase__ ( self ) -> MetricInfo: '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features( { '''predictions''': datasets.Sequence(datasets.Value('''string''' ,id='''token''' ) ,id='''sequence''' ), '''references''': datasets.Sequence( datasets.Sequence(datasets.Value('''string''' ,id='''token''' ) ,id='''sequence''' ) ,id='''references''' ), } ) ,) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 1 ,lowerCamelCase_ = 4 ,) -> Dict[str, float]: '''simple docstring''' return { "google_bleu": gleu_score.corpus_gleu( list_of_references=__a ,hypotheses=__a ,min_len=__a ,max_len=__a ) }
614
"""simple docstring""" import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import UperNetForSemanticSegmentation from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = num_channels _UpperCamelCase = num_stages _UpperCamelCase = hidden_sizes _UpperCamelCase = depths _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = out_features _UpperCamelCase = num_labels _UpperCamelCase = scope _UpperCamelCase = num_stages def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ConvNextConfig( num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return UperNetConfig( backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = UperNetForSemanticSegmentation(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size)) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else () lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = UperNetModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*__a) @unittest.skip(reason='''UperNet does not use inputs_embeds''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not support input and output embeddings''') def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> int: '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''') def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' pass @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' def check_hidden_states_output(__a , __a , __a): _UpperCamelCase = model_class(__a) model.to(__a) model.eval() with torch.no_grad(): _UpperCamelCase = model(**self._prepare_for_class(__a , __a)) _UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states _UpperCamelCase = self.model_tester.num_stages self.assertEqual(len(__a) , expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) _UpperCamelCase = _config_zero_init(configs_no_init.backbone_config) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @unittest.skip(reason='''UperNet does not have tied weights''') def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' pass @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = hf_hub_download( repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' ) _UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' ) return image @require_torch @require_vision @slow class _UpperCAmelCase( unittest.TestCase ): def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
19
0
'''simple docstring''' import random class __magic_name__ : @staticmethod def lowerCAmelCase ( snake_case) -> tuple[list[int], list[int]]: '''simple docstring''' _UpperCAmelCase : str =[ord(__a) for i in text] _UpperCAmelCase : Any =[] _UpperCAmelCase : str =[] for i in plain: _UpperCAmelCase : Optional[int] =random.randint(1 , 3_0_0) _UpperCAmelCase : int =(i + k) * k cipher.append(__a) key.append(__a) return cipher, key @staticmethod def lowerCAmelCase ( snake_case , snake_case) -> str: '''simple docstring''' _UpperCAmelCase : str =[] for i in range(len(__a)): _UpperCAmelCase : List[str] =int((cipher[i] - (key[i]) ** 2) / key[i]) plain.append(chr(__a)) return "".join(__a) if __name__ == "__main__": lowercase, lowercase =Onepad().encrypt('Hello') print(c, k) print(Onepad().decrypt(c, k))
446
"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DDPMScheduler,) def UpperCAmelCase ( self , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 10_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''variance_type''': '''fixed_small''', '''clip_sample''': True, } config.update(**__a) return config def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for timesteps in [1, 5, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.check_over_configs(thresholding=__a) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=__a , prediction_type=__a , sample_max_value=__a , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for t in [0, 5_00, 9_99]: self.check_over_forward(time_step=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 258.9606) < 1e-2 assert abs(result_mean.item() - 0.3372) < 1e-3 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 202.0296) < 1e-2 assert abs(result_mean.item() - 0.2631) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=__a) _UpperCamelCase = scheduler.timesteps for i, timestep in enumerate(__a): if i == len(__a) - 1: _UpperCamelCase = -1 else: _UpperCamelCase = timesteps[i + 1] _UpperCamelCase = scheduler.previous_timestep(__a) _UpperCamelCase = prev_t.item() self.assertEqual(__a , __a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 51, 0] with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''): scheduler.set_timesteps(timesteps=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] _UpperCamelCase = len(__a) with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''): scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [scheduler.config.num_train_timesteps] with self.assertRaises( __a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ): scheduler.set_timesteps(timesteps=__a)
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from pathlib import Path import cva import numpy as np from matplotlib import pyplot as plt def _a ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] ) -> np.ndarray: '''simple docstring''' SCREAMING_SNAKE_CASE__ : str = cva.getAffineTransform(__snake_case , __snake_case ) return cva.warpAffine(__snake_case , __snake_case , (rows, cols) ) if __name__ == "__main__": # read original image _lowerCamelCase : Optional[Any] = cva.imread( str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''') ) # turn image in gray scale value _lowerCamelCase : str = cva.cvtColor(image, cva.COLOR_BGR2GRAY) # get image shape _lowerCamelCase , _lowerCamelCase : Union[str, Any] = gray_img.shape # set different points to rotate image _lowerCamelCase : Optional[int] = np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa) _lowerCamelCase : List[str] = np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa) _lowerCamelCase : Optional[int] = np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa) _lowerCamelCase : List[str] = np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa) # add all rotated images in a list _lowerCamelCase : Dict = [ gray_img, get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols), get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols), get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols), ] # plot different image rotations _lowerCamelCase : str = plt.figure(1) _lowerCamelCase : Any = ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3'''] for i, image in enumerate(images): plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''') plt.title(titles[i]) plt.axis('''off''') plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95) plt.show()
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"""simple docstring""" from __future__ import annotations from functools import lru_cache from math import ceil _a = 100 _a = set(range(3, NUM_PRIMES, 2)) primes.add(2) _a = 42 for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=1_00 ) def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} _UpperCamelCase = set() _UpperCamelCase = 42 _UpperCamelCase = 42 for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None: """simple docstring""" for number_to_partition in range(1, __snake_case ): if len(partition(__snake_case ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(F"""{solution() = }""")
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import logging from pathlib import Path import numpy as np import pytorch_lightning as pl import torch from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.utilities import rank_zero_only from utils_rag import save_json def lowerCamelCase_(lowerCamelCase_ ) -> List[str]: UpperCAmelCase = filter(lambda lowerCamelCase_ : p.requires_grad , model.parameters() ) UpperCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] ) return params __lowerCamelCase : int = logging.getLogger(__name__) def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]: if metric == "rouge2": UpperCAmelCase = "{val_avg_rouge2:.4f}-{step_count}" elif metric == "bleu": UpperCAmelCase = "{val_avg_bleu:.4f}-{step_count}" elif metric == "em": UpperCAmelCase = "{val_avg_em:.4f}-{step_count}" elif metric == "loss": UpperCAmelCase = "{val_avg_loss:.4f}-{step_count}" else: raise NotImplementedError( F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this' " function." ) UpperCAmelCase = ModelCheckpoint( dirpath=__snake_case , filename=__snake_case , monitor=F'val_{metric}' , mode="max" , save_top_k=1 , every_n_epochs=1 , ) return checkpoint_callback def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Any: return EarlyStopping( monitor=F'val_{metric}' , mode="min" if "loss" in metric else "max" , patience=__snake_case , verbose=__snake_case , ) class __magic_name__ ( pl.Callback ): def SCREAMING_SNAKE_CASE_ ( self : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Any: '''simple docstring''' UpperCAmelCase = {F'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )} pl_module.logger.log_metrics(__a ) @rank_zero_only def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple=True ) -> None: '''simple docstring''' logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****' ) UpperCAmelCase = trainer.callback_metrics trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} ) # Log results UpperCAmelCase = Path(pl_module.hparams.output_dir ) if type_path == "test": UpperCAmelCase = od / "test_results.txt" UpperCAmelCase = od / "test_generations.txt" else: # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json # If people want this it will be easy enough to add back. UpperCAmelCase = od / F'{type_path}_results/{trainer.global_step:05d}.txt' UpperCAmelCase = od / F'{type_path}_generations/{trainer.global_step:05d}.txt' results_file.parent.mkdir(exist_ok=__a ) generations_file.parent.mkdir(exist_ok=__a ) with open(__a , "a+" ) as writer: for key in sorted(__a ): if key in ["log", "progress_bar", "preds"]: continue UpperCAmelCase = metrics[key] if isinstance(__a , torch.Tensor ): UpperCAmelCase = val.item() UpperCAmelCase = F'{key}: {val:.6f}\n' writer.write(__a ) if not save_generations: return if "preds" in metrics: UpperCAmelCase = "\n".join(metrics["preds"] ) generations_file.open("w+" ).write(__a ) @rank_zero_only def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : int ) -> str: '''simple docstring''' try: UpperCAmelCase = pl_module.model.model.num_parameters() except AttributeError: UpperCAmelCase = pl_module.model.num_parameters() UpperCAmelCase = count_trainable_parameters(__a ) # mp stands for million parameters trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} ) @rank_zero_only def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any ) -> Tuple: '''simple docstring''' save_json(pl_module.metrics , pl_module.metrics_save_path ) return self._write_logs(__a , __a , "test" ) @rank_zero_only def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] ) -> Tuple: '''simple docstring''' save_json(pl_module.metrics , pl_module.metrics_save_path ) # Uncommenting this will save val generations # return self._write_logs(trainer, pl_module, "valid")
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"""simple docstring""" from collections.abc import Callable import numpy as np def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array: """simple docstring""" _UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) ) _UpperCamelCase = np.zeros((n + 1,) ) _UpperCamelCase = ya _UpperCamelCase = xa for k in range(__snake_case ): _UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] ) _UpperCamelCase = y[k] + ( (step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import math from typing import Optional import numpy as np from ...configuration_utils import PretrainedConfig from ...utils import logging __a: Any = logging.get_logger(__name__) __a: List[str] = { """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""", """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""", } class UpperCAmelCase ( a__ ): '''simple docstring''' SCREAMING_SNAKE_CASE = "encodec" def __init__( self , __lowerCAmelCase=[1.5, 3.0, 6.0, 1_2.0, 2_4.0] , __lowerCAmelCase=24000 , __lowerCAmelCase=1 , __lowerCAmelCase=False , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=128 , __lowerCAmelCase=32 , __lowerCAmelCase=1 , __lowerCAmelCase=[8, 5, 4, 2] , __lowerCAmelCase="weight_norm" , __lowerCAmelCase=7 , __lowerCAmelCase=7 , __lowerCAmelCase=3 , __lowerCAmelCase=2 , __lowerCAmelCase=True , __lowerCAmelCase="reflect" , __lowerCAmelCase=2 , __lowerCAmelCase=2 , __lowerCAmelCase=1.0 , __lowerCAmelCase=1024 , __lowerCAmelCase=None , __lowerCAmelCase=True , **__lowerCAmelCase , ) -> int: lowercase__ : Tuple = target_bandwidths lowercase__ : Dict = sampling_rate lowercase__ : List[Any] = audio_channels lowercase__ : Optional[Any] = normalize lowercase__ : int = chunk_length_s lowercase__ : Any = overlap lowercase__ : Optional[Any] = hidden_size lowercase__ : Union[str, Any] = num_filters lowercase__ : Optional[int] = num_residual_layers lowercase__ : Tuple = upsampling_ratios lowercase__ : Any = norm_type lowercase__ : Union[str, Any] = kernel_size lowercase__ : Union[str, Any] = last_kernel_size lowercase__ : Any = residual_kernel_size lowercase__ : Tuple = dilation_growth_rate lowercase__ : List[str] = use_causal_conv lowercase__ : Optional[Any] = pad_mode lowercase__ : List[str] = compress lowercase__ : List[str] = num_lstm_layers lowercase__ : Tuple = trim_right_ratio lowercase__ : Optional[Any] = codebook_size lowercase__ : List[Any] = codebook_dim if codebook_dim is not None else hidden_size lowercase__ : Optional[int] = use_conv_shortcut if self.norm_type not in ["weight_norm", "time_group_norm"]: raise ValueError( F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" ) super().__init__(**__a ) @property def _lowerCAmelCase( self ) -> Optional[int]: if self.chunk_length_s is None: return None else: return int(self.chunk_length_s * self.sampling_rate ) @property def _lowerCAmelCase( self ) -> Optional[int]: if self.chunk_length_s is None or self.overlap is None: return None else: return max(1 , int((1.0 - self.overlap) * self.chunk_length ) ) @property def _lowerCAmelCase( self ) -> int: lowercase__ : Tuple = np.prod(self.upsampling_ratios ) return math.ceil(self.sampling_rate / hop_length ) @property def _lowerCAmelCase( self ) -> int: return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
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"""simple docstring""" import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": _a = argparse.ArgumentParser( description=( """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned""" """ Distillation""" ) ) parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""]) parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str) parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str) parser.add_argument("""--vocab_transform""", action="""store_true""") _a = parser.parse_args() if args.model_type == "bert": _a = BertForMaskedLM.from_pretrained(args.model_name) _a = """bert""" else: raise ValueError("""args.model_type should be \"bert\".""") _a = model.state_dict() _a = {} for w in ["word_embeddings", "position_embeddings"]: _a = state_dict[F"""{prefix}.embeddings.{w}.weight"""] for w in ["weight", "bias"]: _a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""] _a = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}""" ] std_idx += 1 _a = state_dict["""cls.predictions.decoder.weight"""] _a = state_dict["""cls.predictions.bias"""] if args.vocab_transform: for w in ["weight", "bias"]: _a = state_dict[F"""cls.predictions.transform.dense.{w}"""] _a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""] print(F"""N layers selected for distillation: {std_idx}""") print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""") print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""") torch.save(compressed_sd, args.dump_checkpoint)
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from typing import List from .keymap import KEYMAP, get_character def lowercase_( SCREAMING_SNAKE_CASE_ ): '''simple docstring''' def decorator(SCREAMING_SNAKE_CASE_ ): lowerCamelCase : Tuple = getattr(__snake_case , "handle_key" , [] ) handle += [key] setattr(__snake_case , "handle_key" , __snake_case ) return func return decorator def lowercase_( *SCREAMING_SNAKE_CASE_ ): '''simple docstring''' def decorator(SCREAMING_SNAKE_CASE_ ): lowerCamelCase : int = getattr(__snake_case , "handle_key" , [] ) handle += keys setattr(__snake_case , "handle_key" , __snake_case ) return func return decorator class UpperCAmelCase_ ( UpperCamelCase ): '''simple docstring''' def __new__( cls , __A , __A , __A ): """simple docstring""" lowerCamelCase : Optional[Any] = super().__new__(cls , __a , __a , __a ) if not hasattr(__a , "key_handler" ): setattr(__a , "key_handler" , {} ) setattr(__a , "handle_input" , KeyHandler.handle_input ) for value in attrs.values(): lowerCamelCase : Tuple = getattr(__a , "handle_key" , [] ) for key in handled_keys: lowerCamelCase : Optional[Any] = value return new_cls @staticmethod def _snake_case ( cls ): """simple docstring""" lowerCamelCase : List[str] = get_character() if char != KEYMAP["undefined"]: lowerCamelCase : Optional[Any] = ord(__a ) lowerCamelCase : str = cls.key_handler.get(__a ) if handler: lowerCamelCase : Any = char return handler(cls ) else: return None def lowercase_( cls ): '''simple docstring''' return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
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"""simple docstring""" import unittest from transformers import PegasusConfig, PegasusTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html _a = """platform""" import jax import jax.numpy as jnp import numpy as np from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel @require_flax class _UpperCAmelCase: lowercase__ = PegasusConfig lowercase__ = {} lowercase__ = 'gelu' def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = eos_token_id _UpperCamelCase = pad_token_id _UpperCamelCase = bos_token_id def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size) _UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1) _UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , ) _UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a) return config, inputs_dict def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''') _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ] , axis=-1 , ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]: """simple docstring""" if attention_mask is None: _UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta ) if decoder_attention_mask is None: _UpperCamelCase = np.concatenate( [ np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ), np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ), ], axis=-1, ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_flax class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( FlaxPegasusForConditionalGeneration, FlaxPegasusModel, ) if is_flax_available() else () ) lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else () lowercase__ = True lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = FlaxPegasusModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = self._prepare_for_class(__a , __a) _UpperCamelCase = model_class(__a) @jax.jit def encode_jitted(__a , __a=None , **__a): return model.encode(input_ids=__a , attention_mask=__a) with self.subTest('''JIT Enabled'''): _UpperCamelCase = encode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = encode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = model_class(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask''']) _UpperCamelCase = { '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''], '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''], '''encoder_outputs''': encoder_outputs, } @jax.jit def decode_jitted(__a , __a , __a): return model.decode( decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , ) with self.subTest('''JIT Enabled'''): _UpperCamelCase = decode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = decode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) @slow def UpperCAmelCase ( self) -> int: '''simple docstring''' for model_class_name in self.all_model_classes: _UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a) _UpperCamelCase = np.ones((1, 1)) _UpperCamelCase = model(__a) self.assertIsNotNone(__a) @slow def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = [ ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''', ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''', ] _UpperCamelCase = [ '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''', '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''', ] _UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a) _UpperCamelCase = model.generate(**__a , num_beams=2).sequences _UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a) assert tgt_text == decoded
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import unittest from transformers import load_tool from .test_tools_common import ToolTesterMixin class lowerCamelCase__ ( unittest.TestCase , lowerCamelCase__): '''simple docstring''' def lowerCAmelCase__ (self ) -> List[Any]: """simple docstring""" lowerCAmelCase__ : int = load_tool('''text-classification''' ) self.tool.setup() lowerCAmelCase__ : List[str] = load_tool('''text-classification''' ,remote=__a ) def lowerCAmelCase__ (self ) -> List[Any]: """simple docstring""" lowerCAmelCase__ : Any = self.tool('''That\'s quite cool''' ,['''positive''', '''negative'''] ) self.assertEqual(__a ,'''positive''' ) def lowerCAmelCase__ (self ) -> int: """simple docstring""" lowerCAmelCase__ : Union[str, Any] = self.remote_tool('''That\'s quite cool''' ,['''positive''', '''negative'''] ) self.assertEqual(__a ,'''positive''' ) def lowerCAmelCase__ (self ) -> Dict: """simple docstring""" lowerCAmelCase__ : List[Any] = self.tool(text='''That\'s quite cool''' ,labels=['''positive''', '''negative'''] ) self.assertEqual(__a ,'''positive''' ) def lowerCAmelCase__ (self ) -> Optional[Any]: """simple docstring""" lowerCAmelCase__ : Union[str, Any] = self.remote_tool(text='''That\'s quite cool''' ,labels=['''positive''', '''negative'''] ) self.assertEqual(__a ,'''positive''' )
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_input_mask _UpperCamelCase = use_token_type_ids _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = type_vocab_size _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = num_labels _UpperCamelCase = num_choices _UpperCamelCase = scope _UpperCamelCase = projection_dim def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py _UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length]) _UpperCamelCase = None if self.use_token_type_ids: _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size) _UpperCamelCase = None _UpperCamelCase = None _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels) _UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices) _UpperCamelCase = BertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , ) _UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]: '''simple docstring''' _UpperCamelCase = TFDPRContextEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict: '''simple docstring''' _UpperCamelCase = TFDPRReader(config=__a) _UpperCamelCase = model(__a , attention_mask=__a) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,)) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''input_ids''': input_ids} return config, inputs_dict @require_tf class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__a) @slow def UpperCAmelCase ( self) -> str: '''simple docstring''' for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRReader.from_pretrained(__a) self.assertIsNotNone(__a) @require_tf class _UpperCAmelCase( unittest.TestCase ): @slow def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''') _UpperCamelCase = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP] _UpperCamelCase = model(__a)[0] # embedding shape = (1, 768) # compare the actual values for a slice. _UpperCamelCase = tf.constant( [ [ 0.0323_6253, 0.1275_3335, 0.1681_8509, 0.0027_9786, 0.389_6933, 0.2426_4945, 0.217_8971, -0.0233_5227, -0.0848_1959, -0.1432_4117, ] ]) self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
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'''simple docstring''' from collections.abc import Callable import numpy as np def UpperCAmelCase_ (__a : Tuple , __a : List[str] , __a : Tuple , __a : Tuple , __a : List[str] ): """simple docstring""" _a : str = int(np.ceil((x_end - xa) / step_size ) ) _a : Optional[int] = np.zeros((n + 1,) ) _a : str = ya _a : Dict = xa for k in range(__snake_case ): _a : List[Any] = y[k] + step_size * ode_func(__snake_case , y[k] ) _a : List[Any] = y[k] + ( (step_size / 2) * (ode_func(__snake_case , y[k] ) + ode_func(x + step_size , __snake_case )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import argparse import json from typing import List from ltp import LTP from transformers.models.bert.tokenization_bert import BertTokenizer def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x2_0000 and cp <= 0x2_A6DF) # or (cp >= 0x2_A700 and cp <= 0x2_B73F) # or (cp >= 0x2_B740 and cp <= 0x2_B81F) # or (cp >= 0x2_B820 and cp <= 0x2_CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2_F800 and cp <= 0x2_FA1F) # ): # return True return False def lowerCamelCase__ ( __snake_case ) -> Optional[Any]: """simple docstring""" for char in word: _UpperCamelCase = ord(__snake_case ) if not _is_chinese_char(__snake_case ): return 0 return 1 def lowerCamelCase__ ( __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = set() for token in tokens: _UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case ) if chinese_word: word_set.add(__snake_case ) _UpperCamelCase = list(__snake_case ) return word_list def lowerCamelCase__ ( __snake_case, __snake_case ) -> int: """simple docstring""" if not chinese_word_set: return bert_tokens _UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] ) _UpperCamelCase = bert_tokens _UpperCamelCase , _UpperCamelCase = 0, len(__snake_case ) while start < end: _UpperCamelCase = True if is_chinese(bert_word[start] ): _UpperCamelCase = min(end - start, __snake_case ) for i in range(__snake_case, 1, -1 ): _UpperCamelCase = ''''''.join(bert_word[start : start + i] ) if whole_word in chinese_word_set: for j in range(start + 1, start + i ): _UpperCamelCase = '''##''' + bert_word[j] _UpperCamelCase = start + i _UpperCamelCase = False break if single_word: start += 1 return bert_word def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws _UpperCamelCase = [get_chinese_word(__snake_case ) for r in res] ltp_res.extend(__snake_case ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for i in range(0, len(__snake_case ), 1_00 ): _UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 ) bert_res.extend(res['''input_ids'''] ) assert len(__snake_case ) == len(__snake_case ) _UpperCamelCase = [] for input_ids, chinese_word in zip(__snake_case, __snake_case ): _UpperCamelCase = [] for id in input_ids: _UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case ) input_tokens.append(__snake_case ) _UpperCamelCase = add_sub_symbol(__snake_case, __snake_case ) _UpperCamelCase = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(__snake_case ): if token[:2] == "##": _UpperCamelCase = token[2:] # save chinese tokens' pos if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ): ref_id.append(__snake_case ) ref_ids.append(__snake_case ) assert len(__snake_case ) == len(__snake_case ) return ref_ids def lowerCamelCase__ ( __snake_case ) -> Optional[int]: """simple docstring""" with open(args.file_name, '''r''', encoding='''utf-8''' ) as f: _UpperCamelCase = f.readlines() _UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029' _UpperCamelCase = LTP(args.ltp ) # faster in GPU device _UpperCamelCase = BertTokenizer.from_pretrained(args.bert ) _UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case ) with open(args.save_path, '''w''', encoding='''utf-8''' ) as f: _UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids] f.writelines(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser(description="""prepare_chinese_ref""") parser.add_argument( """--file_name""", required=False, type=str, default="""./resources/chinese-demo.txt""", help="""file need process, same as training data in lm""", ) parser.add_argument( """--ltp""", required=False, type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path""", ) parser.add_argument( """--bert""", required=False, type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""", ) parser.add_argument( """--save_path""", required=False, type=str, default="""./resources/ref.txt""", help="""path to save res""", ) _a = parser.parse_args() main(args)
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from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, is_valid_image, to_numpy_array, valid_images, ) from ...utils import TensorType, is_vision_available, logging if is_vision_available(): import PIL _lowerCamelCase : Tuple = logging.get_logger(__name__) def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ): if isinstance(__snake_case , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ): return videos elif isinstance(__snake_case , (list, tuple) ) and is_valid_image(videos[0] ): return [videos] elif is_valid_image(__snake_case ): return [[videos]] raise ValueError(F"Could not make batched video from {videos}" ) class lowercase ( a ): lowercase__ : List[str] = ["""pixel_values"""] def __init__( self : Optional[Any] , _UpperCamelCase : Dict = True , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : List[Any] = PILImageResampling.BILINEAR , _UpperCamelCase : Optional[Any] = True , _UpperCamelCase : Tuple = None , _UpperCamelCase : str = True , _UpperCamelCase : int = 1 / 255 , _UpperCamelCase : Any = True , _UpperCamelCase : str = None , _UpperCamelCase : List[Any] = None , **_UpperCamelCase : Dict , ) -> None: '''simple docstring''' super().__init__(**__a ) SCREAMING_SNAKE_CASE = size if size is not None else {"shortest_edge": 224} SCREAMING_SNAKE_CASE = get_size_dict(__a , default_to_square=__a ) SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"height": 224, "width": 224} SCREAMING_SNAKE_CASE = get_size_dict(__a , param_name="crop_size" ) SCREAMING_SNAKE_CASE = do_resize SCREAMING_SNAKE_CASE = size SCREAMING_SNAKE_CASE = do_center_crop SCREAMING_SNAKE_CASE = crop_size SCREAMING_SNAKE_CASE = resample SCREAMING_SNAKE_CASE = do_rescale SCREAMING_SNAKE_CASE = rescale_factor SCREAMING_SNAKE_CASE = do_normalize SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD def __snake_case( self : Union[str, Any] , _UpperCamelCase : Optional[int] , _UpperCamelCase : int , _UpperCamelCase : Tuple = PILImageResampling.BILINEAR , _UpperCamelCase : Optional[int] = None , **_UpperCamelCase : str , ) -> np.ndarray: '''simple docstring''' SCREAMING_SNAKE_CASE = get_size_dict(__a , default_to_square=__a ) if "shortest_edge" in size: SCREAMING_SNAKE_CASE = get_resize_output_image_size(__a , size["shortest_edge"] , default_to_square=__a ) elif "height" in size and "width" in size: SCREAMING_SNAKE_CASE = (size["height"], size["width"]) else: raise ValueError(F"Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}" ) return resize(__a , size=__a , resample=__a , data_format=__a , **__a ) def __snake_case( self : List[str] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Tuple = None , **_UpperCamelCase : Optional[int] , ) -> np.ndarray: '''simple docstring''' SCREAMING_SNAKE_CASE = get_size_dict(__a ) if "height" not in size or "width" not in size: raise ValueError(F"Size must have \'height\' and \'width\' as keys. Got {size.keys()}" ) return center_crop(__a , size=(size["height"], size["width"]) , data_format=__a , **__a ) def __snake_case( self : Union[str, Any] , _UpperCamelCase : Dict , _UpperCamelCase : Dict , _UpperCamelCase : Tuple = None , **_UpperCamelCase : Any , ) -> Dict: '''simple docstring''' return rescale(__a , scale=__a , data_format=__a , **__a ) def __snake_case( self : Dict , _UpperCamelCase : int , _UpperCamelCase : Tuple , _UpperCamelCase : Dict , _UpperCamelCase : Any = None , **_UpperCamelCase : Any , ) -> np.ndarray: '''simple docstring''' return normalize(__a , mean=__a , std=__a , data_format=__a , **__a ) def __snake_case( self : str , _UpperCamelCase : int , _UpperCamelCase : Tuple = None , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : Dict = None , _UpperCamelCase : Dict = None , _UpperCamelCase : List[str] = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Dict = None , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : int = None , _UpperCamelCase : List[str] = None , _UpperCamelCase : Optional[Any] = ChannelDimension.FIRST , ) -> np.ndarray: '''simple docstring''' if do_resize and size is None or resample is None: raise ValueError("Size and resample must be specified if do_resize is True." ) if do_center_crop and crop_size is None: raise ValueError("Crop size must be specified if do_center_crop is True." ) if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True." ) if do_normalize and (image_mean is None or image_std is None): raise ValueError("Image mean and std must be specified if do_normalize is True." ) # All transformations expect numpy arrays. SCREAMING_SNAKE_CASE = to_numpy_array(__a ) if do_resize: SCREAMING_SNAKE_CASE = self.resize(image=__a , size=__a , resample=__a ) if do_center_crop: SCREAMING_SNAKE_CASE = self.center_crop(__a , size=__a ) if do_rescale: SCREAMING_SNAKE_CASE = self.rescale(image=__a , scale=__a ) if do_normalize: SCREAMING_SNAKE_CASE = self.normalize(image=__a , mean=__a , std=__a ) SCREAMING_SNAKE_CASE = to_channel_dimension_format(__a , __a ) return image def __snake_case( self : List[str] , _UpperCamelCase : List[Any] , _UpperCamelCase : Tuple = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Any = None , _UpperCamelCase : List[str] = None , _UpperCamelCase : Any = None , _UpperCamelCase : Any = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : List[Any] = None , _UpperCamelCase : Dict = None , _UpperCamelCase : Dict = ChannelDimension.FIRST , **_UpperCamelCase : str , ) -> PIL.Image.Image: '''simple docstring''' SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std SCREAMING_SNAKE_CASE = size if size is not None else self.size SCREAMING_SNAKE_CASE = get_size_dict(__a , default_to_square=__a ) SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size SCREAMING_SNAKE_CASE = get_size_dict(__a , param_name="crop_size" ) if not valid_images(__a ): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) SCREAMING_SNAKE_CASE = make_batched(__a ) SCREAMING_SNAKE_CASE = [ [ self._preprocess_image( image=__a , do_resize=__a , size=__a , resample=__a , do_center_crop=__a , crop_size=__a , do_rescale=__a , rescale_factor=__a , do_normalize=__a , image_mean=__a , image_std=__a , data_format=__a , ) for img in video ] for video in videos ] SCREAMING_SNAKE_CASE = {"pixel_values": videos} return BatchFeature(data=__a , tensor_type=__a )
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"""simple docstring""" import heapq def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" _UpperCamelCase = [] # for each node and his adjacency list add them and the rank of the node to queue # using heapq module the queue will be filled like a Priority Queue # heapq works with a min priority queue, so I used -1*len(v) to build it for key, value in graph.items(): # O(log(n)) heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] ) # chosen_vertices = set of chosen vertices _UpperCamelCase = set() # while queue isn't empty and there are still edges # (queue[0][0] is the rank of the node with max rank) while queue and queue[0][0] != 0: # extract vertex with max rank from queue and add it to chosen_vertices _UpperCamelCase = heapq.heappop(__snake_case )[1][0] chosen_vertices.add(__snake_case ) # Remove all arcs adjacent to argmax for elem in queue: # if v haven't adjacent node, skip if elem[0] == 0: continue # if argmax is reachable from elem # remove argmax from elem's adjacent list and update his rank if argmax in elem[1][1]: _UpperCamelCase = elem[1][1].index(__snake_case ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(__snake_case ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() _a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
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'''simple docstring''' import importlib.util import os import platform from argparse import ArgumentParser import huggingface_hub from .. import __version__ as version from ..utils import ( is_accelerate_available, is_flax_available, is_safetensors_available, is_tf_available, is_torch_available, ) from . import BaseTransformersCLICommand def snake_case_ (UpperCamelCase : Dict ): '''simple docstring''' return EnvironmentCommand() def snake_case_ (UpperCamelCase : int ): '''simple docstring''' return EnvironmentCommand(args.accelerate_config_file ) class A ( _a ): @staticmethod def __lowerCAmelCase ( lowerCAmelCase_ : Any ) -> Union[str, Any]: """simple docstring""" _a = parser.add_parser('''env''' ) download_parser.set_defaults(func=__a ) download_parser.add_argument( '''--accelerate-config_file''' , default=__a , help='''The accelerate config file to use for the default values in the launching script.''' , ) download_parser.set_defaults(func=__a ) def __init__( self : Tuple , lowerCAmelCase_ : List[Any] , *lowerCAmelCase_ : List[Any] ) -> None: """simple docstring""" _a = accelerate_config_file def __lowerCAmelCase ( self : Dict ) -> int: """simple docstring""" _a = '''not installed''' if is_safetensors_available(): import safetensors _a = safetensors.__version__ elif importlib.util.find_spec('''safetensors''' ) is not None: import safetensors _a = F'{safetensors.__version__} but is ignored because of PyTorch version too old.' _a = '''not installed''' _a = _a = '''not found''' if is_accelerate_available(): import accelerate from accelerate.commands.config import default_config_file, load_config_from_file _a = accelerate.__version__ # Get the default from the config file. if self._accelerate_config_file is not None or os.path.isfile(__a ): _a = load_config_from_file(self._accelerate_config_file ).to_dict() _a = ( '''\n'''.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()] ) if isinstance(__a , __a ) else F'\t{accelerate_config}' ) _a = '''not installed''' _a = '''NA''' if is_torch_available(): import torch _a = torch.__version__ _a = torch.cuda.is_available() _a = '''not installed''' _a = '''NA''' if is_tf_available(): import tensorflow as tf _a = tf.__version__ try: # deprecated in v2.1 _a = tf.test.is_gpu_available() except AttributeError: # returns list of devices, convert to bool _a = bool(tf.config.list_physical_devices('''GPU''' ) ) _a = '''not installed''' _a = '''not installed''' _a = '''not installed''' _a = '''NA''' if is_flax_available(): import flax import jax import jaxlib _a = flax.__version__ _a = jax.__version__ _a = jaxlib.__version__ _a = jax.lib.xla_bridge.get_backend().platform _a = { '''`transformers` version''': version, '''Platform''': platform.platform(), '''Python version''': platform.python_version(), '''Huggingface_hub version''': huggingface_hub.__version__, '''Safetensors version''': F'{safetensors_version}', '''Accelerate version''': F'{accelerate_version}', '''Accelerate config''': F'{accelerate_config_str}', '''PyTorch version (GPU?)''': F'{pt_version} ({pt_cuda_available})', '''Tensorflow version (GPU?)''': F'{tf_version} ({tf_cuda_available})', '''Flax version (CPU?/GPU?/TPU?)''': F'{flax_version} ({jax_backend})', '''Jax version''': F'{jax_version}', '''JaxLib version''': F'{jaxlib_version}', '''Using GPU in script?''': '''<fill in>''', '''Using distributed or parallel set-up in script?''': '''<fill in>''', } print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' ) print(self.format_dict(__a ) ) return info @staticmethod def __lowerCAmelCase ( lowerCAmelCase_ : Tuple ) -> Union[str, Any]: """simple docstring""" return "\n".join([F'- {prop}: {val}' for prop, val in d.items()] ) + "\n"
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"""simple docstring""" from datasets.utils.patching import _PatchedModuleObj, patch_submodule from . import _test_patching def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" import os as original_os from os import path as original_path from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join _UpperCamelCase = '''__test_patch_submodule_mock__''' with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): # Every way to access os.path.join must be patched, and the rest must stay untouched # check os.path.join assert isinstance(_test_patching.os, _PatchedModuleObj ) assert isinstance(_test_patching.os.path, _PatchedModuleObj ) assert _test_patching.os.path.join is mock # check path.join assert isinstance(_test_patching.path, _PatchedModuleObj ) assert _test_patching.path.join is mock # check join assert _test_patching.join is mock # check that the other attributes are untouched assert _test_patching.os.rename is original_rename assert _test_patching.path.dirname is original_dirname assert _test_patching.os.path.dirname is original_dirname # Even renamed modules or objects must be patched # check renamed_os.path.join assert isinstance(_test_patching.renamed_os, _PatchedModuleObj ) assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj ) assert _test_patching.renamed_os.path.join is mock # check renamed_path.join assert isinstance(_test_patching.renamed_path, _PatchedModuleObj ) assert _test_patching.renamed_path.join is mock # check renamed_join assert _test_patching.renamed_join is mock # check that the other attributes are untouched assert _test_patching.renamed_os.rename is original_rename assert _test_patching.renamed_path.dirname is original_dirname assert _test_patching.renamed_os.path.dirname is original_dirname # check that everthing is back to normal when the patch is over assert _test_patching.os is original_os assert _test_patching.path is original_path assert _test_patching.join is original_join assert _test_patching.renamed_os is original_os assert _test_patching.renamed_path is original_path assert _test_patching.renamed_join is original_join def lowerCamelCase__ ( ) -> List[str]: """simple docstring""" assert _test_patching.open is open _UpperCamelCase = '''__test_patch_submodule_builtin_mock__''' # _test_patching has "open" in its globals assert _test_patching.open is open with patch_submodule(_test_patching, '''open''', __snake_case ): assert _test_patching.open is mock # check that everthing is back to normal when the patch is over assert _test_patching.open is open def lowerCamelCase__ ( ) -> Union[str, Any]: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_mock__''' with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ): pass def lowerCamelCase__ ( ) -> Dict: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__''' # _test_patching doesn't have "len" in its globals assert getattr(_test_patching, '''len''', __snake_case ) is None with patch_submodule(_test_patching, '''len''', __snake_case ): assert _test_patching.len is mock assert _test_patching.len is len def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__''' _UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case ) assert _test_patching.open is open patch.start() assert _test_patching.open is mock patch.stop() assert _test_patching.open is open def lowerCamelCase__ ( ) -> Optional[int]: """simple docstring""" from os import rename as original_rename from os.path import dirname as original_dirname from os.path import join as original_join _UpperCamelCase = '''__test_patch_submodule_successive_join__''' _UpperCamelCase = '''__test_patch_submodule_successive_dirname__''' _UpperCamelCase = '''__test_patch_submodule_successive_rename__''' assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename # try another order with patch_submodule(_test_patching, '''os.rename''', __snake_case ): with patch_submodule(_test_patching, '''os.path.join''', __snake_case ): with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ): assert _test_patching.os.path.join is mock_join assert _test_patching.os.path.dirname is mock_dirname assert _test_patching.os.rename is mock_rename assert _test_patching.os.path.join is original_join assert _test_patching.os.path.dirname is original_dirname assert _test_patching.os.rename is original_rename def lowerCamelCase__ ( ) -> str: """simple docstring""" _UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__''' with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ): pass with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ): pass
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'''simple docstring''' from __future__ import annotations def __a ( A__ ) -> None: create_state_space_tree(__snake_case , [] , 0 , [0 for i in range(len(__snake_case ) )] ) def __a ( A__ , A__ , A__ , A__ , ) -> None: if index == len(__snake_case ): print(__snake_case ) return for i in range(len(__snake_case ) ): if not index_used[i]: current_sequence.append(sequence[i] ) lowerCAmelCase = True create_state_space_tree(__snake_case , __snake_case , index + 1 , __snake_case ) current_sequence.pop() lowerCAmelCase = False lowercase : Union[str, Any] = [3, 1, 2, 4] generate_all_permutations(sequence) lowercase : List[str] = ['A', 'B', 'C'] generate_all_permutations(sequence_a)
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"""simple docstring""" import argparse import json from collections import OrderedDict from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor from transformers.utils import logging logging.set_verbosity_info() _a = logging.get_logger(__name__) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = original_name.split('''.''' )[0] _UpperCamelCase = key.split('''.''' ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] ) _UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] ) _UpperCamelCase = orig_block_num - offset _UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' ) return key def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase = OrderedDict() _UpperCamelCase , _UpperCamelCase = 0, 0 for key, value in state_dict.items(): if key.startswith('''network''' ): _UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' ) if "proj" in key: # Works for the first embedding as well as the internal embedding layers if key.endswith('''bias''' ) and "patch_embed" not in key: patch_emb_offset += 1 _UpperCamelCase = key[: key.find('''proj''' )] _UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' ) _UpperCamelCase = key.replace('''proj''', '''projection''' ) if key.endswith('''bias''' ): total_embed_found += 1 if "patch_embeddings" in key: _UpperCamelCase = '''poolformer.encoder.''' + key if "mlp.fc1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' ) if "mlp.fc2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' ) if "norm1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' ) if "norm2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' ) if "layer_scale_1" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' ) if "layer_scale_2" in key: _UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' ) if "head" in key: _UpperCamelCase = key.replace('''head''', '''classifier''' ) _UpperCamelCase = value return new_state_dict def lowerCamelCase__ ( ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg''' _UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw ) return image @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int: """simple docstring""" _UpperCamelCase = PoolFormerConfig() # set attributes based on model_name _UpperCamelCase = '''huggingface/label-files''' _UpperCamelCase = model_name[-3:] _UpperCamelCase = 10_00 _UpperCamelCase = '''imagenet-1k-id2label.json''' _UpperCamelCase = (1, 10_00) # set config attributes _UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) ) _UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()} _UpperCamelCase = idalabel _UpperCamelCase = {v: k for k, v in idalabel.items()} if size == "s12": _UpperCamelCase = [2, 2, 6, 2] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s24": _UpperCamelCase = [4, 4, 12, 4] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 0.9 elif size == "s36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [64, 1_28, 3_20, 5_12] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.9 elif size == "m36": _UpperCamelCase = [6, 6, 18, 6] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 elif size == "m48": _UpperCamelCase = [8, 8, 24, 8] _UpperCamelCase = [96, 1_92, 3_84, 7_68] _UpperCamelCase = 4.0 _UpperCamelCase = 1e-6 _UpperCamelCase = 0.95 else: raise ValueError(F'''Size {size} not supported''' ) # load image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) # Prepare image _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values logger.info(F'''Converting model {model_name}...''' ) # load original state dict _UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) ) # rename keys _UpperCamelCase = rename_keys(__snake_case ) # create HuggingFace model and load state dict _UpperCamelCase = PoolFormerForImageClassification(__snake_case ) model.load_state_dict(__snake_case ) model.eval() # Define image processor _UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case ) _UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values # forward pass _UpperCamelCase = model(__snake_case ) _UpperCamelCase = outputs.logits # define expected logit slices for different models if size == "s12": _UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] ) elif size == "s24": _UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] ) elif size == "s36": _UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] ) elif size == "m36": _UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] ) elif size == "m48": _UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] ) else: raise ValueError(F'''Size {size} not supported''' ) # verify logits assert logits.shape == expected_shape assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 ) # finally, save model and image processor logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' ) Path(__snake_case ).mkdir(exist_ok=__snake_case ) model.save_pretrained(__snake_case ) print(F'''Saving image processor to {pytorch_dump_folder_path}''' ) image_processor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument( """--model_name""", default="""poolformer_s12""", type=str, help="""Name of the model you'd like to convert.""", ) parser.add_argument( """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file).""" ) parser.add_argument( """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model.""" ) _a = parser.parse_args() convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
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'''simple docstring''' import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging UpperCamelCase__ : Dict = logging.get_logger(__name__) UpperCamelCase__ : Tuple = '▁' UpperCamelCase__ : Dict = {'vocab_file': 'sentencepiece.bpe.model'} UpperCamelCase__ : Any = { 'vocab_file': { 'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model', } } UpperCamelCase__ : Any = { 'facebook/xglm-564M': 2_048, } class _lowercase ( lowerCAmelCase ): '''simple docstring''' UpperCAmelCase_ : Dict = VOCAB_FILES_NAMES UpperCAmelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP UpperCAmelCase_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCAmelCase_ : Optional[Any] = ['''input_ids''', '''attention_mask'''] def __init__( self ,lowerCamelCase_ ,lowerCamelCase_="<s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="<s>" ,lowerCamelCase_="<unk>" ,lowerCamelCase_="<pad>" ,lowerCamelCase_ = None ,**lowerCamelCase_ ,) -> None: '''simple docstring''' UpperCAmelCase__ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs # Compatibility with the original tokenizer UpperCAmelCase__ : int = 7 UpperCAmelCase__ : Any = [f'''<madeupword{i}>''' for i in range(self.num_madeup_words )] UpperCAmelCase__ : Any = kwargs.get('''additional_special_tokens''' ,[] ) kwargs["additional_special_tokens"] += [ word for word in madeup_words if word not in kwargs["additional_special_tokens"] ] super().__init__( bos_token=__a ,eos_token=__a ,unk_token=__a ,sep_token=__a ,cls_token=__a ,pad_token=__a ,sp_model_kwargs=self.sp_model_kwargs ,**__a ,) UpperCAmelCase__ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__a ) ) UpperCAmelCase__ : List[Any] = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab UpperCAmelCase__ : Optional[Any] = 1 # Mimic fairseq token-to-id alignment for the first 4 token UpperCAmelCase__ : Any = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3} UpperCAmelCase__ : Dict = len(self.sp_model ) UpperCAmelCase__ : List[str] = {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )} self.fairseq_tokens_to_ids.update(__a ) UpperCAmelCase__ : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def __getstate__( self ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase__ : Optional[Any] = self.__dict__.copy() UpperCAmelCase__ : Dict = None UpperCAmelCase__ : Union[str, Any] = self.sp_model.serialized_model_proto() return state def __setstate__( self ,lowerCamelCase_ ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase__ : Tuple = d # for backward compatibility if not hasattr(self ,'''sp_model_kwargs''' ): UpperCAmelCase__ : Dict = {} UpperCAmelCase__ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.LoadFromSerializedProto(self.sp_model_proto ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.sep_token_id] + token_ids_a UpperCAmelCase__ : int = [self.sep_token_id] return sep + token_ids_a + sep + sep + token_ids_a def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__a ,token_ids_a=__a ,already_has_special_tokens=__a ) if token_ids_a is None: return [1] + ([0] * len(__a )) return [1] + ([0] * len(__a )) + [1, 1] + ([0] * len(__a )) def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[int]: '''simple docstring''' UpperCAmelCase__ : str = [self.sep_token_id] if token_ids_a is None: return len(sep + token_ids_a ) * [0] return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0] @property def lowerCAmelCase__ ( self ) -> str: '''simple docstring''' return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words def lowerCAmelCase__ ( self ) -> List[str]: '''simple docstring''' UpperCAmelCase__ : Dict = {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> List[str]: '''simple docstring''' return self.sp_model.encode(__a ,out_type=__a ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Union[str, Any]: '''simple docstring''' if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] UpperCAmelCase__ : Any = self.sp_model.PieceToId(__a ) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Tuple: '''simple docstring''' if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset ) def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> int: '''simple docstring''' UpperCAmelCase__ : Tuple = ''''''.join(__a ).replace(__a ,''' ''' ).strip() return out_string def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(__a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCAmelCase__ : List[Any] = os.path.join( __a ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file ,__a ) elif not os.path.isfile(self.vocab_file ): with open(__a ,'''wb''' ) as fi: UpperCAmelCase__ : Tuple = self.sp_model.serialized_model_proto() fi.write(__a ) return (out_vocab_file,)
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"""simple docstring""" import torch from diffusers import DPMSolverSDEScheduler from diffusers.utils import torch_device from diffusers.utils.testing_utils import require_torchsde from .test_schedulers import SchedulerCommonTest @require_torchsde class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DPMSolverSDEScheduler,) lowercase__ = 10 def UpperCAmelCase ( self , **__a) -> int: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 11_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''noise_sampler_seed''': 0, } config.update(**__a) return config def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' for timesteps in [10, 50, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> str: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2 assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for i, t in enumerate(scheduler.timesteps): _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2 assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2 assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3 else: assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2 assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2 assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2 assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3 else: assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2 assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a) scheduler.set_timesteps(self.num_inference_steps , device=__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma _UpperCamelCase = sample.to(__a) for t in scheduler.timesteps: _UpperCamelCase = scheduler.scale_model_input(__a , __a) _UpperCamelCase = model(__a , __a) _UpperCamelCase = scheduler.step(__a , __a , __a) _UpperCamelCase = output.prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) if torch_device in ["mps"]: assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 elif torch_device in ["cuda"]: assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2 else: assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
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'''simple docstring''' from typing import Callable, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging lowercase =logging.get_logger(__name__) lowercase ={ 'microsoft/xprophetnet-large-wiki100-cased': ( 'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json' ), } class __magic_name__ ( lowerCAmelCase ): UpperCAmelCase ="xlm-prophetnet" UpperCAmelCase =["past_key_values"] UpperCAmelCase ={ "num_attention_heads": "num_encoder_attention_heads", } def __init__( self , snake_case = 0.1 , snake_case = "gelu" , snake_case = 3_0_5_2_2 , snake_case = 1_0_2_4 , snake_case = 4_0_9_6 , snake_case = 1_2 , snake_case = 1_6 , snake_case = 4_0_9_6 , snake_case = 1_2 , snake_case = 1_6 , snake_case = 0.1 , snake_case = 0.1 , snake_case = 5_1_2 , snake_case = 0.02 , snake_case = True , snake_case = True , snake_case = 0 , snake_case = 2 , snake_case = 3_2 , snake_case = 1_2_8 , snake_case = False , snake_case = 0.0 , snake_case = True , snake_case = 0 , snake_case = 1 , snake_case = 2 , **snake_case , ) -> List[Any]: '''simple docstring''' _UpperCAmelCase : List[Any] =vocab_size _UpperCAmelCase : int =hidden_size _UpperCAmelCase : Dict =encoder_ffn_dim _UpperCAmelCase : Tuple =num_encoder_layers _UpperCAmelCase : Optional[int] =num_encoder_attention_heads _UpperCAmelCase : str =decoder_ffn_dim _UpperCAmelCase : Any =num_decoder_layers _UpperCAmelCase : str =num_decoder_attention_heads _UpperCAmelCase : List[str] =max_position_embeddings _UpperCAmelCase : Dict =init_std # Normal(0, this parameter) _UpperCAmelCase : Optional[Any] =activation_function # parameters for xlmprophetnet _UpperCAmelCase : Dict =ngram _UpperCAmelCase : Dict =num_buckets _UpperCAmelCase : List[str] =relative_max_distance _UpperCAmelCase : Any =disable_ngram_loss _UpperCAmelCase : int =eps # 3 Types of Dropout _UpperCAmelCase : List[str] =attention_dropout _UpperCAmelCase : str =activation_dropout _UpperCAmelCase : Dict =dropout _UpperCAmelCase : List[str] =use_cache super().__init__( pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , add_cross_attention=__a , decoder_start_token_id=__a , **__a , ) @property def lowerCAmelCase ( self) -> int: '''simple docstring''' return self.num_encoder_layers + self.num_decoder_layers @num_hidden_layers.setter def lowerCAmelCase ( self , snake_case) -> str: '''simple docstring''' raise NotImplementedError( 'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and' ' `num_decoder_layers`.')
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"""simple docstring""" from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images, ) from ...utils import TensorType, logging _a = logging.get_logger(__name__) class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['pixel_values'] def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None: '''simple docstring''' super().__init__(**__a) _UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a) _UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24} _UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''') _UpperCamelCase = do_resize _UpperCamelCase = do_rescale _UpperCamelCase = do_normalize _UpperCamelCase = do_center_crop _UpperCamelCase = crop_size _UpperCamelCase = size _UpperCamelCase = resample _UpperCamelCase = rescale_factor _UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN _UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "shortest_edge" in size: _UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a) # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"]) elif "height" in size and "width" in size: _UpperCamelCase = (size['''height'''], size['''width''']) else: raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''') return resize(__a , size=__a , resample=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' _UpperCamelCase = get_size_dict(__a) if "height" not in size or "width" not in size: raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''') return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray: '''simple docstring''' return rescale(__a , scale=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray: '''simple docstring''' return normalize(__a , mean=__a , std=__a , data_format=__a , **__a) def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature: '''simple docstring''' _UpperCamelCase = do_resize if do_resize is not None else self.do_resize _UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale _UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize _UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop _UpperCamelCase = crop_size if crop_size is not None else self.crop_size _UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a) _UpperCamelCase = resample if resample is not None else self.resample _UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor _UpperCamelCase = image_mean if image_mean is not None else self.image_mean _UpperCamelCase = image_std if image_std is not None else self.image_std _UpperCamelCase = size if size is not None else self.size _UpperCamelCase = get_size_dict(__a) if not is_batched(__a): _UpperCamelCase = [images] if not valid_images(__a): raise ValueError( '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, ''' '''torch.Tensor, tf.Tensor or jax.ndarray.''') if do_resize and size is None: raise ValueError('''Size must be specified if do_resize is True.''') if do_center_crop and crop_size is None: raise ValueError('''Crop size must be specified if do_center_crop is True.''') if do_rescale and rescale_factor is None: raise ValueError('''Rescale factor must be specified if do_rescale is True.''') # All transformations expect numpy arrays. _UpperCamelCase = [to_numpy_array(__a) for image in images] if do_resize: _UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images] if do_center_crop: _UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images] if do_rescale: _UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images] if do_normalize: _UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images] _UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images] _UpperCamelCase = {'''pixel_values''': images} return BatchFeature(data=__a , tensor_type=__a)
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import sys import warnings from os.path import abspath, dirname, join # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. _lowerCamelCase : Tuple = abspath(join(dirname(dirname(__file__)), '''src''')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='''ignore''', category=FutureWarning) def _a ( SCREAMING_SNAKE_CASE__ : List[str] ) -> List[str]: '''simple docstring''' from diffusers.utils.testing_utils import pytest_addoption_shared pytest_addoption_shared(__snake_case ) def _a ( SCREAMING_SNAKE_CASE__ : Tuple ) -> Union[str, Any]: '''simple docstring''' from diffusers.utils.testing_utils import pytest_terminal_summary_main SCREAMING_SNAKE_CASE__ : str = terminalreporter.config.getoption("--make-reports" ) if make_reports: pytest_terminal_summary_main(__snake_case , id=__snake_case )
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"""simple docstring""" # Imports import numpy as np class _UpperCAmelCase: def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict: '''simple docstring''' if red is not None: _UpperCamelCase = red if green is not None: _UpperCamelCase = green if blue is not None: _UpperCamelCase = blue if red_edge is not None: _UpperCamelCase = red_edge if nir is not None: _UpperCamelCase = nir return True def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]: '''simple docstring''' self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a) _UpperCamelCase = { '''ARVI2''': self.arvaa, '''CCCI''': self.ccci, '''CVI''': self.cvi, '''GLI''': self.gli, '''NDVI''': self.ndvi, '''BNDVI''': self.bndvi, '''redEdgeNDVI''': self.red_edge_ndvi, '''GNDVI''': self.gndvi, '''GBNDVI''': self.gbndvi, '''GRNDVI''': self.grndvi, '''RBNDVI''': self.rbndvi, '''PNDVI''': self.pndvi, '''ATSAVI''': self.atsavi, '''BWDRVI''': self.bwdrvi, '''CIgreen''': self.ci_green, '''CIrededge''': self.ci_rededge, '''CI''': self.ci, '''CTVI''': self.ctvi, '''GDVI''': self.gdvi, '''EVI''': self.evi, '''GEMI''': self.gemi, '''GOSAVI''': self.gosavi, '''GSAVI''': self.gsavi, '''Hue''': self.hue, '''IVI''': self.ivi, '''IPVI''': self.ipvi, '''I''': self.i, '''RVI''': self.rvi, '''MRVI''': self.mrvi, '''MSAVI''': self.m_savi, '''NormG''': self.norm_g, '''NormNIR''': self.norm_nir, '''NormR''': self.norm_r, '''NGRDI''': self.ngrdi, '''RI''': self.ri, '''S''': self.s, '''IF''': self._if, '''DVI''': self.dvi, '''TVI''': self.tvi, '''NDRE''': self.ndre, } try: return funcs[index]() except KeyError: print('''Index not in the list!''') return False def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red))) def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / ( (self.nir - self.red) / (self.nir + self.red) ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir * (self.red / (self.green**2)) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.green - self.red - self.blue) / ( 2 * self.green + self.red + self.blue ) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - self.red) / (self.nir + self.red) def UpperCAmelCase ( self) -> str: '''simple docstring''' return (self.nir - self.blue) / (self.nir + self.blue) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.redEdge - self.red) / (self.redEdge + self.red) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir - (self.green + self.blue)) / ( self.nir + (self.green + self.blue) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.nir - (self.green + self.red)) / ( self.nir + (self.green + self.red) ) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red)) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir - (self.green + self.red + self.blue)) / ( self.nir + (self.green + self.red + self.blue) ) def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]: '''simple docstring''' return a * ( (self.nir - a * self.red - b) / (a * self.nir + self.red - a * b + x * (1 + a**2)) ) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return (self.nir / self.green) - 1 def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return (self.nir / self.redEdge) - 1 def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.red - self.blue) / self.red def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.ndvi() return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2)) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.nir - self.green def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' return 2.5 * ( (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1) ) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / ( self.nir + self.red + 0.5 ) return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red) def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]: '''simple docstring''' return (self.nir - self.green) / (self.nir + self.green + y) def UpperCAmelCase ( self , __a=0.5) -> Dict: '''simple docstring''' return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return np.arctan( ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)) def UpperCAmelCase ( self , __a=None , __a=None) -> Any: '''simple docstring''' return (self.nir - b) / (a * self.red) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return (self.red + self.green + self.blue) / 30.5 def UpperCAmelCase ( self) -> Any: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.rvi() - 1) / (self.rvi() + 1) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return ( (2 * self.nir + 1) - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2) ) / 2 def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.green / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> str: '''simple docstring''' return self.nir / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return self.red / (self.nir + self.red + self.green) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return (self.green - self.red) / (self.green + self.red) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' return (self.red - self.green) / (self.red + self.green) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)]) _UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)]) return (max_value - min_value) / max_value def UpperCAmelCase ( self) -> str: '''simple docstring''' return (2 * self.red - self.green - self.blue) / (self.green - self.blue) def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.nir / self.red def UpperCAmelCase ( self) -> Any: '''simple docstring''' return (self.ndvi() + 0.5) ** (1 / 2) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' return (self.nir - self.redEdge) / (self.nir + self.redEdge)
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from math import pi, sqrt def lowerCamelCase_(lowerCamelCase_ ) -> float: if num <= 0: raise ValueError("math domain error" ) if num > 171.5: raise OverflowError("math range error" ) elif num - int(__snake_case ) not in (0, 0.5): raise NotImplementedError("num must be an integer or a half-integer" ) elif num == 0.5: return sqrt(__snake_case ) else: return 1.0 if num == 1 else (num - 1) * gamma(num - 1 ) def lowerCamelCase_() -> None: assert gamma(0.5 ) == sqrt(__snake_case ) assert gamma(1 ) == 1.0 assert gamma(2 ) == 1.0 if __name__ == "__main__": from doctest import testmod testmod() __lowerCamelCase : int = 1.0 while num: __lowerCamelCase : List[Any] = float(input("Gamma of: ")) print(F'''gamma({num}) = {gamma(num)}''') print("\nEnter 0 to exit...")
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"""simple docstring""" import inspect import unittest from transformers import ViTHybridConfig from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = patch_size _UpperCamelCase = num_channels _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = scope _UpperCamelCase = backbone_featmap_shape # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) # the number of patches is based on the feature map of the backbone, which by default uses an output stride # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size _UpperCamelCase = (self.image_size // 32) ** 2 _UpperCamelCase = num_patches + 1 def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = { '''global_padding''': '''same''', '''layer_type''': '''bottleneck''', '''depths''': [3, 4, 9], '''out_features''': ['''stage1''', '''stage2''', '''stage3'''], '''embedding_dynamic_padding''': True, '''hidden_sizes''': [4, 8, 16, 32], '''num_groups''': 2, } return ViTHybridConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModel(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.type_sequence_label_size _UpperCamelCase = ViTHybridForImageClassification(__a) model.to(__a) model.eval() _UpperCamelCase = model(__a , labels=__a) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else () lowercase__ = ( {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification} if is_torch_available() else {} ) lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = ViTHybridModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Any: '''simple docstring''' self.config_tester.run_common_tests() @unittest.skip(reason='''ViT does not use inputs_embeds''') def UpperCAmelCase ( self) -> Dict: '''simple docstring''' pass def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) self.assertIsInstance(model.get_input_embeddings() , (nn.Module)) _UpperCamelCase = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__a , nn.Linear)) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "ViTHybridPatchEmbeddings": _UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if name in backbone_params: continue self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = ViTHybridModel.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" _UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class _UpperCAmelCase( unittest.TestCase ): @cached_property def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' return ( ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to( __a) _UpperCamelCase = self.default_image_processor _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a) # forward pass with torch.no_grad(): _UpperCamelCase = model(**__a) # verify the logits _UpperCamelCase = torch.Size((1, 10_00)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4)) @slow @require_accelerate def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''') _UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''') _UpperCamelCase = prepare_img() _UpperCamelCase = image_processor(images=__a , return_tensors='''pt''') _UpperCamelCase = model(**__a) _UpperCamelCase = outputs.logits # model predicts one of the 1000 ImageNet classes _UpperCamelCase = logits.argmax(-1).item() self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
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'''simple docstring''' import random import unittest import numpy as np import transformers from transformers import is_flax_available, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_flax if is_flax_available(): import os import jax.numpy as jnp from jax import jit from transformers import AutoTokenizer, FlaxAutoModelForCausalLM from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model __a: Optional[Any] = """0.12""" # assumed parallelism: 8 if is_torch_available(): import torch def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None ): if rng is None: lowercase__ : int = random.Random() lowercase__ : Union[str, Any] = 1 for dim in shape: total_dims *= dim lowercase__ : str = [] for _ in range(__snake_case ): values.append(rng.randint(0 , vocab_size - 1 ) ) lowercase__ : Optional[int] = np.array(__snake_case , dtype=jnp.intaa ).reshape(__snake_case ) return output def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=None ): lowercase__ : Optional[Any] = ids_tensor(__snake_case , vocab_size=2 , rng=__snake_case ) # make sure that at least one token is attended to for each batch lowercase__ : Dict = 1 return attn_mask @require_flax class UpperCAmelCase : '''simple docstring''' SCREAMING_SNAKE_CASE = None SCREAMING_SNAKE_CASE = () def _lowerCAmelCase( self ) -> str: lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common() # cut to half length & take max batch_size 3 lowercase__ : Union[str, Any] = 2 lowercase__ : List[str] = inputs['''input_ids'''].shape[-1] // 2 lowercase__ : Tuple = inputs['''input_ids'''][:max_batch_size, :sequence_length] lowercase__ : Any = jnp.ones_like(__a ) lowercase__ : Any = attention_mask[:max_batch_size, :sequence_length] # generate max 5 tokens lowercase__ : Tuple = input_ids.shape[-1] + 5 if config.eos_token_id is not None and config.pad_token_id is None: # hack to allow generate for models such as GPT2 as is done in `generate()` lowercase__ : Optional[Any] = config.eos_token_id return config, input_ids, attention_mask, max_length @is_pt_flax_cross_test def _lowerCAmelCase( self ) -> Union[str, Any]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[Any] = self._get_input_ids_and_config() lowercase__ : int = False lowercase__ : Optional[int] = max_length lowercase__ : List[str] = 0 for model_class in self.all_generative_model_classes: lowercase__ : Any = model_class(__a ) lowercase__ : int = model_class.__name__[4:] # Skip the "Flax" at the beginning lowercase__ : Optional[Any] = getattr(__a , __a ) lowercase__ : Optional[Any] = pt_model_class(__a ).eval() lowercase__ : List[str] = load_flax_weights_in_pytorch_model(__a , flax_model.params ) lowercase__ : str = flax_model.generate(__a ).sequences lowercase__ : Tuple = pt_model.generate(torch.tensor(__a , dtype=torch.long ) ) if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]: lowercase__ : Dict = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]] self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> Dict: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = self._get_input_ids_and_config() lowercase__ : Dict = False lowercase__ : Optional[int] = max_length for model_class in self.all_generative_model_classes: lowercase__ : Optional[int] = model_class(__a ) lowercase__ : Optional[Any] = model.generate(__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : str = jit(model.generate ) lowercase__ : Tuple = jit_generate(__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> Optional[Any]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Any = self._get_input_ids_and_config() lowercase__ : str = True lowercase__ : Optional[int] = max_length for model_class in self.all_generative_model_classes: lowercase__ : int = model_class(__a ) lowercase__ : Optional[int] = model.generate(__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : int = jit(model.generate ) lowercase__ : Optional[int] = jit_generate(__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> List[str]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = self._get_input_ids_and_config() lowercase__ : Optional[int] = False lowercase__ : int = max_length lowercase__ : Tuple = 2 for model_class in self.all_generative_model_classes: lowercase__ : int = model_class(__a ) lowercase__ : Optional[int] = model.generate(__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : int = jit(model.generate ) lowercase__ : str = jit_generate(__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> Optional[Any]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Optional[Any] = self._get_input_ids_and_config() lowercase__ : Optional[Any] = False lowercase__ : Dict = max_length lowercase__ : List[Any] = 2 lowercase__ : Optional[Any] = 2 for model_class in self.all_generative_model_classes: lowercase__ : Optional[Any] = model_class(__a ) lowercase__ : Union[str, Any] = model.generate(__a ).sequences self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences ) def _lowerCAmelCase( self ) -> Optional[Any]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : int = self._get_input_ids_and_config() lowercase__ : int = True lowercase__ : Optional[int] = max_length lowercase__ : Optional[int] = 0.8 lowercase__ : Optional[int] = 10 lowercase__ : Optional[int] = 0.3 lowercase__ : Tuple = 1 lowercase__ : List[str] = 8 lowercase__ : int = 9 for model_class in self.all_generative_model_classes: lowercase__ : str = model_class(__a ) lowercase__ : str = model.generate(__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : Tuple = jit(model.generate ) lowercase__ : List[str] = jit_generate(__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> Optional[Any]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = self._get_input_ids_and_config() lowercase__ : Optional[Any] = max_length lowercase__ : Optional[int] = 1 lowercase__ : List[Any] = 8 lowercase__ : str = 9 for model_class in self.all_generative_model_classes: lowercase__ : Optional[Any] = model_class(__a ) lowercase__ : str = model.generate(__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : Tuple = jit(model.generate ) lowercase__ : List[Any] = jit_generate(__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> Optional[int]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Optional[int] = self._get_input_ids_and_config() lowercase__ : List[Any] = max_length lowercase__ : Dict = 2 lowercase__ : int = 1 lowercase__ : Optional[int] = 8 lowercase__ : int = 9 for model_class in self.all_generative_model_classes: lowercase__ : List[str] = model_class(__a ) lowercase__ : Dict = model.generate(__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : Union[str, Any] = jit(model.generate ) lowercase__ : Tuple = jit_generate(__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> List[Any]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = self._get_input_ids_and_config() # pad attention mask on the left lowercase__ : int = attention_mask.at[(0, 0)].set(0 ) lowercase__ : str = False lowercase__ : Union[str, Any] = max_length for model_class in self.all_generative_model_classes: lowercase__ : Optional[int] = model_class(__a ) lowercase__ : int = model.generate(__a , attention_mask=__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : Any = jit(model.generate ) lowercase__ : int = jit_generate(__a , attention_mask=__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> Optional[Any]: lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = self._get_input_ids_and_config() # pad attention mask on the left lowercase__ : List[Any] = attention_mask.at[(0, 0)].set(0 ) lowercase__ : Optional[Any] = True lowercase__ : List[str] = max_length for model_class in self.all_generative_model_classes: lowercase__ : List[str] = model_class(__a ) lowercase__ : Union[str, Any] = model.generate(__a , attention_mask=__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : Optional[int] = jit(model.generate ) lowercase__ : Optional[Any] = jit_generate(__a , attention_mask=__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) def _lowerCAmelCase( self ) -> int: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = self._get_input_ids_and_config() # pad attention mask on the left lowercase__ : str = attention_mask.at[(0, 0)].set(0 ) lowercase__ : Optional[Any] = 2 lowercase__ : Union[str, Any] = max_length for model_class in self.all_generative_model_classes: lowercase__ : List[Any] = model_class(__a ) lowercase__ : str = model.generate(__a , attention_mask=__a ).sequences self.assertEqual(generation_outputs.shape[-1] , __a ) lowercase__ : List[Any] = jit(model.generate ) lowercase__ : Tuple = jit_generate(__a , attention_mask=__a ).sequences self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() ) @require_flax class UpperCAmelCase ( unittest.TestCase ): '''simple docstring''' def _lowerCAmelCase( self ) -> str: lowercase__ : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' ) lowercase__ : str = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) lowercase__ : List[str] = '''Hello world''' lowercase__ : Optional[Any] = tokenizer(__a , return_tensors='''np''' ).input_ids # typos are quickly detected (the correct argument is `do_sample`) with self.assertRaisesRegex(__a , '''do_samples''' ): model.generate(__a , do_samples=__a ) # arbitrary arguments that will not be used anywhere are also not accepted with self.assertRaisesRegex(__a , '''foo''' ): lowercase__ : str = {'''foo''': '''bar'''} model.generate(__a , **__a )
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"""simple docstring""" from math import acos, sin from typing import List, Tuple, Union import numpy as np import torch from PIL import Image from ...models import AutoencoderKL, UNetaDConditionModel from ...schedulers import DDIMScheduler, DDPMScheduler from ...utils import randn_tensor from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput from .mel import Mel class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['vqvae'] def __init__( self , __a , __a , __a , __a , ) -> List[str]: '''simple docstring''' super().__init__() self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' return 50 if isinstance(self.scheduler , __a) else 10_00 @torch.no_grad() def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[ Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]], ]: '''simple docstring''' _UpperCamelCase = steps or self.get_default_steps() self.scheduler.set_timesteps(__a) _UpperCamelCase = step_generator or generator # For backwards compatibility if type(self.unet.config.sample_size) == int: _UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size) if noise is None: _UpperCamelCase = randn_tensor( ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1], ) , generator=__a , device=self.device , ) _UpperCamelCase = noise _UpperCamelCase = None if audio_file is not None or raw_audio is not None: self.mel.load_audio(__a , __a) _UpperCamelCase = self.mel.audio_slice_to_image(__a) _UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape( (input_image.height, input_image.width)) _UpperCamelCase = (input_image / 2_55) * 2 - 1 _UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device) if self.vqvae is not None: _UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample( generator=__a)[0] _UpperCamelCase = self.vqvae.config.scaling_factor * input_images if start_step > 0: _UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1]) _UpperCamelCase = ( self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length ) _UpperCamelCase = int(mask_start_secs * pixels_per_second) _UpperCamelCase = int(mask_end_secs * pixels_per_second) _UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:])) for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])): if isinstance(self.unet , __a): _UpperCamelCase = self.unet(__a , __a , __a)['''sample'''] else: _UpperCamelCase = self.unet(__a , __a)['''sample'''] if isinstance(self.scheduler , __a): _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample'''] else: _UpperCamelCase = self.scheduler.step( model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample'''] if mask is not None: if mask_start > 0: _UpperCamelCase = mask[:, step, :, :mask_start] if mask_end > 0: _UpperCamelCase = mask[:, step, :, -mask_end:] if self.vqvae is not None: # 0.18215 was scaling factor used in training to ensure unit variance _UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images _UpperCamelCase = self.vqvae.decode(__a)['''sample'''] _UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1) _UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy() _UpperCamelCase = (images * 2_55).round().astype('''uint8''') _UpperCamelCase = list( (Image.fromarray(_[:, :, 0]) for _ in images) if images.shape[3] == 1 else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images)) _UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images] if not return_dict: return images, (self.mel.get_sample_rate(), audios) return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a)) @torch.no_grad() def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray: '''simple docstring''' assert isinstance(self.scheduler , __a) self.scheduler.set_timesteps(__a) _UpperCamelCase = np.array( [np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images]) _UpperCamelCase = (sample / 2_55) * 2 - 1 _UpperCamelCase = torch.Tensor(__a).to(self.device) for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))): _UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps _UpperCamelCase = self.scheduler.alphas_cumprod[t] _UpperCamelCase = ( self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod ) _UpperCamelCase = 1 - alpha_prod_t _UpperCamelCase = self.unet(__a , __a)['''sample'''] _UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output _UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5) _UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output return sample @staticmethod def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor: '''simple docstring''' _UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a)) return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
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from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging if TYPE_CHECKING: from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType _snake_case = logging.get_logger(__name__) _snake_case = { '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''', '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''', '''microsoft/deberta-v2-xlarge-mnli''': ( '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json''' ), '''microsoft/deberta-v2-xxlarge-mnli''': ( '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json''' ), } class UpperCAmelCase_ ( UpperCamelCase ): '''simple docstring''' __A : List[str] = "deberta-v2" def __init__( self , __A=12_8100 , __A=1536 , __A=24 , __A=24 , __A=6144 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0 , __A=0.02 , __A=1e-7 , __A=False , __A=-1 , __A=0 , __A=True , __A=None , __A=0 , __A="gelu" , **__A , ): """simple docstring""" super().__init__(**__a ) lowerCamelCase : Tuple = hidden_size lowerCamelCase : Optional[Any] = num_hidden_layers lowerCamelCase : Optional[int] = num_attention_heads lowerCamelCase : str = intermediate_size lowerCamelCase : List[Any] = hidden_act lowerCamelCase : Optional[Any] = hidden_dropout_prob lowerCamelCase : Dict = attention_probs_dropout_prob lowerCamelCase : List[str] = max_position_embeddings lowerCamelCase : Any = type_vocab_size lowerCamelCase : Tuple = initializer_range lowerCamelCase : Optional[Any] = relative_attention lowerCamelCase : List[Any] = max_relative_positions lowerCamelCase : int = pad_token_id lowerCamelCase : Optional[int] = position_biased_input # Backwards compatibility if type(__a ) == str: lowerCamelCase : Optional[Any] = [x.strip() for x in pos_att_type.lower().split("|" )] lowerCamelCase : Any = pos_att_type lowerCamelCase : Tuple = vocab_size lowerCamelCase : int = layer_norm_eps lowerCamelCase : int = kwargs.get("pooler_hidden_size" , __a ) lowerCamelCase : Optional[Any] = pooler_dropout lowerCamelCase : Tuple = pooler_hidden_act class UpperCAmelCase_ ( UpperCamelCase ): '''simple docstring''' @property def _snake_case ( self ): """simple docstring""" if self.task == "multiple-choice": lowerCamelCase : Any = {0: "batch", 1: "choice", 2: "sequence"} else: lowerCamelCase : Tuple = {0: "batch", 1: "sequence"} if self._config.type_vocab_size > 0: return OrderedDict( [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] ) else: return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] ) @property def _snake_case ( self ): """simple docstring""" return 12 def _snake_case ( self , __A , __A = -1 , __A = -1 , __A = -1 , __A = False , __A = None , __A = 3 , __A = 40 , __A = 40 , __A = None , ): """simple docstring""" lowerCamelCase : Tuple = super().generate_dummy_inputs(preprocessor=__a , framework=__a ) if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs: del dummy_inputs["token_type_ids"] return dummy_inputs
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"""simple docstring""" import copy from collections import OrderedDict from typing import Dict, Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING _a = logging.get_logger(__name__) _a = { """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""", # See all DETR models at https://huggingface.co/models?filter=detr } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'detr' lowercase__ = ['past_key_values'] lowercase__ = { 'hidden_size': 'd_model', 'num_attention_heads': 'encoder_attention_heads', } def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int: '''simple docstring''' if backbone_config is not None and use_timm_backbone: raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''') if not use_timm_backbone: if backbone_config is None: logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''') _UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']) elif isinstance(__a , __a): _UpperCamelCase = backbone_config.get('''model_type''') _UpperCamelCase = CONFIG_MAPPING[backbone_model_type] _UpperCamelCase = config_class.from_dict(__a) # set timm attributes to None _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None _UpperCamelCase = use_timm_backbone _UpperCamelCase = backbone_config _UpperCamelCase = num_channels _UpperCamelCase = num_queries _UpperCamelCase = d_model _UpperCamelCase = encoder_ffn_dim _UpperCamelCase = encoder_layers _UpperCamelCase = encoder_attention_heads _UpperCamelCase = decoder_ffn_dim _UpperCamelCase = decoder_layers _UpperCamelCase = decoder_attention_heads _UpperCamelCase = dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = activation_function _UpperCamelCase = init_std _UpperCamelCase = init_xavier_std _UpperCamelCase = encoder_layerdrop _UpperCamelCase = decoder_layerdrop _UpperCamelCase = encoder_layers _UpperCamelCase = auxiliary_loss _UpperCamelCase = position_embedding_type _UpperCamelCase = backbone _UpperCamelCase = use_pretrained_backbone _UpperCamelCase = dilation # Hungarian matcher _UpperCamelCase = class_cost _UpperCamelCase = bbox_cost _UpperCamelCase = giou_cost # Loss coefficients _UpperCamelCase = mask_loss_coefficient _UpperCamelCase = dice_loss_coefficient _UpperCamelCase = bbox_loss_coefficient _UpperCamelCase = giou_loss_coefficient _UpperCamelCase = eos_coefficient super().__init__(is_encoder_decoder=__a , **__a) @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.encoder_attention_heads @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return self.d_model @classmethod def UpperCAmelCase ( cls , __a , **__a) -> int: '''simple docstring''' return cls(backbone_config=__a , **__a) def UpperCAmelCase ( self) -> Dict[str, any]: '''simple docstring''' _UpperCamelCase = copy.deepcopy(self.__dict__) if output["backbone_config"] is not None: _UpperCamelCase = self.backbone_config.to_dict() _UpperCamelCase = self.__class__.model_type return output class _UpperCAmelCase( lowerCamelCase ): lowercase__ = version.parse('1.11' ) @property def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' return OrderedDict( [ ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}), ('''pixel_mask''', {0: '''batch'''}), ]) @property def UpperCAmelCase ( self) -> float: '''simple docstring''' return 1e-5 @property def UpperCAmelCase ( self) -> int: '''simple docstring''' return 12
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0
import math def lowerCAmelCase__ ( lowerCamelCase_ : Dict): '''simple docstring''' lowerCAmelCase__ : Dict = 0 lowerCAmelCase__ : Union[str, Any] = 0 while num > 0: lowerCAmelCase__ : List[Any] = num % 8 lowerCAmelCase__ : str = octal + (remainder * math.floor(math.pow(10 ,__snake_case))) counter += 1 lowerCAmelCase__ : Any = math.floor(num / 8) # basically /= 8 without remainder if any # This formatting removes trailing '.0' from `octal`. return f"""0o{int(__snake_case)}""" def lowerCAmelCase__ ( ): '''simple docstring''' print('''\n2 in octal is:''') print(decimal_to_octal(2)) # = 2 print('''\n8 in octal is:''') print(decimal_to_octal(8)) # = 10 print('''\n65 in octal is:''') print(decimal_to_octal(65)) # = 101 print('''\n216 in octal is:''') print(decimal_to_octal(216)) # = 330 print('''\n512 in octal is:''') print(decimal_to_octal(512)) # = 1000 print('''\n''') if __name__ == "__main__": main()
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"""simple docstring""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging _a = logging.get_logger(__name__) _a = { """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""", # See all WavLM models at https://huggingface.co/models?filter=wavlm } class _UpperCAmelCase( lowerCamelCase ): lowercase__ = 'wavlm' def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]: '''simple docstring''' super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a) _UpperCamelCase = hidden_size _UpperCamelCase = feat_extract_norm _UpperCamelCase = feat_extract_activation _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = conv_bias _UpperCamelCase = num_buckets _UpperCamelCase = max_bucket_distance _UpperCamelCase = num_conv_pos_embeddings _UpperCamelCase = num_conv_pos_embedding_groups _UpperCamelCase = len(self.conv_dim) _UpperCamelCase = num_hidden_layers _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = num_attention_heads _UpperCamelCase = hidden_dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = feat_proj_dropout _UpperCamelCase = final_dropout _UpperCamelCase = layerdrop _UpperCamelCase = layer_norm_eps _UpperCamelCase = initializer_range _UpperCamelCase = num_ctc_classes _UpperCamelCase = vocab_size _UpperCamelCase = do_stable_layer_norm _UpperCamelCase = use_weighted_layer_sum _UpperCamelCase = classifier_proj_size if ( (len(self.conv_stride) != self.num_feat_extract_layers) or (len(self.conv_kernel) != self.num_feat_extract_layers) or (len(self.conv_dim) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==''' ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =''' F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,''' F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''') # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _UpperCamelCase = apply_spec_augment _UpperCamelCase = mask_time_prob _UpperCamelCase = mask_time_length _UpperCamelCase = mask_time_min_masks _UpperCamelCase = mask_feature_prob _UpperCamelCase = mask_feature_length # parameters for pretraining with codevector quantized representations _UpperCamelCase = num_codevectors_per_group _UpperCamelCase = num_codevector_groups _UpperCamelCase = contrastive_logits_temperature _UpperCamelCase = num_negatives _UpperCamelCase = codevector_dim _UpperCamelCase = proj_codevector_dim _UpperCamelCase = diversity_loss_weight # ctc loss _UpperCamelCase = ctc_loss_reduction _UpperCamelCase = ctc_zero_infinity # adapter _UpperCamelCase = add_adapter _UpperCamelCase = adapter_kernel_size _UpperCamelCase = adapter_stride _UpperCamelCase = num_adapter_layers _UpperCamelCase = output_hidden_size or hidden_size # SequenceClassification-specific parameter. Feel free to ignore for other classes. _UpperCamelCase = classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = list(__a) _UpperCamelCase = xvector_output_dim @property def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' return functools.reduce(operator.mul , self.conv_stride , 1)
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'''simple docstring''' import argparse import logging import os import sys import numpy as np import onnxruntime import torch from bart_onnx.generation_onnx import BARTBeamSearchGenerator from bart_onnx.reduce_onnx_size import remove_dup_initializers import transformers from transformers import BartForConditionalGeneration, BartTokenizer logging.basicConfig( format="""%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s""", datefmt="""%Y-%m-%d %H:%M:%S""", level=os.environ.get("""LOGLEVEL""", """INFO""").upper(), stream=sys.stdout, ) __lowerCAmelCase = logging.getLogger(__name__) __lowerCAmelCase = {"""facebook/bart-base""": BartForConditionalGeneration} __lowerCAmelCase = {"""facebook/bart-base""": BartTokenizer} def UpperCAmelCase_ (): """simple docstring""" _a : List[str] = argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.' ) parser.add_argument( '--validation_file' , type=__snake_case , default=__snake_case , help='A csv or a json file containing the validation data.' ) parser.add_argument( '--max_length' , type=__snake_case , default=5 , help='The maximum total input sequence length after tokenization.' , ) parser.add_argument( '--num_beams' , type=__snake_case , default=__snake_case , help=( 'Number of beams to use for evaluation. This argument will be ' 'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.' ) , ) parser.add_argument( '--model_name_or_path' , type=__snake_case , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=__snake_case , ) parser.add_argument( '--config_name' , type=__snake_case , default=__snake_case , help='Pretrained config name or path if not the same as model_name' , ) parser.add_argument( '--device' , type=__snake_case , default='cpu' , help='Device where the model will be run' , ) parser.add_argument('--output_file_path' , type=__snake_case , default=__snake_case , help='Where to store the final ONNX file.' ) _a : List[str] = parser.parse_args() return args def UpperCAmelCase_ (__a : List[str] , __a : Any="cpu" ): """simple docstring""" _a : Union[str, Any] = model_dict[model_name].from_pretrained(__snake_case ).to(__snake_case ) _a : Tuple = tokenizer_dict[model_name].from_pretrained(__snake_case ) if model_name in ["facebook/bart-base"]: _a : List[Any] = 0 _a : Any = None _a : List[Any] = 0 return huggingface_model, tokenizer def UpperCAmelCase_ (__a : List[Any] , __a : Any , __a : Optional[int] , __a : int , __a : Any ): """simple docstring""" model.eval() _a : List[Any] = None _a : int = torch.jit.script(BARTBeamSearchGenerator(__snake_case ) ) with torch.no_grad(): _a : Union[str, Any] = 'My friends are cool but they eat too many carbs.' _a : List[str] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1_0_2_4 , return_tensors='pt' ).to(model.device ) _a : List[str] = model.generate( inputs['input_ids'] , attention_mask=inputs['attention_mask'] , num_beams=__snake_case , max_length=__snake_case , early_stopping=__snake_case , decoder_start_token_id=model.config.decoder_start_token_id , ) torch.onnx.export( __snake_case , ( inputs['input_ids'], inputs['attention_mask'], num_beams, max_length, model.config.decoder_start_token_id, ) , __snake_case , opset_version=1_4 , input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id'] , output_names=['output_ids'] , dynamic_axes={ 'input_ids': {0: 'batch', 1: 'seq'}, 'output_ids': {0: 'batch', 1: 'seq_out'}, } , example_outputs=__snake_case , ) logger.info('Model exported to {}'.format(__snake_case ) ) _a : Union[str, Any] = remove_dup_initializers(os.path.abspath(__snake_case ) ) logger.info('Deduplicated and optimized model written to {}'.format(__snake_case ) ) _a : List[Any] = onnxruntime.InferenceSession(__snake_case ) _a : Optional[Any] = ort_sess.run( __snake_case , { 'input_ids': inputs['input_ids'].cpu().numpy(), 'attention_mask': inputs['attention_mask'].cpu().numpy(), 'num_beams': np.array(__snake_case ), 'max_length': np.array(__snake_case ), 'decoder_start_token_id': np.array(model.config.decoder_start_token_id ), } , ) np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1e-3 , atol=1e-3 ) logger.info('Model outputs from torch and ONNX Runtime are similar.' ) logger.info('Success.' ) def UpperCAmelCase_ (): """simple docstring""" _a : str = parse_args() _a : Tuple = 5 _a : List[Any] = 4 # Make one log on every process with the configuration for debugging. logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , ) logger.setLevel(logging.INFO ) transformers.utils.logging.set_verbosity_error() _a : Any = torch.device(args.device ) _a, _a : Any = load_model_tokenizer(args.model_name_or_path , __snake_case ) if model.config.decoder_start_token_id is None: raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined' ) model.to(__snake_case ) if args.max_length: _a : Dict = args.max_length if args.num_beams: _a : Optional[int] = args.num_beams if args.output_file_path: _a : Dict = args.output_file_path else: _a : Union[str, Any] = 'BART.onnx' logger.info('Exporting model to ONNX' ) export_and_validate_model(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) if __name__ == "__main__": main()
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"""simple docstring""" import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer _a = """bart""" _a = True @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Dict: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' ) _UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' ) _UpperCamelCase = qar_model.eval() else: _UpperCamelCase , _UpperCamelCase = (None, None) if MODEL_TYPE == "bart": _UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' ) _UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' ) _UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' ) sas_model.load_state_dict(save_dict['''model'''] ) _UpperCamelCase = sas_model.eval() else: _UpperCamelCase , _UpperCamelCase = make_qa_sas_model( model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' ) return (qar_tokenizer, qar_model, sas_tokenizer, sas_model) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> Tuple: """simple docstring""" if LOAD_DENSE_INDEX: _UpperCamelCase = faiss.StandardGpuResources() _UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train'''] _UpperCamelCase = np.memmap( '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) _UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case ) wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU else: _UpperCamelCase , _UpperCamelCase = (None, None) _UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] ) return (wikiaab_passages, wikiaab_gpu_index_flat, es_client) @st.cache(allow_output_mutation=__snake_case ) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' ) _UpperCamelCase = elia['''train_eli5'''] _UpperCamelCase = np.memmap( '''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) ) _UpperCamelCase = faiss.IndexFlatIP(1_28 ) eli5_train_q_index.add(__snake_case ) return (elia_train, eli5_train_q_index) _a , _a , _a = load_indexes() _a , _a , _a , _a = load_models() _a , _a = load_train_data() def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]: """simple docstring""" _UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case ) _UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case ) _UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]] return nn_examples def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]: """simple docstring""" if source == "none": _UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), []) else: if method == "dense": _UpperCamelCase , _UpperCamelCase = query_qa_dense_index( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) else: _UpperCamelCase , _UpperCamelCase = query_es_index( __snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, ) _UpperCamelCase = [ (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst ] _UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case ) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda __snake_case : None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None), } ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict: """simple docstring""" with torch.no_grad(): _UpperCamelCase = qa_sas_generate( __snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0] return (answer, support_list) st.title("""Long Form Question Answering with ELI5""") # Start sidebar _a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>""" _a = """ <html> <head> <style> .img-container { padding-left: 90px; padding-right: 90px; padding-top: 50px; padding-bottom: 50px; background-color: #f0f3f9; } </style> </head> <body> <span class=\"img-container\"> <!-- Inline parent element --> %s </span> </body> </html> """ % ( header_html, ) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia _a = """ This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html). First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset, a pre-processed fixed snapshot of Wikipedia. """ st.sidebar.markdown(description, unsafe_allow_html=True) _a = [ """Answer the question""", """View the retrieved document only""", """View the most similar ELI5 question and answer""", """Show me everything, please!""", ] _a = st.sidebar.checkbox("""Demo options""") if demo_options: _a = st.sidebar.selectbox( """""", action_list, index=3, ) _a = action_list.index(action_st) _a = st.sidebar.selectbox( """""", ["""Show full text of passages""", """Show passage section titles"""], index=0, ) _a = show_type == """Show full text of passages""" else: _a = 3 _a = True _a = st.sidebar.checkbox("""Retrieval options""") if retrieval_options: _a = """ ### Information retriever options The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs. The answer is then generated by sequence to sequence model which takes the question and retrieved document as input. """ st.sidebar.markdown(retriever_info) _a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""]) _a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""]) else: _a = """wiki40b""" _a = """dense""" _a = """beam""" _a = 2 _a = 64 _a = 256 _a = None _a = None _a = st.sidebar.checkbox("""Generation options""") if generate_options: _a = """ ### Answer generation options The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large) weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with **beam** search, or **sample** from the decoder's output probabilities. """ st.sidebar.markdown(generate_info) _a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""]) _a = st.sidebar.slider( """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None ) _a = st.sidebar.slider( """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None ) if sampled == "beam": _a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: _a = st.sidebar.slider( """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) _a = st.sidebar.slider( """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) _a = None # start main text _a = [ """<MY QUESTION>""", """How do people make chocolate?""", """Why do we get a fever when we are sick?""", """How can different animals perceive different colors?""", """What is natural language processing?""", """What's the best way to treat a sunburn?""", """What exactly are vitamins ?""", """How does nuclear energy provide electricity?""", """What's the difference between viruses and bacteria?""", """Why are flutes classified as woodwinds when most of them are made out of metal ?""", """Why do people like drinking coffee even though it tastes so bad?""", """What happens when wine ages? How does it make the wine taste better?""", """If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""", """How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""", """How does New Zealand have so many large bird predators?""", ] _a = st.selectbox( """What would you like to ask? ---- select <MY QUESTION> to enter a new query""", questions_list, index=1, ) if question_s == "<MY QUESTION>": _a = st.text_input("""Enter your question here:""", """""") else: _a = question_s if st.button("""Show me!"""): if action in [0, 1, 3]: if index_type == "mixed": _a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10) _a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10) _a = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] _a = support_list[:10] _a = """<P> """ + """ <P> """.join([res[-1] for res in support_list]) else: _a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10) if action in [0, 3]: _a , _a = answer_question( question_doc, sas_model, sas_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == """sampled"""), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown("""### The model generated answer is:""") st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""") for i, res in enumerate(support_list): _a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_""")) _a = res[1].strip() if sec_titles == "": _a = """[{}]({})""".format(res[0], wiki_url) else: _a = sec_titles.split(""" & """) _a = """ & """.join( ["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list] ) st.markdown( """{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( """> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True ) if action in [2, 3]: _a = find_nearest_training(question) _a = nn_train_list[0] st.markdown( """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""]) ) _a = [ """{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""])) for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""])) if i == 0 or sc > 2 ] st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st))) _a = """ --- **Disclaimer** *The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system. Evaluating biases of such a model and ensuring factual generations are still very much open research problems. Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.* """ st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
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import os import re import shutil import sys import tempfile import unittest import black _lowerCamelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, '''utils''')) import check_copies # noqa: E402 # This is the reference code that will be used in the tests. # If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated. _lowerCamelCase : Union[str, Any] = ''' \"\"\" Output class for the scheduler\'s step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. \"\"\" prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None ''' class lowercase ( unittest.TestCase ): def __snake_case( self : Any ) -> Any: '''simple docstring''' SCREAMING_SNAKE_CASE = tempfile.mkdtemp() os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) ) SCREAMING_SNAKE_CASE = self.diffusers_dir shutil.copy( os.path.join(__a , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , ) def __snake_case( self : Optional[int] ) -> int: '''simple docstring''' SCREAMING_SNAKE_CASE = "src/diffusers" shutil.rmtree(self.diffusers_dir ) def __snake_case( self : Any , _UpperCamelCase : Dict , _UpperCamelCase : str , _UpperCamelCase : Any , _UpperCamelCase : Optional[int]=None ) -> str: '''simple docstring''' SCREAMING_SNAKE_CASE = comment + F"\nclass {class_name}(nn.Module):\n" + class_code if overwrite_result is not None: SCREAMING_SNAKE_CASE = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result SCREAMING_SNAKE_CASE = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 ) SCREAMING_SNAKE_CASE = black.format_str(__a , mode=__a ) SCREAMING_SNAKE_CASE = os.path.join(self.diffusers_dir , "new_code.py" ) with open(__a , "w" , newline="\n" ) as f: f.write(__a ) if overwrite_result is None: self.assertTrue(len(check_copies.is_copy_consistent(__a ) ) == 0 ) else: check_copies.is_copy_consistent(f.name , overwrite=__a ) with open(__a , "r" ) as f: self.assertTrue(f.read() , __a ) def __snake_case( self : Union[str, Any] ) -> List[Any]: '''simple docstring''' SCREAMING_SNAKE_CASE = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" ) self.assertEqual(__a , __a ) def __snake_case( self : Tuple ) -> Dict: '''simple docstring''' self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , ) # With no empty line at the end self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , __a , ) # Copy consistency with rename self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , __a ) , ) # Copy consistency with a really long name SCREAMING_SNAKE_CASE = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason" self.check_copy_consistency( F"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}" , F"{long_class_name}SchedulerOutput" , re.sub("Bert" , __a , __a ) , ) # Copy consistency with overwrite self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , __a , overwrite_result=re.sub("DDPM" , "Test" , __a ) , )
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"""simple docstring""" import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() _a = logging.get_logger(__name__) _a = { """post_extract_proj""": """feature_projection.projection""", """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""", """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""", """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""", """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""", """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""", """self_attn_layer_norm""": """encoder.layers.*.layer_norm""", """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""", """fc2""": """encoder.layers.*.feed_forward.output_dense""", """final_layer_norm""": """encoder.layers.*.final_layer_norm""", """encoder.layer_norm""": """encoder.layer_norm""", """w2v_model.layer_norm""": """feature_projection.layer_norm""", """quantizer.weight_proj""": """quantizer.weight_proj""", """quantizer.vars""": """quantizer.codevectors""", """project_q""": """project_q""", """final_proj""": """project_hid""", """w2v_encoder.proj""": """lm_head""", """mask_emb""": """masked_spec_embed""", } _a = [ """lm_head""", """quantizer.weight_proj""", """quantizer.codevectors""", """project_q""", """project_hid""", ] def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple: """simple docstring""" for attribute in key.split('''.''' ): _UpperCamelCase = getattr(__snake_case, __snake_case ) if weight_type is not None: _UpperCamelCase = getattr(__snake_case, __snake_case ).shape else: _UpperCamelCase = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": _UpperCamelCase = value elif weight_type == "weight_g": _UpperCamelCase = value elif weight_type == "weight_v": _UpperCamelCase = value elif weight_type == "bias": _UpperCamelCase = value else: _UpperCamelCase = value logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' ) def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]: """simple docstring""" _UpperCamelCase = [] _UpperCamelCase = fairseq_model.state_dict() _UpperCamelCase = hf_model.feature_extractor _UpperCamelCase = hf_model.adapter for name, value in fairseq_dict.items(): _UpperCamelCase = False if "conv_layers" in name: load_conv_layer( __snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', ) _UpperCamelCase = True elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ): load_adapter(__snake_case, __snake_case, __snake_case, __snake_case ) _UpperCamelCase = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: _UpperCamelCase = True if "*" in mapped_key: _UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2] _UpperCamelCase = mapped_key.replace('''*''', __snake_case ) if "weight_g" in name: _UpperCamelCase = '''weight_g''' elif "weight_v" in name: _UpperCamelCase = '''weight_v''' elif "bias" in name: _UpperCamelCase = '''bias''' elif "weight" in name: _UpperCamelCase = '''weight''' else: _UpperCamelCase = None set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) continue if not is_used: unused_weights.append(__snake_case ) logger.warning(F'''Unused weights: {unused_weights}''' ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]: """simple docstring""" _UpperCamelCase = full_name.split('''conv_layers.''' )[-1] _UpperCamelCase = name.split('''.''' ) _UpperCamelCase = int(items[0] ) _UpperCamelCase = int(items[1] ) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) _UpperCamelCase = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict: """simple docstring""" _UpperCamelCase = full_name.split('''adaptor.''' )[-1] _UpperCamelCase = name.split('''.''' ) if items[1].isdigit(): _UpperCamelCase = int(items[1] ) else: _UpperCamelCase = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' _UpperCamelCase = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' ) elif isinstance(__snake_case, __snake_case ): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' _UpperCamelCase = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) else: unused_weights.append(__snake_case ) def lowerCamelCase__ ( __snake_case ) -> str: """simple docstring""" _UpperCamelCase , _UpperCamelCase = emb.weight.shape _UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case ) _UpperCamelCase = emb.weight.data return lin_layer @torch.no_grad() def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]: """simple docstring""" _UpperCamelCase = WavaVecaConfig.from_pretrained( __snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, ) _UpperCamelCase = MBartConfig.from_pretrained(__snake_case ) # load model _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={ '''config_yaml''': config_yaml_path, '''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path, '''load_pretrained_decoder_from''': None, }, ) _UpperCamelCase = model[0].eval() # load feature extractor _UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case ) # set weights for wav2vec2 encoder _UpperCamelCase = WavaVecaModel(__snake_case ) recursively_load_weights_wavaveca(model.encoder, __snake_case ) # load decoder weights _UpperCamelCase = MBartForCausalLM(__snake_case ) _UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case ) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' ) logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' ) _UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case ) _UpperCamelCase = False _UpperCamelCase = MBartaaTokenizer(__snake_case ) tokenizer.save_pretrained(__snake_case ) _UpperCamelCase = hf_wavavec.config.to_dict() _UpperCamelCase = tokenizer.pad_token_id _UpperCamelCase = tokenizer.bos_token_id _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = '''mbart50''' _UpperCamelCase = '''wav2vec2''' _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = 25_00_04 _UpperCamelCase = tokenizer.eos_token_id _UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case ) hf_wavavec.save_pretrained(__snake_case ) feature_extractor.save_pretrained(__snake_case ) if __name__ == "__main__": _a = argparse.ArgumentParser() parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""") parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""") parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""") parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""") parser.add_argument( """--encoder_config_path""", default="""facebook/wav2vec2-xls-r-1b""", type=str, help="""Path to hf encoder wav2vec2 checkpoint config""", ) parser.add_argument( """--decoder_config_path""", default="""facebook/mbart-large-50-one-to-many-mmt""", type=str, help="""Path to hf decoder checkpoint config""", ) parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""") parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""") parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""") parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""") parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""") _a = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )
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'''simple docstring''' import os import unittest from transformers import MobileBertTokenizer, MobileBertTokenizerFast from transformers.models.bert.tokenization_bert import ( VOCAB_FILES_NAMES, BasicTokenizer, WordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english @require_tokenizers class A ( _a ,unittest.TestCase ): lowercase_ = MobileBertTokenizer lowercase_ = MobileBertTokenizerFast lowercase_ = True lowercase_ = True lowercase_ = filter_non_english lowercase_ = 'google/mobilebert-uncased' def __lowerCAmelCase ( self : Optional[Any] ) -> Tuple: """simple docstring""" super().setUp() _a = [ '''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''', ] _a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer: vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) ) _a = [ (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped for tokenizer_def in self.tokenizers_list ] def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : str ) -> List[str]: """simple docstring""" _a = '''UNwant\u00E9d,running''' _a = '''unwanted, running''' return input_text, output_text def __lowerCAmelCase ( self : Dict ) -> Any: """simple docstring""" _a = self.tokenizer_class(self.vocab_file ) _a = tokenizer.tokenize('''UNwant\u00E9d,running''' ) self.assertListEqual(__a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] ) def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict: """simple docstring""" if not self.test_rust_tokenizer: return _a = self.get_tokenizer() _a = self.get_rust_tokenizer() _a = '''UNwant\u00E9d,running''' _a = tokenizer.tokenize(__a ) _a = rust_tokenizer.tokenize(__a ) self.assertListEqual(__a , __a ) _a = tokenizer.encode(__a , add_special_tokens=__a ) _a = rust_tokenizer.encode(__a , add_special_tokens=__a ) self.assertListEqual(__a , __a ) _a = self.get_rust_tokenizer() _a = tokenizer.encode(__a ) _a = rust_tokenizer.encode(__a ) self.assertListEqual(__a , __a ) # With lower casing _a = self.get_tokenizer(do_lower_case=__a ) _a = self.get_rust_tokenizer(do_lower_case=__a ) _a = '''UNwant\u00E9d,running''' _a = tokenizer.tokenize(__a ) _a = rust_tokenizer.tokenize(__a ) self.assertListEqual(__a , __a ) _a = tokenizer.encode(__a , add_special_tokens=__a ) _a = rust_tokenizer.encode(__a , add_special_tokens=__a ) self.assertListEqual(__a , __a ) _a = self.get_rust_tokenizer() _a = tokenizer.encode(__a ) _a = rust_tokenizer.encode(__a ) self.assertListEqual(__a , __a ) def __lowerCAmelCase ( self : Tuple ) -> Optional[Any]: """simple docstring""" _a = BasicTokenizer() self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] ) def __lowerCAmelCase ( self : List[str] ) -> Dict: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a ) self.assertListEqual( tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] ) def __lowerCAmelCase ( self : int ) -> Union[str, Any]: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a , strip_accents=__a ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''h\u00E9llo'''] ) def __lowerCAmelCase ( self : Optional[Any] ) -> List[Any]: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a , strip_accents=__a ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] ) def __lowerCAmelCase ( self : Any ) -> Optional[int]: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] ) self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] ) def __lowerCAmelCase ( self : List[Any] ) -> Dict: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a ) self.assertListEqual( tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] ) def __lowerCAmelCase ( self : str ) -> List[Any]: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a , strip_accents=__a ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] ) def __lowerCAmelCase ( self : Tuple ) -> Tuple: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a , strip_accents=__a ) self.assertListEqual( tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] ) def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]: """simple docstring""" _a = BasicTokenizer(do_lower_case=__a , never_split=['''[UNK]'''] ) self.assertListEqual( tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] ) def __lowerCAmelCase ( self : Tuple ) -> Optional[int]: """simple docstring""" _a = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing'''] _a = {} for i, token in enumerate(__a ): _a = i _a = WordpieceTokenizer(vocab=__a , unk_token='''[UNK]''' ) self.assertListEqual(tokenizer.tokenize('''''' ) , [] ) self.assertListEqual(tokenizer.tokenize('''unwanted running''' ) , ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] ) self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ) , ['''[UNK]''', '''runn''', '''##ing'''] ) def __lowerCAmelCase ( self : Any ) -> List[str]: """simple docstring""" self.assertTrue(_is_whitespace(''' ''' ) ) self.assertTrue(_is_whitespace('''\t''' ) ) self.assertTrue(_is_whitespace('''\r''' ) ) self.assertTrue(_is_whitespace('''\n''' ) ) self.assertTrue(_is_whitespace('''\u00A0''' ) ) self.assertFalse(_is_whitespace('''A''' ) ) self.assertFalse(_is_whitespace('''-''' ) ) def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]: """simple docstring""" self.assertTrue(_is_control('''\u0005''' ) ) self.assertFalse(_is_control('''A''' ) ) self.assertFalse(_is_control(''' ''' ) ) self.assertFalse(_is_control('''\t''' ) ) self.assertFalse(_is_control('''\r''' ) ) def __lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]: """simple docstring""" self.assertTrue(_is_punctuation('''-''' ) ) self.assertTrue(_is_punctuation('''$''' ) ) self.assertTrue(_is_punctuation('''`''' ) ) self.assertTrue(_is_punctuation('''.''' ) ) self.assertFalse(_is_punctuation('''A''' ) ) self.assertFalse(_is_punctuation(''' ''' ) ) def __lowerCAmelCase ( self : str ) -> List[Any]: """simple docstring""" _a = self.get_tokenizer() _a = self.get_rust_tokenizer() # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340 self.assertListEqual([tokenizer.tokenize(__a ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] ) self.assertListEqual( [rust_tokenizer.tokenize(__a ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] ) @slow def __lowerCAmelCase ( self : str ) -> Optional[Any]: """simple docstring""" _a = self.tokenizer_class.from_pretrained('''google/mobilebert-uncased''' ) _a = tokenizer.encode('''sequence builders''' , add_special_tokens=__a ) _a = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__a ) _a = tokenizer.build_inputs_with_special_tokens(__a ) _a = tokenizer.build_inputs_with_special_tokens(__a , __a ) assert encoded_sentence == [1_01] + text + [1_02] assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02] def __lowerCAmelCase ( self : int ) -> List[str]: """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ): _a = self.rust_tokenizer_class.from_pretrained(__a , **__a ) _a = F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.' _a = tokenizer_r.encode_plus( __a , return_attention_mask=__a , return_token_type_ids=__a , return_offsets_mapping=__a , add_special_tokens=__a , ) _a = tokenizer_r.do_lower_case if hasattr(__a , '''do_lower_case''' ) else False _a = ( [ ((0, 0), tokenizer_r.cls_token), ((0, 1), '''A'''), ((1, 2), ''','''), ((3, 5), '''na'''), ((5, 6), '''##ï'''), ((6, 8), '''##ve'''), ((9, 15), tokenizer_r.mask_token), ((16, 21), '''Allen'''), ((21, 23), '''##NL'''), ((23, 24), '''##P'''), ((25, 33), '''sentence'''), ((33, 34), '''.'''), ((0, 0), tokenizer_r.sep_token), ] if not do_lower_case else [ ((0, 0), tokenizer_r.cls_token), ((0, 1), '''a'''), ((1, 2), ''','''), ((3, 8), '''naive'''), ((9, 15), tokenizer_r.mask_token), ((16, 21), '''allen'''), ((21, 23), '''##nl'''), ((23, 24), '''##p'''), ((25, 33), '''sentence'''), ((33, 34), '''.'''), ((0, 0), tokenizer_r.sep_token), ] ) self.assertEqual( [e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) ) self.assertEqual([e[0] for e in expected_results] , tokens['''offset_mapping'''] ) def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]: """simple docstring""" _a = ['''的''', '''人''', '''有'''] _a = ''''''.join(__a ) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ): _a = True _a = self.tokenizer_class.from_pretrained(__a , **__a ) _a = self.rust_tokenizer_class.from_pretrained(__a , **__a ) _a = tokenizer_p.encode(__a , add_special_tokens=__a ) _a = tokenizer_r.encode(__a , add_special_tokens=__a ) _a = tokenizer_r.convert_ids_to_tokens(__a ) _a = tokenizer_p.convert_ids_to_tokens(__a ) # it is expected that each Chinese character is not preceded by "##" self.assertListEqual(__a , __a ) self.assertListEqual(__a , __a ) _a = False _a = self.rust_tokenizer_class.from_pretrained(__a , **__a ) _a = self.tokenizer_class.from_pretrained(__a , **__a ) _a = tokenizer_r.encode(__a , add_special_tokens=__a ) _a = tokenizer_p.encode(__a , add_special_tokens=__a ) _a = tokenizer_r.convert_ids_to_tokens(__a ) _a = tokenizer_p.convert_ids_to_tokens(__a ) # it is expected that only the first Chinese character is not preceded by "##". _a = [ F'##{token}' if idx != 0 else token for idx, token in enumerate(__a ) ] self.assertListEqual(__a , __a ) self.assertListEqual(__a , __a )
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"""simple docstring""" import fire from utils import calculate_rouge, save_json def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]: """simple docstring""" _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()] _UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )] _UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case ) if save_path is not None: save_json(__snake_case, __snake_case, indent=__snake_case ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)
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0
'''simple docstring''' import collections import json import os import re from typing import TYPE_CHECKING, List, Optional, Tuple import numpy as np from ...tokenization_utils_fast import PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation lowercase : Optional[Any] = logging.get_logger(__name__) lowercase : int = {'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'} lowercase : Dict = { 'vocab_file': { 'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt', }, 'emoji_file': { 'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json', }, } lowercase : Tuple = { 'abeja/gpt-neox-japanese-2.7b': 2_0_4_8, } def __a ( A__ , A__ ) -> Union[str, Any]: with open(__snake_case , "r" , encoding="utf-8" ) as f: lowerCAmelCase = json.loads(f.read() ) lowerCAmelCase = collections.OrderedDict() lowerCAmelCase = collections.OrderedDict() lowerCAmelCase = collections.OrderedDict() with open(__snake_case , "r" , encoding="utf-8" ) as f: lowerCAmelCase = f.readlines() lowerCAmelCase = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token] for idx, b in enumerate(__snake_case ): lowerCAmelCase = b lowerCAmelCase = idx for wd in b: lowerCAmelCase = idx return vocab, raw_vocab, ids_to_tokens, emoji class _lowerCAmelCase ( UpperCamelCase_ ): """simple docstring""" lowerCAmelCase = VOCAB_FILES_NAMES lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCAmelCase = ['input_ids', 'attention_mask'] def __init__( self : List[str] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any]="<|endoftext|>" , SCREAMING_SNAKE_CASE : str="<|endoftext|>" , SCREAMING_SNAKE_CASE : Any="<|startoftext|>" , SCREAMING_SNAKE_CASE : Dict="<|endoftext|>" , SCREAMING_SNAKE_CASE : Any=False , **SCREAMING_SNAKE_CASE : Dict , ) -> Optional[Any]: """simple docstring""" super().__init__( unk_token=__a , pad_token=__a , bos_token=__a , eos_token=__a , do_clean_text=__a , **__a , ) if not os.path.isfile(__a ): raise ValueError( f"Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained" " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" ) if not os.path.isfile(__a ): raise ValueError( f"Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google" " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" ) lowerCAmelCase = do_clean_text lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = load_vocab_and_emoji(__a , __a ) lowerCAmelCase = SubWordJapaneseTokenizer( vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji ) @property def __A ( self : List[str] ) -> Optional[Any]: """simple docstring""" return len(self.raw_vocab ) def __A ( self : Dict ) -> Optional[int]: """simple docstring""" return dict(self.raw_vocab , **self.added_tokens_encoder ) def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]: """simple docstring""" return self.subword_tokenizer.tokenize(__a , clean=self.do_clean_text ) def __A ( self : Tuple , SCREAMING_SNAKE_CASE : Dict ) -> Optional[Any]: """simple docstring""" return self.vocab.get(__a , self.vocab.get(self.unk_token ) ) def __A ( self : str , SCREAMING_SNAKE_CASE : List[str] ) -> str: """simple docstring""" return self.subword_tokenizer.convert_id_to_token(__a ) def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] ) -> Union[str, Any]: """simple docstring""" lowerCAmelCase = "".join(__a ).strip() return out_string def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : Tuple ) -> List[int]: """simple docstring""" lowerCAmelCase = [] for is_user, text in conversation.iter_texts(): input_ids.extend(self.encode(__a , add_special_tokens=__a ) + [self.eos_token_id] ) if len(__a ) > self.model_max_length: lowerCAmelCase = input_ids[-self.model_max_length :] return input_ids def __A ( self : List[Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Any = None ) -> Tuple[str]: """simple docstring""" lowerCAmelCase = 0 if os.path.isdir(__a ): lowerCAmelCase = os.path.join( __a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase = os.path.join( __a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] ) else: lowerCAmelCase = ( (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"] ) lowerCAmelCase = ( (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"] ) with open(__a , "w" , encoding="utf-8" ) as writer: for token_index, token in self.ids_to_tokens.items(): if index != token_index: logger.warning( f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." " Please check that the vocabulary is not corrupted!" ) lowerCAmelCase = token_index writer.write(",".join(__a ) + "\n" ) index += 1 with open(__a , "w" , encoding="utf-8" ) as writer: json.dump(self.emoji , __a ) return vocab_file, emoji_file class _lowerCAmelCase ( UpperCamelCase_ ): """simple docstring""" def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int: """simple docstring""" lowerCAmelCase = vocab # same as swe lowerCAmelCase = ids_to_tokens # same as bpe lowerCAmelCase = emoji lowerCAmelCase = np.max([len(__a ) for w in self.vocab.keys()] ) lowerCAmelCase = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" ) lowerCAmelCase = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" ) lowerCAmelCase = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" ) lowerCAmelCase = re.compile( R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" ) lowerCAmelCase = re.compile( R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" ) lowerCAmelCase = re.compile( R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" ) lowerCAmelCase = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿" lowerCAmelCase = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟" lowerCAmelCase = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} ) def __len__( self : Any ) -> Optional[Any]: """simple docstring""" return len(self.ids_to_tokens ) def __A ( self : Any , SCREAMING_SNAKE_CASE : List[str] ) -> Dict: """simple docstring""" lowerCAmelCase = self.content_repattera.sub("<URL>" , __a ) lowerCAmelCase = self.content_repattera.sub("<EMAIL>" , __a ) lowerCAmelCase = self.content_repattera.sub("<TEL>" , __a ) lowerCAmelCase = self.content_repattera.sub("<DATE>" , __a ) lowerCAmelCase = self.content_repattera.sub("<DATE>" , __a ) lowerCAmelCase = self.content_repattera.sub("<PRICE>" , __a ) lowerCAmelCase = content.translate(self.content_transa ) while "<BLOCK><BLOCK>" in content: lowerCAmelCase = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" ) return content def __A ( self : Tuple , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any]=False ) -> Any: """simple docstring""" lowerCAmelCase = text.replace(" " , "<SP>" ) lowerCAmelCase = text.replace(" " , "<SP>" ) lowerCAmelCase = text.replace("\r\n" , "<BR>" ) lowerCAmelCase = text.replace("\n" , "<BR>" ) lowerCAmelCase = text.replace("\r" , "<BR>" ) lowerCAmelCase = text.replace("\t" , "<TAB>" ) lowerCAmelCase = text.replace("—" , "ー" ) lowerCAmelCase = text.replace("−" , "ー" ) for k, v in self.emoji["emoji"].items(): if k in text: lowerCAmelCase = text.replace(__a , __a ) if clean: lowerCAmelCase = self.clean_text(__a ) def check_simbol(SCREAMING_SNAKE_CASE : Union[str, Any] ): lowerCAmelCase = x.encode() if len(__a ) == 1 and len(__a ) == 2: lowerCAmelCase = (int(e[0] ) << 8) + int(e[1] ) if ( (c >= 0xC2A1 and c <= 0xC2BF) or (c >= 0xC780 and c <= 0xC783) or (c >= 0xCAB9 and c <= 0xCBBF) or (c >= 0xCC80 and c <= 0xCDA2) ): return True return False def checkuae(SCREAMING_SNAKE_CASE : List[Any] ): lowerCAmelCase = x.encode() if len(__a ) == 1 and len(__a ) == 3: lowerCAmelCase = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] ) if c >= 0xE2_8080 and c <= 0xE2_B07F: return True return False lowerCAmelCase = 0 lowerCAmelCase = [] while pos < len(__a ): lowerCAmelCase = min(len(__a ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3 lowerCAmelCase = [] # (token_id, token, pos) for e in range(__a , __a , -1 ): lowerCAmelCase = text[pos:e] if wd in self.vocab: if wd[0] == "<" and len(__a ) > 2: lowerCAmelCase = [(self.vocab[wd], wd, e)] break else: candidates.append((self.vocab[wd], wd, e) ) if len(__a ) > 0: # the smallest token_id is adopted lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = sorted(__a , key=lambda SCREAMING_SNAKE_CASE : x[0] )[0] result.append(__a ) lowerCAmelCase = e else: lowerCAmelCase = pos + 1 lowerCAmelCase = text[pos:end] if check_simbol(__a ): result.append("<KIGOU>" ) elif checkuae(__a ): result.append("<U2000U2BFF>" ) else: for i in wd.encode("utf-8" ): result.append("<|byte%d|>" % i ) lowerCAmelCase = end return result def __A ( self : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[Any]="\n" ) -> int: """simple docstring""" lowerCAmelCase = [] lowerCAmelCase = [] lowerCAmelCase = self.ids_to_tokens[index][0] if word[:6] == "<|byte" and word[-2:] == "|>": byte_tokens.append(int(word[6:-2] ) ) else: if len(__a ) > 0: words.append(bytearray(__a ).decode("utf-8" , errors="replace" ) ) lowerCAmelCase = [] if word[:7] == "<|emoji" and word[-2:] == "|>": words.append(self.emoji["emoji_inv"][word] ) elif word == "<SP>": words.append(" " ) elif word == "<BR>": words.append(__a ) elif word == "<TAB>": words.append("\t" ) elif word == "<BLOCK>": words.append("▀" ) elif word == "<KIGOU>": words.append("ǀ" ) elif word == "<U2000U2BFF>": words.append("‖" ) else: words.append(__a ) if len(__a ) > 0: words.append(bytearray(__a ).decode("utf-8" , errors="replace" ) ) lowerCAmelCase = "".join(__a ) return text
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"""simple docstring""" import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class _UpperCAmelCase( lowerCamelCase ): lowercase__ = ['image_processor', 'tokenizer'] lowercase__ = 'ViTImageProcessor' lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast') def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = None if "feature_extractor" in kwargs: warnings.warn( '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`''' ''' instead.''' , __a , ) _UpperCamelCase = kwargs.pop('''feature_extractor''') _UpperCamelCase = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError('''You need to specify an `image_processor`.''') if tokenizer is None: raise ValueError('''You need to specify a `tokenizer`.''') super().__init__(__a , __a) def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple: '''simple docstring''' if text is None and visual_prompt is None and images is None: raise ValueError('''You have to specify either text, visual prompt or images.''') if text is not None and visual_prompt is not None: raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''') if text is not None: _UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a) if visual_prompt is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if images is not None: _UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a) if visual_prompt is not None and images is not None: _UpperCamelCase = { '''pixel_values''': image_features.pixel_values, '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding elif text is not None and images is not None: _UpperCamelCase = image_features.pixel_values return encoding elif text is not None: return encoding elif visual_prompt is not None: _UpperCamelCase = { '''conditional_pixel_values''': prompt_features.pixel_values, } return encoding else: return BatchEncoding(data=dict(**__a) , tensor_type=__a) def UpperCAmelCase ( self , *__a , **__a) -> Any: '''simple docstring''' return self.tokenizer.batch_decode(*__a , **__a) def UpperCAmelCase ( self , *__a , **__a) -> List[str]: '''simple docstring''' return self.tokenizer.decode(*__a , **__a) @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , ) return self.image_processor_class @property def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' warnings.warn( '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , ) return self.image_processor
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'''simple docstring''' import argparse import datetime def __UpperCamelCase( _A : str ): '''simple docstring''' UpperCAmelCase__ : Dict = { '''0''': '''Sunday''', '''1''': '''Monday''', '''2''': '''Tuesday''', '''3''': '''Wednesday''', '''4''': '''Thursday''', '''5''': '''Friday''', '''6''': '''Saturday''', } UpperCAmelCase__ : Tuple = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0} # Validate if not 0 < len(__snake_case ) < 11: raise ValueError('''Must be 10 characters long''' ) # Get month UpperCAmelCase__ : Union[str, Any] = int(date_input[0] + date_input[1] ) # Validate if not 0 < m < 13: raise ValueError('''Month must be between 1 - 12''' ) UpperCAmelCase__ : Any = date_input[2] # Validate if sep_a not in ["-", "/"]: raise ValueError('''Date separator must be \'-\' or \'/\'''' ) # Get day UpperCAmelCase__ : List[str] = int(date_input[3] + date_input[4] ) # Validate if not 0 < d < 32: raise ValueError('''Date must be between 1 - 31''' ) # Get second separator UpperCAmelCase__ : Any = date_input[5] # Validate if sep_a not in ["-", "/"]: raise ValueError('''Date separator must be \'-\' or \'/\'''' ) # Get year UpperCAmelCase__ : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] ) # Arbitrary year range if not 45 < y < 85_00: raise ValueError( '''Year out of range. There has to be some sort of limit...right?''' ) # Get datetime obj for validation UpperCAmelCase__ : int = datetime.date(int(__snake_case ) , int(__snake_case ) , int(__snake_case ) ) # Start math if m <= 2: UpperCAmelCase__ : Union[str, Any] = y - 1 UpperCAmelCase__ : Dict = m + 12 # maths var UpperCAmelCase__ : Tuple = int(str(__snake_case )[:2] ) UpperCAmelCase__ : Optional[int] = int(str(__snake_case )[2:] ) UpperCAmelCase__ : int = int(2.6 * m - 5.3_9 ) UpperCAmelCase__ : Optional[Any] = int(c / 4 ) UpperCAmelCase__ : Tuple = int(k / 4 ) UpperCAmelCase__ : List[str] = int(d + k ) UpperCAmelCase__ : int = int(t + u + v + x ) UpperCAmelCase__ : Optional[int] = int(z - (2 * c) ) UpperCAmelCase__ : Dict = round(w % 7 ) # End math # Validate math if f != convert_datetime_days[dt_ck.weekday()]: raise AssertionError('''The date was evaluated incorrectly. Contact developer.''' ) # Response UpperCAmelCase__ : List[str] = F'''Your date {date_input}, is a {days[str(__snake_case )]}!''' return response if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase__ : str = argparse.ArgumentParser( description=( 'Find out what day of the week nearly any date is or was. Enter ' 'date as a string in the mm-dd-yyyy or mm/dd/yyyy format' ) ) parser.add_argument( 'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)' ) UpperCamelCase__ : Dict = parser.parse_args() zeller(args.date_input)
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"""simple docstring""" import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ConvNextConfig, UperNetConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import UperNetForSemanticSegmentation from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = image_size _UpperCamelCase = num_channels _UpperCamelCase = num_stages _UpperCamelCase = hidden_sizes _UpperCamelCase = depths _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = out_features _UpperCamelCase = num_labels _UpperCamelCase = scope _UpperCamelCase = num_stages def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = self.get_config() return config, pixel_values, labels def UpperCAmelCase ( self) -> Any: '''simple docstring''' return ConvNextConfig( num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' return UperNetConfig( backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , ) def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = UperNetForSemanticSegmentation(config=__a) model.to(__a) model.eval() _UpperCamelCase = model(__a) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size)) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else () lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = UperNetModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' return def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = model_class(__a) _UpperCamelCase = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic _UpperCamelCase = [*signature.parameters.keys()] _UpperCamelCase = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*__a) @unittest.skip(reason='''UperNet does not use inputs_embeds''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not support input and output embeddings''') def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' pass @unittest.skip(reason='''UperNet does not have a base model''') def UpperCAmelCase ( self) -> int: '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''') def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' pass @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''') def UpperCAmelCase ( self) -> Any: '''simple docstring''' pass def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' def check_hidden_states_output(__a , __a , __a): _UpperCamelCase = model_class(__a) model.to(__a) model.eval() with torch.no_grad(): _UpperCamelCase = model(**self._prepare_for_class(__a , __a)) _UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states _UpperCamelCase = self.model_tester.num_stages self.assertEqual(len(__a) , expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _UpperCamelCase = True check_hidden_states_output(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() _UpperCamelCase = _config_zero_init(__a) _UpperCamelCase = _config_zero_init(configs_no_init.backbone_config) for model_class in self.all_model_classes: _UpperCamelCase = model_class(config=__a) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @unittest.skip(reason='''UperNet does not have tied weights''') def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' pass @slow def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a) self.assertIsNotNone(__a) def lowerCamelCase__ ( ) -> int: """simple docstring""" _UpperCamelCase = hf_hub_download( repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' ) _UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' ) return image @require_torch @require_vision @slow class _UpperCAmelCase( unittest.TestCase ): def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4)) def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''') _UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a) _UpperCamelCase = prepare_img() _UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a) with torch.no_grad(): _UpperCamelCase = model(**__a) _UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12)) self.assertEqual(outputs.logits.shape , __a) _UpperCamelCase = torch.tensor( [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a) self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
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'''simple docstring''' import requests lowercase ='YOUR API KEY' def lowerCamelCase__ ( __lowerCamelCase : Any , __lowerCamelCase : str = giphy_api_key ): '''simple docstring''' _UpperCAmelCase : Any ='+'.join(query.split() ) _UpperCAmelCase : Any =f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}" _UpperCAmelCase : Optional[Any] =requests.get(__snake_case ).json()['data'] return [gif["url"] for gif in gifs] if __name__ == "__main__": print('\n'.join(get_gifs('space ship')))
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class _UpperCAmelCase( lowerCamelCase ): lowercase__ = (DDPMScheduler,) def UpperCAmelCase ( self , **__a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = { '''num_train_timesteps''': 10_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''variance_type''': '''fixed_small''', '''clip_sample''': True, } config.update(**__a) return config def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for timesteps in [1, 5, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__a) def UpperCAmelCase ( self) -> int: '''simple docstring''' for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]): self.check_over_configs(beta_start=__a , beta_end=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=__a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.check_over_configs(thresholding=__a) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=__a , prediction_type=__a , sample_max_value=__a , ) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=__a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' for t in [0, 5_00, 9_99]: self.check_over_forward(time_step=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 258.9606) < 1e-2 assert abs(result_mean.item() - 0.3372) < 1e-3 def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''') _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = len(__a) _UpperCamelCase = self.dummy_model() _UpperCamelCase = self.dummy_sample_deter _UpperCamelCase = torch.manual_seed(0) for t in reversed(range(__a)): # 1. predict noise residual _UpperCamelCase = model(__a , __a) # 2. predict previous mean of sample x_t-1 _UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance _UpperCamelCase = pred_prev_sample _UpperCamelCase = torch.sum(torch.abs(__a)) _UpperCamelCase = torch.mean(torch.abs(__a)) assert abs(result_sum.item() - 202.0296) < 1e-2 assert abs(result_mean.item() - 0.2631) < 1e-3 def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=__a) _UpperCamelCase = scheduler.timesteps for i, timestep in enumerate(__a): if i == len(__a) - 1: _UpperCamelCase = -1 else: _UpperCamelCase = timesteps[i + 1] _UpperCamelCase = scheduler.previous_timestep(__a) _UpperCamelCase = prev_t.item() self.assertEqual(__a , __a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 51, 0] with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''): scheduler.set_timesteps(timesteps=__a) def UpperCAmelCase ( self) -> List[str]: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [1_00, 87, 50, 1, 0] _UpperCamelCase = len(__a) with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''): scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a) def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.scheduler_classes[0] _UpperCamelCase = self.get_scheduler_config() _UpperCamelCase = scheduler_class(**__a) _UpperCamelCase = [scheduler.config.num_train_timesteps] with self.assertRaises( __a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ): scheduler.set_timesteps(timesteps=__a)
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import inspect from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel, VQModel from ...schedulers import DDIMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class lowerCamelCase (__lowerCamelCase ): """simple docstring""" def __init__( self : List[Any], _UpperCAmelCase : Tuple, _UpperCAmelCase : List[str], _UpperCAmelCase : List[Any] ) -> Optional[Any]: """simple docstring""" super().__init__() self.register_modules(vqvae=__a, unet=__a, scheduler=__a ) @torch.no_grad() def __call__( self : List[Any], _UpperCAmelCase : List[Any] = 1, _UpperCAmelCase : int = None, _UpperCAmelCase : Any = 0.0, _UpperCAmelCase : List[str] = 5_0, _UpperCAmelCase : List[Any] = "pil", _UpperCAmelCase : Any = True, **_UpperCAmelCase : List[Any], ) -> Union[Tuple, ImagePipelineOutput]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = randn_tensor( (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), generator=__a, ) SCREAMING_SNAKE_CASE__ : Optional[int] = latents.to(self.device ) # scale the initial noise by the standard deviation required by the scheduler SCREAMING_SNAKE_CASE__ : List[str] = latents * self.scheduler.init_noise_sigma self.scheduler.set_timesteps(__a ) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature SCREAMING_SNAKE_CASE__ : Any = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() ) SCREAMING_SNAKE_CASE__ : Dict = {} if accepts_eta: SCREAMING_SNAKE_CASE__ : int = eta for t in self.progress_bar(self.scheduler.timesteps ): SCREAMING_SNAKE_CASE__ : int = self.scheduler.scale_model_input(__a, __a ) # predict the noise residual SCREAMING_SNAKE_CASE__ : Optional[Any] = self.unet(__a, __a ).sample # compute the previous noisy sample x_t -> x_t-1 SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler.step(__a, __a, __a, **__a ).prev_sample # decode the image latents with the VAE SCREAMING_SNAKE_CASE__ : int = self.vqvae.decode(__a ).sample SCREAMING_SNAKE_CASE__ : Optional[Any] = (image / 2 + 0.5).clamp(0, 1 ) SCREAMING_SNAKE_CASE__ : str = image.cpu().permute(0, 2, 3, 1 ).numpy() if output_type == "pil": SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.numpy_to_pil(__a ) if not return_dict: return (image,) return ImagePipelineOutput(images=__a )
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"""simple docstring""" from __future__ import annotations from functools import lru_cache from math import ceil _a = 100 _a = set(range(3, NUM_PRIMES, 2)) primes.add(2) _a = 42 for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=1_00 ) def lowerCamelCase__ ( __snake_case ) -> set[int]: """simple docstring""" if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} _UpperCamelCase = set() _UpperCamelCase = 42 _UpperCamelCase = 42 for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None: """simple docstring""" for number_to_partition in range(1, __snake_case ): if len(partition(__snake_case ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(F"""{solution() = }""")
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from __future__ import annotations import unittest from transformers import RoFormerConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForMultipleChoice, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerModel, ) from transformers.models.roformer.modeling_tf_roformer import ( TFRoFormerSelfAttention, TFRoFormerSinusoidalPositionalEmbedding, ) class __magic_name__ : def __init__( self : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any=13 , UpperCamelCase__ : Union[str, Any]=7 , UpperCamelCase__ : int=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Optional[Any]=99 , UpperCamelCase__ : List[str]=32 , UpperCamelCase__ : int=2 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : List[str]=37 , UpperCamelCase__ : Optional[int]="gelu" , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : int=5_12 , UpperCamelCase__ : Optional[int]=16 , UpperCamelCase__ : List[Any]=2 , UpperCamelCase__ : int=0.02 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : Optional[Any]=None , ) -> List[Any]: '''simple docstring''' UpperCAmelCase = parent UpperCAmelCase = 13 UpperCAmelCase = 7 UpperCAmelCase = True UpperCAmelCase = True UpperCAmelCase = True UpperCAmelCase = True UpperCAmelCase = 99 UpperCAmelCase = 32 UpperCAmelCase = 2 UpperCAmelCase = 4 UpperCAmelCase = 37 UpperCAmelCase = "gelu" UpperCAmelCase = 0.1 UpperCAmelCase = 0.1 UpperCAmelCase = 5_12 UpperCAmelCase = 16 UpperCAmelCase = 2 UpperCAmelCase = 0.02 UpperCAmelCase = 3 UpperCAmelCase = 4 UpperCAmelCase = None def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase = None if self.use_input_mask: UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase = None if self.use_token_type_ids: UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase = None UpperCAmelCase = None UpperCAmelCase = None if self.use_labels: UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase = RoFormerConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=__a , ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple ) -> List[str]: '''simple docstring''' UpperCAmelCase = TFRoFormerModel(config=__a ) UpperCAmelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} UpperCAmelCase = [input_ids, input_mask] UpperCAmelCase = model(__a ) UpperCAmelCase = model(__a ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def SCREAMING_SNAKE_CASE_ ( self : str , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] ) -> List[str]: '''simple docstring''' UpperCAmelCase = True UpperCAmelCase = TFRoFormerForCausalLM(config=__a ) UpperCAmelCase = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase = model(__a )["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str] ) -> int: '''simple docstring''' UpperCAmelCase = TFRoFormerForMaskedLM(config=__a ) UpperCAmelCase = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase = model(__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] ) -> Tuple: '''simple docstring''' UpperCAmelCase = self.num_labels UpperCAmelCase = TFRoFormerForSequenceClassification(config=__a ) UpperCAmelCase = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase = model(__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def SCREAMING_SNAKE_CASE_ ( self : str , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] ) -> Optional[int]: '''simple docstring''' UpperCAmelCase = self.num_choices UpperCAmelCase = TFRoFormerForMultipleChoice(config=__a ) UpperCAmelCase = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } UpperCAmelCase = model(__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] ) -> List[Any]: '''simple docstring''' UpperCAmelCase = self.num_labels UpperCAmelCase = TFRoFormerForTokenClassification(config=__a ) UpperCAmelCase = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase = model(__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def SCREAMING_SNAKE_CASE_ ( self : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple ) -> str: '''simple docstring''' UpperCAmelCase = TFRoFormerForQuestionAnswering(config=__a ) UpperCAmelCase = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase = model(__a ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Dict: '''simple docstring''' UpperCAmelCase = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) = config_and_inputs UpperCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class __magic_name__ ( A__, A__, unittest.TestCase ): lowercase : List[Any] =( ( TFRoFormerModel, TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerForMultipleChoice, ) if is_tf_available() else () ) lowercase : str =( { '''feature-extraction''': TFRoFormerModel, '''fill-mask''': TFRoFormerForMaskedLM, '''question-answering''': TFRoFormerForQuestionAnswering, '''text-classification''': TFRoFormerForSequenceClassification, '''text-generation''': TFRoFormerForCausalLM, '''token-classification''': TFRoFormerForTokenClassification, '''zero-shot''': TFRoFormerForSequenceClassification, } if is_tf_available() else {} ) lowercase : Optional[Any] =False lowercase : str =False def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] ) -> List[str]: '''simple docstring''' if pipeline_test_casse_name == "TextGenerationPipelineTests": return True return False def SCREAMING_SNAKE_CASE_ ( self : int ) -> Union[str, Any]: '''simple docstring''' UpperCAmelCase = TFRoFormerModelTester(self ) UpperCAmelCase = ConfigTester(self , config_class=__a , hidden_size=37 ) def SCREAMING_SNAKE_CASE_ ( self : Any ) -> str: '''simple docstring''' self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Dict: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Any: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> List[Any]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head(*__a ) def SCREAMING_SNAKE_CASE_ ( self : str ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__a ) def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[str]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__a ) def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Tuple: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__a ) def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Optional[Any]: '''simple docstring''' UpperCAmelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__a ) @slow def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]: '''simple docstring''' UpperCAmelCase = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base" ) self.assertIsNotNone(__a ) @require_tf class __magic_name__ ( unittest.TestCase ): @slow def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> str: '''simple docstring''' UpperCAmelCase = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" ) UpperCAmelCase = tf.constant([[0, 1, 2, 3, 4, 5]] ) UpperCAmelCase = model(__a )[0] # TODO Replace vocab size UpperCAmelCase = 5_00_00 UpperCAmelCase = [1, 6, vocab_size] self.assertEqual(output.shape , __a ) print(output[:, :3, :3] ) # TODO Replace values below with what was printed above. UpperCAmelCase = tf.constant( [ [ [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46], [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07], [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64], ] ] ) tf.debugging.assert_near(output[:, :3, :3] , __a , atol=1e-4 ) @require_tf class __magic_name__ ( unittest.TestCase ): lowercase : Any =1E-4 def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> List[str]: '''simple docstring''' UpperCAmelCase = tf.constant([[4, 10]] ) UpperCAmelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 ) UpperCAmelCase = emba(input_ids.shape ) UpperCAmelCase = tf.constant( [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]] ) tf.debugging.assert_near(__a , __a , atol=self.tolerance ) def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Dict: '''simple docstring''' UpperCAmelCase = tf.constant( [ [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00], [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17], [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70], ] ) UpperCAmelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 ) emba([2, 16, 5_12] ) UpperCAmelCase = emba.weight[:3, :5] tf.debugging.assert_near(__a , __a , atol=self.tolerance ) @require_tf class __magic_name__ ( unittest.TestCase ): lowercase : int =1E-4 def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]: '''simple docstring''' UpperCAmelCase = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00 UpperCAmelCase = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00 UpperCAmelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 ) UpperCAmelCase = embed_positions([2, 16, 7_68] )[None, None, :, :] UpperCAmelCase , UpperCAmelCase = TFRoFormerSelfAttention.apply_rotary_position_embeddings( __a , __a , __a ) UpperCAmelCase = tf.constant( [ [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00], [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43], [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85], [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71], [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80], [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53], ] ) UpperCAmelCase = tf.constant( [ [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00], [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43], [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85], [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71], [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80], [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53], ] ) tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __a , atol=self.tolerance ) tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __a , atol=self.tolerance )
323
"""simple docstring""" from collections.abc import Callable import numpy as np def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array: """simple docstring""" _UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) ) _UpperCamelCase = np.zeros((n + 1,) ) _UpperCamelCase = ya _UpperCamelCase = xa for k in range(__snake_case ): _UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] ) _UpperCamelCase = y[k] + ( (step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
19
0
'''simple docstring''' import numpy as np class UpperCAmelCase : '''simple docstring''' def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Dict: self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a ) def _lowerCAmelCase( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Dict: if red is not None: lowercase__ : Any = red if green is not None: lowercase__ : List[Any] = green if blue is not None: lowercase__ : Union[str, Any] = blue if red_edge is not None: lowercase__ : Optional[int] = red_edge if nir is not None: lowercase__ : List[str] = nir return True def _lowerCAmelCase( self , __lowerCAmelCase="" , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> List[str]: self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a ) lowercase__ : Union[str, Any] = { '''ARVI2''': self.arvaa, '''CCCI''': self.ccci, '''CVI''': self.cvi, '''GLI''': self.gli, '''NDVI''': self.ndvi, '''BNDVI''': self.bndvi, '''redEdgeNDVI''': self.red_edge_ndvi, '''GNDVI''': self.gndvi, '''GBNDVI''': self.gbndvi, '''GRNDVI''': self.grndvi, '''RBNDVI''': self.rbndvi, '''PNDVI''': self.pndvi, '''ATSAVI''': self.atsavi, '''BWDRVI''': self.bwdrvi, '''CIgreen''': self.ci_green, '''CIrededge''': self.ci_rededge, '''CI''': self.ci, '''CTVI''': self.ctvi, '''GDVI''': self.gdvi, '''EVI''': self.evi, '''GEMI''': self.gemi, '''GOSAVI''': self.gosavi, '''GSAVI''': self.gsavi, '''Hue''': self.hue, '''IVI''': self.ivi, '''IPVI''': self.ipvi, '''I''': self.i, '''RVI''': self.rvi, '''MRVI''': self.mrvi, '''MSAVI''': self.m_savi, '''NormG''': self.norm_g, '''NormNIR''': self.norm_nir, '''NormR''': self.norm_r, '''NGRDI''': self.ngrdi, '''RI''': self.ri, '''S''': self.s, '''IF''': self._if, '''DVI''': self.dvi, '''TVI''': self.tvi, '''NDRE''': self.ndre, } try: return funcs[index]() except KeyError: print('''Index not in the list!''' ) return False def _lowerCAmelCase( self ) -> List[Any]: return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red))) def _lowerCAmelCase( self ) -> Any: return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / ( (self.nir - self.red) / (self.nir + self.red) ) def _lowerCAmelCase( self ) -> Optional[int]: return self.nir * (self.red / (self.green**2)) def _lowerCAmelCase( self ) -> str: return (2 * self.green - self.red - self.blue) / ( 2 * self.green + self.red + self.blue ) def _lowerCAmelCase( self ) -> List[str]: return (self.nir - self.red) / (self.nir + self.red) def _lowerCAmelCase( self ) -> str: return (self.nir - self.blue) / (self.nir + self.blue) def _lowerCAmelCase( self ) -> List[Any]: return (self.redEdge - self.red) / (self.redEdge + self.red) def _lowerCAmelCase( self ) -> Optional[int]: return (self.nir - self.green) / (self.nir + self.green) def _lowerCAmelCase( self ) -> Optional[Any]: return (self.nir - (self.green + self.blue)) / ( self.nir + (self.green + self.blue) ) def _lowerCAmelCase( self ) -> Tuple: return (self.nir - (self.green + self.red)) / ( self.nir + (self.green + self.red) ) def _lowerCAmelCase( self ) -> List[Any]: return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red)) def _lowerCAmelCase( self ) -> List[str]: return (self.nir - (self.green + self.red + self.blue)) / ( self.nir + (self.green + self.red + self.blue) ) def _lowerCAmelCase( self , __lowerCAmelCase=0.0_8 , __lowerCAmelCase=1.2_2 , __lowerCAmelCase=0.0_3 ) -> Optional[Any]: return a * ( (self.nir - a * self.red - b) / (a * self.nir + self.red - a * b + x * (1 + a**2)) ) def _lowerCAmelCase( self ) -> Dict: return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue) def _lowerCAmelCase( self ) -> List[str]: return (self.nir / self.green) - 1 def _lowerCAmelCase( self ) -> List[Any]: return (self.nir / self.redEdge) - 1 def _lowerCAmelCase( self ) -> Union[str, Any]: return (self.red - self.blue) / self.red def _lowerCAmelCase( self ) -> Optional[int]: lowercase__ : int = self.ndvi() return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2)) def _lowerCAmelCase( self ) -> Optional[int]: return self.nir - self.green def _lowerCAmelCase( self ) -> List[str]: return 2.5 * ( (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1) ) def _lowerCAmelCase( self ) -> Tuple: lowercase__ : Optional[int] = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / ( self.nir + self.red + 0.5 ) return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red) def _lowerCAmelCase( self , __lowerCAmelCase=0.1_6 ) -> Optional[Any]: return (self.nir - self.green) / (self.nir + self.green + y) def _lowerCAmelCase( self , __lowerCAmelCase=0.5 ) -> Dict: return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n) def _lowerCAmelCase( self ) -> Dict: return np.arctan( ((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) ) def _lowerCAmelCase( self , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Any: return (self.nir - b) / (a * self.red) def _lowerCAmelCase( self ) -> Optional[Any]: return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1) def _lowerCAmelCase( self ) -> Optional[Any]: return (self.red + self.green + self.blue) / 3_0.5 def _lowerCAmelCase( self ) -> Any: return self.nir / self.red def _lowerCAmelCase( self ) -> Tuple: return (self.rvi() - 1) / (self.rvi() + 1) def _lowerCAmelCase( self ) -> List[Any]: return ( (2 * self.nir + 1) - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2) ) / 2 def _lowerCAmelCase( self ) -> Optional[int]: return self.green / (self.nir + self.red + self.green) def _lowerCAmelCase( self ) -> str: return self.nir / (self.nir + self.red + self.green) def _lowerCAmelCase( self ) -> Optional[int]: return self.red / (self.nir + self.red + self.green) def _lowerCAmelCase( self ) -> Tuple: return (self.green - self.red) / (self.green + self.red) def _lowerCAmelCase( self ) -> Dict: return (self.red - self.green) / (self.red + self.green) def _lowerCAmelCase( self ) -> Any: lowercase__ : Optional[int] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] ) lowercase__ : str = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] ) return (max_value - min_value) / max_value def _lowerCAmelCase( self ) -> str: return (2 * self.red - self.green - self.blue) / (self.green - self.blue) def _lowerCAmelCase( self ) -> int: return self.nir / self.red def _lowerCAmelCase( self ) -> Any: return (self.ndvi() + 0.5) ** (1 / 2) def _lowerCAmelCase( self ) -> Union[str, Any]: return (self.nir - self.redEdge) / (self.nir + self.redEdge)
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"""simple docstring""" import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": _a = argparse.ArgumentParser( description=( """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned""" """ Distillation""" ) ) parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""]) parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str) parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str) parser.add_argument("""--vocab_transform""", action="""store_true""") _a = parser.parse_args() if args.model_type == "bert": _a = BertForMaskedLM.from_pretrained(args.model_name) _a = """bert""" else: raise ValueError("""args.model_type should be \"bert\".""") _a = model.state_dict() _a = {} for w in ["word_embeddings", "position_embeddings"]: _a = state_dict[F"""{prefix}.embeddings.{w}.weight"""] for w in ["weight", "bias"]: _a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""] _a = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}""" ] _a = state_dict[ F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}""" ] std_idx += 1 _a = state_dict["""cls.predictions.decoder.weight"""] _a = state_dict["""cls.predictions.bias"""] if args.vocab_transform: for w in ["weight", "bias"]: _a = state_dict[F"""cls.predictions.transform.dense.{w}"""] _a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""] print(F"""N layers selected for distillation: {std_idx}""") print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""") print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""") torch.save(compressed_sd, args.dump_checkpoint)
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import unittest from transformers import is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow if is_flax_available(): import optax from flax.training.common_utils import onehot from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration from transformers.models.ta.modeling_flax_ta import shift_tokens_right @require_torch @require_sentencepiece @require_tokenizers @require_flax class UpperCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def _snake_case ( self ): """simple docstring""" lowerCamelCase : Optional[Any] = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small" ) lowerCamelCase : int = AutoTokenizer.from_pretrained("google/mt5-small" ) lowerCamelCase : Optional[int] = tokenizer("Hello there" , return_tensors="np" ).input_ids lowerCamelCase : str = tokenizer("Hi I am" , return_tensors="np" ).input_ids lowerCamelCase : Optional[Any] = shift_tokens_right(__a , model.config.pad_token_id , model.config.decoder_start_token_id ) lowerCamelCase : Dict = model(__a , decoder_input_ids=__a ).logits lowerCamelCase : Any = optax.softmax_cross_entropy(__a , onehot(__a , logits.shape[-1] ) ).mean() lowerCamelCase : str = -(labels.shape[-1] * loss.item()) lowerCamelCase : Optional[int] = -84.9127 self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
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"""simple docstring""" import unittest from transformers import PegasusConfig, PegasusTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html _a = """platform""" import jax import jax.numpy as jnp import numpy as np from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel @require_flax class _UpperCAmelCase: lowercase__ = PegasusConfig lowercase__ = {} lowercase__ = 'gelu' def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = eos_token_id _UpperCamelCase = pad_token_id _UpperCamelCase = bos_token_id def UpperCAmelCase ( self) -> int: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size) _UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1) _UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , ) _UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a) return config, inputs_dict def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''') _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def UpperCAmelCase ( self , __a , __a , __a) -> Tuple: '''simple docstring''' _UpperCamelCase = 20 _UpperCamelCase = model_class_name(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids''']) _UpperCamelCase , _UpperCamelCase = ( inputs_dict['''decoder_input_ids'''], inputs_dict['''decoder_attention_mask'''], ) _UpperCamelCase = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ] , axis=-1 , ) _UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a) _UpperCamelCase = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _UpperCamelCase = model.decode( decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , ) _UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''') _UpperCamelCase = model.decode( decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , ) _UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a) _UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''') def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]: """simple docstring""" if attention_mask is None: _UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta ) if decoder_attention_mask is None: _UpperCamelCase = np.concatenate( [ np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ), np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ), ], axis=-1, ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_flax class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( FlaxPegasusForConditionalGeneration, FlaxPegasusModel, ) if is_flax_available() else () ) lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else () lowercase__ = True lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = FlaxPegasusModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[Any]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = self._prepare_for_class(__a , __a) _UpperCamelCase = model_class(__a) @jax.jit def encode_jitted(__a , __a=None , **__a): return model.encode(input_ids=__a , attention_mask=__a) with self.subTest('''JIT Enabled'''): _UpperCamelCase = encode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = encode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) def UpperCAmelCase ( self) -> str: '''simple docstring''' _UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): _UpperCamelCase = model_class(__a) _UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask''']) _UpperCamelCase = { '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''], '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''], '''encoder_outputs''': encoder_outputs, } @jax.jit def decode_jitted(__a , __a , __a): return model.decode( decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , ) with self.subTest('''JIT Enabled'''): _UpperCamelCase = decode_jitted(**__a).to_tuple() with self.subTest('''JIT Disabled'''): with jax.disable_jit(): _UpperCamelCase = decode_jitted(**__a).to_tuple() self.assertEqual(len(__a) , len(__a)) for jitted_output, output in zip(__a , __a): self.assertEqual(jitted_output.shape , output.shape) @slow def UpperCAmelCase ( self) -> int: '''simple docstring''' for model_class_name in self.all_model_classes: _UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a) _UpperCamelCase = np.ones((1, 1)) _UpperCamelCase = model(__a) self.assertIsNotNone(__a) @slow def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' _UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''') _UpperCamelCase = [ ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''', ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''', ] _UpperCamelCase = [ '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''', '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''', ] _UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a) _UpperCamelCase = model.generate(**__a , num_beams=2).sequences _UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a) assert tgt_text == decoded
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def lowerCAmelCase__ ( lowerCamelCase_ : Tuple ,lowerCamelCase_ : Dict ,lowerCamelCase_ : Optional[Any]): '''simple docstring''' lowerCAmelCase__ : str = len(__snake_case) lowerCAmelCase__ : Tuple = [[0] * n for i in range(__snake_case)] for i in range(__snake_case): lowerCAmelCase__ : int = y_points[i] for i in range(2 ,__snake_case): for j in range(__snake_case ,__snake_case): lowerCAmelCase__ : List[Any] = ( (xa - x_points[j - i + 1]) * q[j][i - 1] - (xa - x_points[j]) * q[j - 1][i - 1] ) / (x_points[j] - x_points[j - i + 1]) return [q[n - 1][n - 1], q] if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class _UpperCAmelCase: def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any: '''simple docstring''' _UpperCamelCase = parent _UpperCamelCase = batch_size _UpperCamelCase = seq_length _UpperCamelCase = is_training _UpperCamelCase = use_input_mask _UpperCamelCase = use_token_type_ids _UpperCamelCase = use_labels _UpperCamelCase = vocab_size _UpperCamelCase = hidden_size _UpperCamelCase = num_hidden_layers _UpperCamelCase = num_attention_heads _UpperCamelCase = intermediate_size _UpperCamelCase = hidden_act _UpperCamelCase = hidden_dropout_prob _UpperCamelCase = attention_probs_dropout_prob _UpperCamelCase = max_position_embeddings _UpperCamelCase = type_vocab_size _UpperCamelCase = type_sequence_label_size _UpperCamelCase = initializer_range _UpperCamelCase = num_labels _UpperCamelCase = num_choices _UpperCamelCase = scope _UpperCamelCase = projection_dim def UpperCAmelCase ( self) -> List[Any]: '''simple docstring''' _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size) _UpperCamelCase = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py _UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length]) _UpperCamelCase = None if self.use_token_type_ids: _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size) _UpperCamelCase = None _UpperCamelCase = None _UpperCamelCase = None if self.use_labels: _UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size) _UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels) _UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices) _UpperCamelCase = BertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , ) _UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]: '''simple docstring''' _UpperCamelCase = TFDPRContextEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder(config=__a) _UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a) _UpperCamelCase = model(__a , token_type_ids=__a) _UpperCamelCase = model(__a) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)) def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict: '''simple docstring''' _UpperCamelCase = TFDPRReader(config=__a) _UpperCamelCase = model(__a , attention_mask=__a) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)) self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,)) def UpperCAmelCase ( self) -> Any: '''simple docstring''' _UpperCamelCase = self.prepare_config_and_inputs() ( ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ( _UpperCamelCase ) , ) = config_and_inputs _UpperCamelCase = {'''input_ids''': input_ids} return config, inputs_dict @require_tf class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ): lowercase__ = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False lowercase__ = False def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRModelTester(self) _UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37) def UpperCAmelCase ( self) -> Optional[int]: '''simple docstring''' self.config_tester.run_common_tests() def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__a) def UpperCAmelCase ( self) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__a) def UpperCAmelCase ( self) -> Dict: '''simple docstring''' _UpperCamelCase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__a) @slow def UpperCAmelCase ( self) -> str: '''simple docstring''' for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a) self.assertIsNotNone(__a) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _UpperCamelCase = TFDPRReader.from_pretrained(__a) self.assertIsNotNone(__a) @require_tf class _UpperCAmelCase( unittest.TestCase ): @slow def UpperCAmelCase ( self) -> Tuple: '''simple docstring''' _UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''') _UpperCamelCase = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP] _UpperCamelCase = model(__a)[0] # embedding shape = (1, 768) # compare the actual values for a slice. _UpperCamelCase = tf.constant( [ [ 0.0323_6253, 0.1275_3335, 0.1681_8509, 0.0027_9786, 0.389_6933, 0.2426_4945, 0.217_8971, -0.0233_5227, -0.0848_1959, -0.1432_4117, ] ]) self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
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