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import argparse import hashlib import os import urllib import warnings import torch from torch import nn from tqdm import tqdm from transformers import WhisperConfig, WhisperForConditionalGeneration UpperCAmelCase_ : Union[str, Any] = { "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", } def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =["""layers""", """blocks"""] for k in ignore_keys: state_dict.pop(lowerCamelCase , lowerCamelCase ) UpperCAmelCase_ : Optional[Any] = { "blocks": "layers", "mlp.0": "fc1", "mlp.2": "fc2", "mlp_ln": "final_layer_norm", ".attn.query": ".self_attn.q_proj", ".attn.key": ".self_attn.k_proj", ".attn.value": ".self_attn.v_proj", ".attn_ln": ".self_attn_layer_norm", ".attn.out": ".self_attn.out_proj", ".cross_attn.query": ".encoder_attn.q_proj", ".cross_attn.key": ".encoder_attn.k_proj", ".cross_attn.value": ".encoder_attn.v_proj", ".cross_attn_ln": ".encoder_attn_layer_norm", ".cross_attn.out": ".encoder_attn.out_proj", "decoder.ln.": "decoder.layer_norm.", "encoder.ln.": "encoder.layer_norm.", "token_embedding": "embed_tokens", "encoder.positional_embedding": "encoder.embed_positions.weight", "decoder.positional_embedding": "decoder.embed_positions.weight", "ln_post": "layer_norm", } def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =list(s_dict.keys() ) for key in keys: __magic_name__ : Optional[Any] =key for k, v in WHISPER_MAPPING.items(): if k in key: __magic_name__ : Tuple =new_key.replace(lowerCamelCase , lowerCamelCase ) print(F"{key} -> {new_key}" ) __magic_name__ : int =s_dict.pop(lowerCamelCase ) return s_dict def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ , __magic_name__ : Optional[Any] =emb.weight.shape __magic_name__ : Optional[Any] =nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase ) __magic_name__ : Any =emb.weight.data return lin_layer def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase ) __magic_name__ : str =os.path.basename(lowerCamelCase ) __magic_name__ : Any =url.split("""/""" )[-2] __magic_name__ : int =os.path.join(lowerCamelCase , lowerCamelCase ) if os.path.exists(lowerCamelCase ) and not os.path.isfile(lowerCamelCase ): raise RuntimeError(F"{download_target} exists and is not a regular file" ) if os.path.isfile(lowerCamelCase ): __magic_name__ : Any =open(lowerCamelCase , """rb""" ).read() if hashlib.shaaaa(lowerCamelCase ).hexdigest() == expected_shaaaa: return model_bytes else: warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" ) with urllib.request.urlopen(lowerCamelCase ) as source, open(lowerCamelCase , """wb""" ) as output: with tqdm( total=int(source.info().get("""Content-Length""" ) ) , ncols=80 , unit="""iB""" , unit_scale=lowerCamelCase , unit_divisor=1024 ) as loop: while True: __magic_name__ : List[str] =source.read(8192 ) if not buffer: break output.write(lowerCamelCase ) loop.update(len(lowerCamelCase ) ) __magic_name__ : Optional[int] =open(lowerCamelCase , """rb""" ).read() if hashlib.shaaaa(lowerCamelCase ).hexdigest() != expected_shaaaa: raise RuntimeError( """Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.""" ) return model_bytes def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): if ".pt" not in checkpoint_path: __magic_name__ : int =_download(_MODELS[checkpoint_path] ) else: __magic_name__ : Tuple =torch.load(lowerCamelCase , map_location="""cpu""" ) __magic_name__ : Dict =original_checkpoint["""dims"""] __magic_name__ : List[Any] =original_checkpoint["""model_state_dict"""] __magic_name__ : Dict =state_dict["""decoder.token_embedding.weight"""] remove_ignore_keys_(lowerCamelCase ) rename_keys(lowerCamelCase ) __magic_name__ : Optional[Any] =True __magic_name__ : Dict =state_dict["""decoder.layers.0.fc1.weight"""].shape[0] __magic_name__ : Dict =WhisperConfig( vocab_size=dimensions["""n_vocab"""] , encoder_ffn_dim=lowerCamelCase , decoder_ffn_dim=lowerCamelCase , num_mel_bins=dimensions["""n_mels"""] , d_model=dimensions["""n_audio_state"""] , max_target_positions=dimensions["""n_text_ctx"""] , encoder_layers=dimensions["""n_audio_layer"""] , encoder_attention_heads=dimensions["""n_audio_head"""] , decoder_layers=dimensions["""n_text_layer"""] , decoder_attention_heads=dimensions["""n_text_state"""] , max_source_positions=dimensions["""n_audio_ctx"""] , ) __magic_name__ : Tuple =WhisperForConditionalGeneration(lowerCamelCase ) __magic_name__ , __magic_name__ : Union[str, Any] =model.model.load_state_dict(lowerCamelCase , strict=lowerCamelCase ) if len(lowerCamelCase ) > 0 and not set(lowerCamelCase ) <= { "encoder.embed_positions.weights", "decoder.embed_positions.weights", }: raise ValueError( """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,""" F" but all the following weights are missing {missing}" ) if tie_embeds: __magic_name__ : List[Any] =make_linear_from_emb(model.model.decoder.embed_tokens ) else: __magic_name__ : Optional[Any] =proj_out_weights model.save_pretrained(lowerCamelCase ) if __name__ == "__main__": UpperCAmelCase_ : Union[str, Any] = argparse.ArgumentParser() # # Required parameters parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints") parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") UpperCAmelCase_ : Dict = parser.parse_args() convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class __A ( unittest.TestCase ): def A__ ( self :Tuple ): '''simple docstring''' debug_launcher(test_script.main ) def A__ ( self :Dict ): '''simple docstring''' debug_launcher(test_ops.main )
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1
# Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer from .base import PipelineTool class __A ( UpperCamelCase__ ): UpperCamelCase = """philschmid/bart-large-cnn-samsum""" UpperCamelCase = ( """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """ """and returns a summary of the text.""" ) UpperCamelCase = """summarizer""" UpperCamelCase = AutoTokenizer UpperCamelCase = AutoModelForSeqaSeqLM UpperCamelCase = ["""text"""] UpperCamelCase = ["""text"""] def A__ ( self :Union[str, Any] , __snake_case :Tuple ): '''simple docstring''' return self.pre_processor(__snake_case , return_tensors="""pt""" , truncation=__snake_case ) def A__ ( self :str , __snake_case :Optional[int] ): '''simple docstring''' return self.model.generate(**__snake_case )[0] def A__ ( self :Optional[Any] , __snake_case :List[Any] ): '''simple docstring''' return self.pre_processor.decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )
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UpperCAmelCase_ : Tuple = 0 # The first color of the flag. UpperCAmelCase_ : Any = 1 # The second color of the flag. UpperCAmelCase_ : str = 2 # The third color of the flag. UpperCAmelCase_ : Tuple = (red, white, blue) def lowerCAmelCase_ ( lowerCamelCase ): if not sequence: return [] if len(lowerCamelCase ) == 1: return list(lowerCamelCase ) __magic_name__ : int =0 __magic_name__ : str =len(lowerCamelCase ) - 1 __magic_name__ : Optional[Any] =0 while mid <= high: if sequence[mid] == colors[0]: __magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low] low += 1 mid += 1 elif sequence[mid] == colors[1]: mid += 1 elif sequence[mid] == colors[2]: __magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid] high -= 1 else: __magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values" raise ValueError(lowerCamelCase ) return sequence if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip() UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")] print(F"""{dutch_national_flag_sort(unsorted)}""")
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1
from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__) UpperCAmelCase_ : List[Any] = { "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json", "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json", "kssteven/ibert-roberta-large-mnli": ( "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json" ), } class __A ( UpperCamelCase__ ): UpperCamelCase = """ibert""" def __init__( self :str , __snake_case :Optional[Any]=3_05_22 , __snake_case :int=7_68 , __snake_case :List[Any]=12 , __snake_case :str=12 , __snake_case :Optional[Any]=30_72 , __snake_case :List[Any]="gelu" , __snake_case :Tuple=0.1 , __snake_case :List[str]=0.1 , __snake_case :Optional[Any]=5_12 , __snake_case :Optional[Any]=2 , __snake_case :List[Any]=0.02 , __snake_case :int=1E-12 , __snake_case :int=1 , __snake_case :Optional[int]=0 , __snake_case :List[Any]=2 , __snake_case :Optional[Any]="absolute" , __snake_case :Optional[int]=False , __snake_case :int="none" , **__snake_case :Union[str, Any] , ): '''simple docstring''' super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case ) __magic_name__ : Tuple =vocab_size __magic_name__ : Tuple =hidden_size __magic_name__ : Any =num_hidden_layers __magic_name__ : Union[str, Any] =num_attention_heads __magic_name__ : str =hidden_act __magic_name__ : List[Any] =intermediate_size __magic_name__ : List[str] =hidden_dropout_prob __magic_name__ : Tuple =attention_probs_dropout_prob __magic_name__ : Tuple =max_position_embeddings __magic_name__ : Tuple =type_vocab_size __magic_name__ : Dict =initializer_range __magic_name__ : Optional[int] =layer_norm_eps __magic_name__ : Optional[int] =position_embedding_type __magic_name__ : Optional[Any] =quant_mode __magic_name__ : Optional[Any] =force_dequant class __A ( UpperCamelCase__ ): @property def A__ ( self :Dict ): '''simple docstring''' if self.task == "multiple-choice": __magic_name__ : Optional[int] ={0: """batch""", 1: """choice""", 2: """sequence"""} else: __magic_name__ : List[str] ={0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ] )
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class __A ( UpperCamelCase__ , UpperCamelCase__ ): UpperCamelCase = 1 @register_to_config def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ): '''simple docstring''' __magic_name__ : Dict =None __magic_name__ : List[str] =None __magic_name__ : str =None def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ): '''simple docstring''' __magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case ) def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ): '''simple docstring''' if self.timesteps is None: raise ValueError( """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score __magic_name__ : int =( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) __magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) __magic_name__ : str =std.flatten() while len(std.shape ) < len(score.shape ): __magic_name__ : List[str] =std.unsqueeze(-1 ) __magic_name__ : Union[str, Any] =-score / std # compute __magic_name__ : Tuple =-1.0 / len(self.timesteps ) __magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) __magic_name__ : Dict =beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): __magic_name__ : Any =beta_t.unsqueeze(-1 ) __magic_name__ : Dict =-0.5 * beta_t * x __magic_name__ : Optional[int] =torch.sqrt(__snake_case ) __magic_name__ : int =drift - diffusion**2 * score __magic_name__ : List[str] =x + drift * dt # add noise __magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype ) __magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__( self :List[Any] ): '''simple docstring''' return self.config.num_train_timesteps
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1
from collections.abc import Sequence def lowerCAmelCase_ ( lowerCamelCase = None ): if nums is None or not nums: raise ValueError("""Input sequence should not be empty""" ) __magic_name__ : str =nums[0] for i in range(1 , len(lowerCamelCase ) ): __magic_name__ : Any =nums[i] __magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase ) return ans if __name__ == "__main__": import doctest doctest.testmod() # Try on a sample input from the user UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip()) UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n] print(max_subsequence_sum(array))
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from typing import Dict, List, Optional, Union import numpy as np from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy UpperCAmelCase_ : Dict = logging.get_logger(__name__) class __A ( UpperCamelCase__ ): def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =feature_size __magic_name__ : Union[str, Any] =sampling_rate __magic_name__ : List[Any] =padding_value __magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" ) __magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case ) super().__init__(**__snake_case ) def A__ ( self :Any , __snake_case :Union[ BatchFeature, List[BatchFeature], Dict[str, BatchFeature], Dict[str, List[BatchFeature]], List[Dict[str, BatchFeature]], ] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ): '''simple docstring''' if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ): __magic_name__ : Union[str, Any] ={ key: [example[key] for example in processed_features] for key in processed_features[0].keys() } # The model's main input name, usually `input_values`, has be passed for padding if self.model_input_names[0] not in processed_features: raise ValueError( """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`""" f" to this method that includes {self.model_input_names[0]}, but you provided" f" {list(processed_features.keys() )}" ) __magic_name__ : int =processed_features[self.model_input_names[0]] __magic_name__ : Union[str, Any] =( return_attention_mask if return_attention_mask is not None else self.return_attention_mask ) if len(__snake_case ) == 0: if return_attention_mask: __magic_name__ : List[str] =[] return processed_features # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch __magic_name__ : Optional[int] =required_input[0] if isinstance(__snake_case , (list, tuple) ): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. __magic_name__ : Optional[Any] =0 while len(required_input[index] ) == 0: index += 1 if index < len(__snake_case ): __magic_name__ : List[str] =required_input[index][0] if return_tensors is None: if is_tf_tensor(__snake_case ): __magic_name__ : int ="""tf""" elif is_torch_tensor(__snake_case ): __magic_name__ : str ="""pt""" elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ): __magic_name__ : List[Any] ="""np""" else: raise ValueError( f"type of {first_element} unknown: {type(__snake_case )}. " """Should be one of a python, numpy, pytorch or tensorflow object.""" ) for key, value in processed_features.items(): if isinstance(value[0] , (int, float) ): __magic_name__ : List[str] =to_numpy(__snake_case ) else: __magic_name__ : str =[to_numpy(__snake_case ) for v in value] # Convert padding_strategy in PaddingStrategy __magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case ) __magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]] __magic_name__ : Dict =len(__snake_case ) if not all(len(__snake_case ) == batch_size for v in processed_features.values() ): raise ValueError("""Some items in the output dictionary have a different batch size than others.""" ) __magic_name__ : Optional[int] =[] for i in range(__snake_case ): __magic_name__ : Any ={k: v[i] for k, v in processed_features.items()} # truncation __magic_name__ : List[str] =self._truncate( __snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , ) truncated_inputs.append(__snake_case ) if padding_strategy == PaddingStrategy.LONGEST: # make sure that `max_length` cannot be longer than the longest truncated length __magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs ) __magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH __magic_name__ : str ={} for i in range(__snake_case ): # padding __magic_name__ : List[str] =self._pad( truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , ) for key, value in outputs.items(): if key not in batch_outputs: __magic_name__ : Dict =[] if value.dtype is np.dtype(np.floataa ): __magic_name__ : Optional[int] =value.astype(np.floataa ) batch_outputs[key].append(__snake_case ) return BatchFeature(__snake_case , tensor_type=__snake_case ) def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' __magic_name__ : Dict =processed_features[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: __magic_name__ : Any =len(__snake_case ) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length if return_attention_mask and "attention_mask" not in processed_features: __magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa ) if needs_to_be_padded: __magic_name__ : List[Any] =max_length - len(__snake_case ) if self.padding_side == "right": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (0, difference) ) __magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference) __magic_name__ : str =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) elif self.padding_side == "left": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (difference, 0) ) __magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0) __magic_name__ : List[Any] =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) else: raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) ) return processed_features def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' if not truncation: return processed_features elif truncation and max_length is None: raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" ) __magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]] # find `max_length` that fits `pad_to_multiple_of` if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : Any =len(__snake_case ) > max_length if needs_to_be_truncated: __magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length] if "attention_mask" in processed_features: __magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length] return processed_features def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ): '''simple docstring''' if padding is not False: if padding is True: __magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch elif not isinstance(__snake_case , __snake_case ): __magic_name__ : Optional[int] =PaddingStrategy(__snake_case ) elif isinstance(__snake_case , __snake_case ): __magic_name__ : Any =padding else: __magic_name__ : Any =PaddingStrategy.DO_NOT_PAD # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: raise ValueError( f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" ) # Test if we have a padding value if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None): raise ValueError( """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use""" """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" ) return padding_strategy
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import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401 from coval.conll import reader, util from coval.eval import evaluator import datasets UpperCAmelCase_ : Dict = datasets.logging.get_logger(__name__) UpperCAmelCase_ : Optional[int] = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n year = {2019},\n booktitle = {Proceedings of the 57th Annual Meeting of\n the Association for Computational Linguistics (Volume 1: Long Papers)},\n publisher = {Association for Computational Linguistics},\n address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n author = {Amit Bagga and Breck Baldwin},\n title = {Algorithms for Scoring Coreference Chains},\n booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n year = {1998},\n pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n author = {Xiaoqiang Luo},\n title = {On coreference resolution performance metrics},\n booktitle = {In Proc. of HLT/EMNLP},\n year = {2005},\n pages = {25--32},\n publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n author = \"Moosavi, Nafise Sadat and\n Strube, Michael\",\n booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n month = aug,\n year = \"2016\",\n address = \"Berlin, Germany\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/P16-1060\",\n doi = \"10.18653/v1/P16-1060\",\n pages = \"632--642\",\n}\n\n" UpperCAmelCase_ : Union[str, Any] = "\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn Type Description\n1 Document ID This is a variation on the document filename\n2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3 Word number\n4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5 Part-of-Speech\n6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.\n9 Word sense This is the word sense of the word in Column 3.\n10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11 Named Entities These columns identifies the spans representing various named entities.\n12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN Coreference Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n" UpperCAmelCase_ : Dict = "\nCalculates coreference evaluation metrics.\nArgs:\n predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n Each prediction is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n Each reference is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n keep_singletons: After extracting all mentions of key or system files,\n mentions whose corresponding coreference chain is of size one,\n are considered as singletons. The default evaluation mode will include\n singletons in evaluations if they are included in the key or the system files.\n By setting 'keep_singletons=False', all singletons in the key and system files\n will be excluded from the evaluation.\n NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n Minimum spans are determined using the MINA algorithm.\n\nReturns:\n 'mentions': mentions\n 'muc': MUC metric [Vilain et al, 1995]\n 'bcub': B-cubed [Bagga and Baldwin, 1998]\n 'ceafe': CEAFe [Luo et al., 2005]\n 'lea': LEA [Moosavi and Strube, 2016]\n 'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n >>> coval = datasets.load_metric('coval')\n >>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -',\n ... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)',\n ... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)',\n ... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -',\n ... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -',\n ... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -']\n >>> references = [words]\n >>> predictions = [words]\n >>> results = coval.compute(predictions=predictions, references=references)\n >>> print(results) # doctest:+ELLIPSIS\n {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n" def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase="dummy_doc" ): __magic_name__ : Optional[int] ={doc: key_lines} __magic_name__ : Optional[int] ={doc: sys_lines} __magic_name__ : Tuple ={} __magic_name__ : Optional[Any] =0 __magic_name__ : str =0 __magic_name__ : str =0 __magic_name__ : Optional[Any] =0 __magic_name__ : str =0 __magic_name__ : Tuple =0 __magic_name__ , __magic_name__ : str =reader.get_doc_mentions(lowerCamelCase , key_doc_lines[doc] , lowerCamelCase ) key_singletons_num += singletons_num if NP_only or min_span: __magic_name__ : List[Any] =reader.set_annotated_parse_trees(lowerCamelCase , key_doc_lines[doc] , lowerCamelCase , lowerCamelCase ) __magic_name__ , __magic_name__ : Dict =reader.get_doc_mentions(lowerCamelCase , sys_doc_lines[doc] , lowerCamelCase ) sys_singletons_num += singletons_num if NP_only or min_span: __magic_name__ : str =reader.set_annotated_parse_trees(lowerCamelCase , key_doc_lines[doc] , lowerCamelCase , lowerCamelCase ) if remove_nested: __magic_name__ , __magic_name__ : List[str] =reader.remove_nested_coref_mentions(lowerCamelCase , lowerCamelCase ) key_nested_coref_num += nested_mentions key_removed_nested_clusters += removed_clusters __magic_name__ , __magic_name__ : int =reader.remove_nested_coref_mentions(lowerCamelCase , lowerCamelCase ) sys_nested_coref_num += nested_mentions sys_removed_nested_clusters += removed_clusters __magic_name__ : Optional[Any] =reader.get_mention_assignments(lowerCamelCase , lowerCamelCase ) __magic_name__ : str =reader.get_mention_assignments(lowerCamelCase , lowerCamelCase ) __magic_name__ : List[str] =(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster) if remove_nested: logger.info( """Number of removed nested coreferring mentions in the key """ F"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}" ) logger.info( """Number of resulting singleton clusters in the key """ F"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}" ) if not keep_singletons: logger.info( F"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system " """files, respectively""" ) return doc_coref_infos def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Dict =get_coref_infos(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) __magic_name__ : Union[str, Any] ={} __magic_name__ : Union[str, Any] =0 __magic_name__ : Optional[Any] =0 for name, metric in metrics: __magic_name__ , __magic_name__ , __magic_name__ : Any =evaluator.evaluate_documents(lowerCamelCase , lowerCamelCase , beta=1 ) if name in ["muc", "bcub", "ceafe"]: conll += fa conll_subparts_num += 1 output_scores.update({F"{name}/recall": recall, F"{name}/precision": precision, F"{name}/f1": fa} ) logger.info( name.ljust(10 ) , F"Recall: {recall * 100:.2f}" , F" Precision: {precision * 100:.2f}" , F" F1: {fa * 100:.2f}" , ) if conll_subparts_num == 3: __magic_name__ : Union[str, Any] =(conll / 3) * 100 logger.info(F"CoNLL score: {conll:.2f}" ) output_scores.update({"""conll_score""": conll} ) return output_scores def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Any =False for line in key_lines: if not line.startswith("""#""" ): if len(line.split() ) > 6: __magic_name__ : Dict =line.split()[5] if not parse_col == "-": __magic_name__ : int =True break else: break return has_gold_parse @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class __A ( datasets.Metric ): def A__ ( self :Union[str, Any] ): '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" ) ), """references""": datasets.Sequence(datasets.Value("""string""" ) ), } ) , codebase_urls=["""https://github.com/ns-moosavi/coval"""] , reference_urls=[ """https://github.com/ns-moosavi/coval""", """https://www.aclweb.org/anthology/P16-1060""", """http://www.conll.cemantix.org/2012/data.html""", ] , ) def A__ ( self :List[str] , __snake_case :Union[str, Any] , __snake_case :Optional[int] , __snake_case :Any=True , __snake_case :Union[str, Any]=False , __snake_case :str=False , __snake_case :Optional[Any]=False ): '''simple docstring''' __magic_name__ : Union[str, Any] =[ ("""mentions""", evaluator.mentions), ("""muc""", evaluator.muc), ("""bcub""", evaluator.b_cubed), ("""ceafe""", evaluator.ceafe), ("""lea""", evaluator.lea), ] if min_span: __magic_name__ : List[str] =util.check_gold_parse_annotation(__snake_case ) if not has_gold_parse: raise NotImplementedError("""References should have gold parse annotation to use 'min_span'.""" ) # util.parse_key_file(key_file) # key_file = key_file + ".parsed" __magic_name__ : str =evaluate( key_lines=__snake_case , sys_lines=__snake_case , metrics=__snake_case , NP_only=__snake_case , remove_nested=__snake_case , keep_singletons=__snake_case , min_span=__snake_case , ) return score
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import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class __A ( nn.Module ): def __init__( self :List[Any] ): '''simple docstring''' super().__init__() __magic_name__ : Tuple =nn.Linear(3 , 4 ) __magic_name__ : Union[str, Any] =nn.BatchNormad(4 ) __magic_name__ : List[str] =nn.Linear(4 , 5 ) def A__ ( self :Dict , __snake_case :Tuple ): '''simple docstring''' return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) ) class __A ( UpperCamelCase__ ): def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ): '''simple docstring''' return (args[0] + 1,) + args[1:], kwargs class __A ( UpperCamelCase__ ): def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ): '''simple docstring''' return output + 1 class __A ( unittest.TestCase ): def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : Tuple =ModelHook() add_hook_to_module(__snake_case , __snake_case ) self.assertEqual(test_model._hf_hook , __snake_case ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : List[str] =ModelHook() add_hook_to_module(__snake_case , __snake_case ) add_hook_to_module(__snake_case , __snake_case , append=__snake_case ) self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case ) self.assertEqual(len(test_model._hf_hook.hooks ) , 2 ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() __magic_name__ : Any =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(x + 1 ) __magic_name__ : Optional[Any] =test_model(x + 2 ) __magic_name__ : int =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : int =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : str =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : List[str] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) assert torch.allclose(__snake_case , __snake_case , atol=1E-5 ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() __magic_name__ : Dict =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(__snake_case ) __magic_name__ : Dict =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Optional[int] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) assert torch.allclose(__snake_case , output + 2 , atol=1E-5 ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : Tuple =ModelForTest() __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Dict =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 ) ) self.assertTrue(outputa.requires_grad ) __magic_name__ : Any =True __magic_name__ : Any =test_model(__snake_case ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) ) self.assertEqual(model.lineara.weight.device , torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[Any] =model(__snake_case ) self.assertEqual(output.device , torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) ) __magic_name__ : int =torch.randn(2 , 3 ).to(0 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , torch.device(0 ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : int =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True} add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : Union[str, Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload __magic_name__ : Tuple ={ """execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True, """offload_buffers""": True, } add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Tuple =torch.randn(2 , 3 ) __magic_name__ : int =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : str =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Optional[int] =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Dict =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Any =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
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import unittest from transformers import SPIECE_UNDERLINE from transformers.models.speechta import SpeechTaTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.tokenization_utils import AddedToken from ...test_tokenization_common import TokenizerTesterMixin UpperCAmelCase_ : int = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model") @require_sentencepiece @require_tokenizers class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = SpeechTaTokenizer UpperCamelCase = False UpperCamelCase = True def A__ ( self :Dict ): '''simple docstring''' super().setUp() # We have a SentencePiece fixture for testing __magic_name__ : int =SpeechTaTokenizer(__snake_case ) __magic_name__ : Any =AddedToken("""<mask>""" , lstrip=__snake_case , rstrip=__snake_case ) __magic_name__ : List[Any] =mask_token tokenizer.add_special_tokens({"""mask_token""": mask_token} ) tokenizer.add_tokens(["""<ctc_blank>"""] ) tokenizer.save_pretrained(self.tmpdirname ) def A__ ( self :List[Any] , __snake_case :List[Any] ): '''simple docstring''' __magic_name__ : Union[str, Any] ="""this is a test""" __magic_name__ : Tuple ="""this is a test""" return input_text, output_text def A__ ( self :str , __snake_case :Tuple , __snake_case :Union[str, Any]=False , __snake_case :List[str]=20 , __snake_case :str=5 ): '''simple docstring''' __magic_name__ , __magic_name__ : List[str] =self.get_input_output_texts(__snake_case ) __magic_name__ : Tuple =tokenizer.encode(__snake_case , add_special_tokens=__snake_case ) __magic_name__ : Optional[int] =tokenizer.decode(__snake_case , clean_up_tokenization_spaces=__snake_case ) return text, ids def A__ ( self :str ): '''simple docstring''' __magic_name__ : int ="""<pad>""" __magic_name__ : Union[str, Any] =1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case ) , __snake_case ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case ) , __snake_case ) def A__ ( self :int ): '''simple docstring''' __magic_name__ : Optional[int] =list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] , """<s>""" ) self.assertEqual(vocab_keys[1] , """<pad>""" ) self.assertEqual(vocab_keys[-4] , """œ""" ) self.assertEqual(vocab_keys[-2] , """<mask>""" ) self.assertEqual(vocab_keys[-1] , """<ctc_blank>""" ) self.assertEqual(len(__snake_case ) , 81 ) def A__ ( self :str ): '''simple docstring''' self.assertEqual(self.get_tokenizer().vocab_size , 79 ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : List[Any] =self.get_tokenizers(do_lower_case=__snake_case ) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}" ): __magic_name__ : Tuple =tokenizer.vocab_size __magic_name__ : Optional[Any] =len(__snake_case ) self.assertNotEqual(__snake_case , 0 ) # We usually have added tokens from the start in tests because our vocab fixtures are # smaller than the original vocabs - let's not assert this # self.assertEqual(vocab_size, all_size) __magic_name__ : Optional[Any] =["""aaaaa bbbbbb""", """cccccccccdddddddd"""] __magic_name__ : Dict =tokenizer.add_tokens(__snake_case ) __magic_name__ : List[str] =tokenizer.vocab_size __magic_name__ : Tuple =len(__snake_case ) self.assertNotEqual(__snake_case , 0 ) self.assertEqual(__snake_case , __snake_case ) self.assertEqual(__snake_case , len(__snake_case ) ) self.assertEqual(__snake_case , all_size + len(__snake_case ) ) __magic_name__ : Any =tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=__snake_case ) self.assertGreaterEqual(len(__snake_case ) , 4 ) self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 ) __magic_name__ : Any ={"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""} __magic_name__ : Any =tokenizer.add_special_tokens(__snake_case ) __magic_name__ : Dict =tokenizer.vocab_size __magic_name__ : str =len(__snake_case ) self.assertNotEqual(__snake_case , 0 ) self.assertEqual(__snake_case , __snake_case ) self.assertEqual(__snake_case , len(__snake_case ) ) self.assertEqual(__snake_case , all_size_a + len(__snake_case ) ) __magic_name__ : Optional[int] =tokenizer.encode( """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=__snake_case ) self.assertGreaterEqual(len(__snake_case ) , 6 ) self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[0] , tokens[1] ) self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[-3] , tokens[-4] ) self.assertEqual(tokens[0] , tokenizer.eos_token_id ) self.assertEqual(tokens[-3] , tokenizer.pad_token_id ) def A__ ( self :Optional[int] ): '''simple docstring''' pass def A__ ( self :List[str] ): '''simple docstring''' pass def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Optional[int] =self.get_tokenizer() __magic_name__ : Optional[int] =tokenizer.tokenize("""This is a test""" ) # fmt: off self.assertListEqual(__snake_case , [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""] ) # fmt: on self.assertListEqual( tokenizer.convert_tokens_to_ids(__snake_case ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , ) __magic_name__ : int =tokenizer.tokenize("""I was born in 92000, and this is falsé.""" ) self.assertListEqual( __snake_case , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] ) __magic_name__ : Any =tokenizer.convert_tokens_to_ids(__snake_case ) # fmt: off self.assertListEqual(__snake_case , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] ) # fmt: on __magic_name__ : List[str] =tokenizer.convert_ids_to_tokens(__snake_case ) self.assertListEqual( __snake_case , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] ) @slow def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Dict =[ """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """ """general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """ """Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """ """models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""", """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """ """conditioning on both left and right context in all layers.""", """The quick brown fox jumps over the lazy dog.""", ] # fmt: off __magic_name__ : Tuple ={ """input_ids""": [ [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2], [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], ], """attention_mask""": [ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ] } # fmt: on self.tokenizer_integration_test_util( expected_encoding=__snake_case , model_name="""microsoft/speecht5_asr""" , revision="""c5ef64c71905caeccde0e4462ef3f9077224c524""" , sequences=__snake_case , )
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import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import XLMRobertaTokenizerFast from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = KandinskyInpaintPipeline UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""] UpperCamelCase = [ """prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image""", ] UpperCamelCase = [ """generator""", """height""", """width""", """latents""", """guidance_scale""", """negative_prompt""", """num_inference_steps""", """return_dict""", """guidance_scale""", """num_images_per_prompt""", """output_type""", """return_dict""", ] UpperCamelCase = False @property def A__ ( self :Union[str, Any] ): '''simple docstring''' return 32 @property def A__ ( self :Optional[Any] ): '''simple docstring''' return 32 @property def A__ ( self :List[Any] ): '''simple docstring''' return self.time_input_dim @property def A__ ( self :Dict ): '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self :List[Any] ): '''simple docstring''' return 1_00 @property def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" ) return tokenizer @property def A__ ( self :str ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : str =MCLIPConfig( numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , ) __magic_name__ : Tuple =MultilingualCLIP(__snake_case ) __magic_name__ : Optional[int] =text_encoder.eval() return text_encoder @property def A__ ( self :Dict ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Optional[Any] ={ """in_channels""": 9, # Out channels is double in channels because predicts mean and variance """out_channels""": 8, """addition_embed_type""": """text_image""", """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""), """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""), """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""", """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2), """layers_per_block""": 1, """encoder_hid_dim""": self.text_embedder_hidden_size, """encoder_hid_dim_type""": """text_image_proj""", """cross_attention_dim""": self.cross_attention_dim, """attention_head_dim""": 4, """resnet_time_scale_shift""": """scale_shift""", """class_embed_type""": None, } __magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case ) return model @property def A__ ( self :List[str] ): '''simple docstring''' return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def A__ ( self :Tuple ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs ) return model def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[str] =self.dummy_text_encoder __magic_name__ : Optional[Any] =self.dummy_tokenizer __magic_name__ : Optional[Any] =self.dummy_unet __magic_name__ : Tuple =self.dummy_movq __magic_name__ : List[str] =DDIMScheduler( num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , ) __magic_name__ : str ={ """text_encoder""": text_encoder, """tokenizer""": tokenizer, """unet""": unet, """scheduler""": scheduler, """movq""": movq, } return components def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ): '''simple docstring''' __magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case ) # create init_image __magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0] __magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) ) # create mask __magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa ) __magic_name__ : Any =0 if str(__snake_case ).startswith("""mps""" ): __magic_name__ : Dict =torch.manual_seed(__snake_case ) else: __magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case ) __magic_name__ : List[Any] ={ """prompt""": """horse""", """image""": init_image, """mask_image""": mask, """image_embeds""": image_embeds, """negative_image_embeds""": negative_image_embeds, """generator""": generator, """height""": 64, """width""": 64, """num_inference_steps""": 2, """guidance_scale""": 4.0, """output_type""": """np""", } return inputs def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Tuple ="""cpu""" __magic_name__ : List[Any] =self.get_dummy_components() __magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case ) __magic_name__ : Tuple =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) ) __magic_name__ : List[Any] =output.images __magic_name__ : Any =pipe( **self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0] __magic_name__ : int =image[0, -3:, -3:, -1] __magic_name__ : str =image_from_tuple[0, -3:, -3:, -1] print(f"image.shape {image.shape}" ) assert image.shape == (1, 64, 64, 3) __magic_name__ : Optional[Any] =np.array( [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" def A__ ( self :Dict ): '''simple docstring''' super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) @slow @require_torch_gpu class __A ( unittest.TestCase ): def A__ ( self :List[Any] ): '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : List[str] =load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" ) __magic_name__ : int =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" ) __magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa ) __magic_name__ : Any =0 __magic_name__ : int ="""a hat""" __magic_name__ : int =KandinskyPriorPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa ) pipe_prior.to(__snake_case ) __magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa ) __magic_name__ : int =pipeline.to(__snake_case ) pipeline.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 ) __magic_name__ , __magic_name__ : Dict =pipe_prior( __snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple() __magic_name__ : Optional[Any] =pipeline( __snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , ) __magic_name__ : Optional[int] =output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(__snake_case , __snake_case )
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1
from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) UpperCAmelCase_ : List[str] = { "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Union[str, Any] = ["MobileViTFeatureExtractor"] UpperCAmelCase_ : int = ["MobileViTImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Dict = [ "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileViTForImageClassification", "MobileViTForSemanticSegmentation", "MobileViTModel", "MobileViTPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Tuple = [ "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMobileViTForImageClassification", "TFMobileViTForSemanticSegmentation", "TFMobileViTModel", "TFMobileViTPreTrainedModel", ] if TYPE_CHECKING: from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_mobilevit import MobileViTFeatureExtractor from .image_processing_mobilevit import MobileViTImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mobilevit import ( MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel, MobileViTPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_mobilevit import ( TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel, TFMobileViTPreTrainedModel, ) else: import sys UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import inspect import unittest import numpy as np from tests.test_modeling_common import floats_tensor from transformers import MaskaFormerConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel if is_vision_available(): from transformers import MaskaFormerImageProcessor if is_vision_available(): from PIL import Image class __A : def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ): '''simple docstring''' __magic_name__ : Optional[int] =parent __magic_name__ : List[Any] =batch_size __magic_name__ : List[str] =is_training __magic_name__ : List[str] =use_auxiliary_loss __magic_name__ : Union[str, Any] =num_queries __magic_name__ : str =num_channels __magic_name__ : Union[str, Any] =min_size __magic_name__ : Union[str, Any] =max_size __magic_name__ : Optional[int] =num_labels __magic_name__ : Tuple =hidden_dim __magic_name__ : Any =hidden_dim def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to( __snake_case ) __magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case ) __magic_name__ : List[str] =( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5 ).float() __magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long() __magic_name__ : str =self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Dict =MaskaFormerConfig( hidden_size=self.hidden_dim , ) __magic_name__ : str =self.num_queries __magic_name__ : Dict =self.num_labels __magic_name__ : int =[1, 1, 1, 1] __magic_name__ : List[str] =self.num_channels __magic_name__ : str =64 __magic_name__ : List[str] =1_28 __magic_name__ : Optional[Any] =self.hidden_dim __magic_name__ : Tuple =self.hidden_dim __magic_name__ : Optional[int] =self.hidden_dim return config def A__ ( self :Any ): '''simple docstring''' __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs() __magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask} return config, inputs_dict def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ): '''simple docstring''' __magic_name__ : int =output.encoder_hidden_states __magic_name__ : List[str] =output.pixel_decoder_hidden_states __magic_name__ : int =output.transformer_decoder_hidden_states self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , config.decoder_layers ) def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ): '''simple docstring''' with torch.no_grad(): __magic_name__ : List[str] =MaskaFormerModel(config=__snake_case ) model.to(__snake_case ) model.eval() __magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case ) self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(output.encoder_last_hidden_state is not None ) if output_hidden_states: self.check_output_hidden_state(__snake_case , __snake_case ) def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ): '''simple docstring''' __magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case ) model.to(__snake_case ) model.eval() def comm_check_on_output(__snake_case :List[str] ): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.encoder_last_hidden_state is not None ) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): __magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : List[str] =model(__snake_case ) comm_check_on_output(__snake_case ) __magic_name__ : Any =model( pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) comm_check_on_output(__snake_case ) self.parent.assertTrue(result.loss is not None ) self.parent.assertEqual(result.loss.shape , torch.Size([1] ) ) @require_torch class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else () UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {} UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False def A__ ( self :str ): '''simple docstring''' __magic_name__ : Any =MaskaFormerModelTester(self ) __magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case ) def A__ ( self :Dict ): '''simple docstring''' self.config_tester.run_common_tests() def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case ) @unittest.skip(reason="""Mask2Former does not use inputs_embeds""" ) def A__ ( self :List[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" ) def A__ ( self :Dict ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former is not a generative model""" ) def A__ ( self :Optional[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not use token embeddings""" ) def A__ ( self :int ): '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip( reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" ) def A__ ( self :Tuple ): '''simple docstring''' pass @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" ) def A__ ( self :Union[str, Any] ): '''simple docstring''' pass def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : Tuple =model_class(__snake_case ) __magic_name__ : Optional[Any] =inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic __magic_name__ : Tuple =[*signature.parameters.keys()] __magic_name__ : Optional[Any] =["""pixel_values"""] self.assertListEqual(arg_names[:1] , __snake_case ) @slow def A__ ( self :Tuple ): '''simple docstring''' for model_name in ["facebook/mask2former-swin-small-coco-instance"]: __magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =(self.model_tester.min_size,) * 2 __magic_name__ : Union[str, Any] ={ """pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ), """mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ), """class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(), } __magic_name__ : Optional[Any] =self.model_tester.get_config() __magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case ) __magic_name__ : Any =model(**__snake_case ) self.assertTrue(outputs.loss is not None ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case ) __magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case ) self.assertTrue(outputs.attentions is not None ) def A__ ( self :int ): '''simple docstring''' if not self.model_tester.is_training: return __magic_name__ : List[Any] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Dict =model_class(__snake_case ) model.to(__snake_case ) model.train() __magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss loss.backward() def A__ ( self :int ): '''simple docstring''' __magic_name__ : List[str] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Tuple =True __magic_name__ : Optional[int] =True __magic_name__ : int =model_class(__snake_case ).to(__snake_case ) model.train() __magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) __magic_name__ : Optional[int] =outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() __magic_name__ : Optional[int] =outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=__snake_case ) self.assertIsNotNone(encoder_hidden_states.grad ) self.assertIsNotNone(pixel_decoder_hidden_states.grad ) self.assertIsNotNone(transformer_decoder_hidden_states.grad ) self.assertIsNotNone(attentions.grad ) UpperCAmelCase_ : Dict = 1e-4 def lowerCAmelCase_ ( ): __magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_vision @slow class __A ( unittest.TestCase ): @cached_property def A__ ( self :int ): '''simple docstring''' return "facebook/mask2former-swin-small-coco-instance" @cached_property def A__ ( self :int ): '''simple docstring''' return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case ) __magic_name__ : int =self.default_image_processor __magic_name__ : List[Any] =prepare_img() __magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Dict =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : List[str] =model(**__snake_case ) __magic_name__ : Any =torch.tensor( [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Dict =torch.tensor( [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Any =torch.tensor( [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Optional[int] =self.default_image_processor __magic_name__ : Tuple =prepare_img() __magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : str =model(**__snake_case ) # masks_queries_logits __magic_name__ : List[Any] =outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ) __magic_name__ : List[Any] =[ [-8.7839, -9.0056, -8.8121], [-7.4104, -7.0313, -6.5401], [-6.6105, -6.3427, -6.4675], ] __magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) # class_queries_logits __magic_name__ : Any =outputs.class_queries_logits self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) ) __magic_name__ : int =torch.tensor( [ [1.8324, -8.0835, -4.1922], [0.8450, -9.0050, -3.6053], [0.3045, -7.7293, -3.0275], ] ).to(__snake_case ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Any =self.default_image_processor __magic_name__ : Union[str, Any] =image_processor( [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , ) __magic_name__ : str =inputs["""pixel_values"""].to(__snake_case ) __magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]] __magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]] with torch.no_grad(): __magic_name__ : Dict =model(**__snake_case ) self.assertTrue(outputs.loss is not None )
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import gc import unittest from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline from diffusers.utils import is_flax_available, load_image, slow from diffusers.utils.testing_utils import require_flax if is_flax_available(): import jax import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard @slow @require_flax class __A ( unittest.TestCase ): def A__ ( self :Tuple ): '''simple docstring''' super().tearDown() gc.collect() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =FlaxControlNetModel.from_pretrained( """lllyasviel/sd-controlnet-canny""" , from_pt=__snake_case , dtype=jnp.bfloataa ) __magic_name__ , __magic_name__ : Union[str, Any] =FlaxStableDiffusionControlNetPipeline.from_pretrained( """runwayml/stable-diffusion-v1-5""" , controlnet=__snake_case , from_pt=__snake_case , dtype=jnp.bfloataa ) __magic_name__ : Union[str, Any] =controlnet_params __magic_name__ : int ="""bird""" __magic_name__ : Optional[int] =jax.device_count() __magic_name__ : Optional[int] =pipe.prepare_text_inputs([prompts] * num_samples ) __magic_name__ : List[str] =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ) __magic_name__ : Union[str, Any] =pipe.prepare_image_inputs([canny_image] * num_samples ) __magic_name__ : Union[str, Any] =jax.random.PRNGKey(0 ) __magic_name__ : List[Any] =jax.random.split(__snake_case , jax.device_count() ) __magic_name__ : Dict =replicate(__snake_case ) __magic_name__ : Tuple =shard(__snake_case ) __magic_name__ : int =shard(__snake_case ) __magic_name__ : Optional[Any] =pipe( prompt_ids=__snake_case , image=__snake_case , params=__snake_case , prng_seed=__snake_case , num_inference_steps=50 , jit=__snake_case , ).images assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3) __magic_name__ : Any =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] ) __magic_name__ : List[Any] =images[0, 2_53:2_56, 2_53:2_56, -1] __magic_name__ : Dict =jnp.asarray(jax.device_get(image_slice.flatten() ) ) __magic_name__ : Optional[int] =jnp.array( [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] ) print(f"output_slice: {output_slice}" ) assert jnp.abs(output_slice - expected_slice ).max() < 1E-2 def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ , __magic_name__ : Optional[int] =FlaxControlNetModel.from_pretrained( """lllyasviel/sd-controlnet-openpose""" , from_pt=__snake_case , dtype=jnp.bfloataa ) __magic_name__ , __magic_name__ : Tuple =FlaxStableDiffusionControlNetPipeline.from_pretrained( """runwayml/stable-diffusion-v1-5""" , controlnet=__snake_case , from_pt=__snake_case , dtype=jnp.bfloataa ) __magic_name__ : List[Any] =controlnet_params __magic_name__ : Optional[int] ="""Chef in the kitchen""" __magic_name__ : Dict =jax.device_count() __magic_name__ : Optional[int] =pipe.prepare_text_inputs([prompts] * num_samples ) __magic_name__ : Union[str, Any] =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" ) __magic_name__ : Optional[Any] =pipe.prepare_image_inputs([pose_image] * num_samples ) __magic_name__ : Any =jax.random.PRNGKey(0 ) __magic_name__ : Optional[Any] =jax.random.split(__snake_case , jax.device_count() ) __magic_name__ : str =replicate(__snake_case ) __magic_name__ : Optional[int] =shard(__snake_case ) __magic_name__ : List[str] =shard(__snake_case ) __magic_name__ : List[str] =pipe( prompt_ids=__snake_case , image=__snake_case , params=__snake_case , prng_seed=__snake_case , num_inference_steps=50 , jit=__snake_case , ).images assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3) __magic_name__ : Union[str, Any] =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] ) __magic_name__ : Dict =images[0, 2_53:2_56, 2_53:2_56, -1] __magic_name__ : Union[str, Any] =jnp.asarray(jax.device_get(image_slice.flatten() ) ) __magic_name__ : str =jnp.array( [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] ) print(f"output_slice: {output_slice}" ) assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
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import warnings from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "nvidia/segformer-b0-finetuned-ade-512-512": ( "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json" ), # See all SegFormer models at https://huggingface.co/models?filter=segformer } class __A ( UpperCamelCase__ ): UpperCamelCase = """segformer""" def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ): '''simple docstring''' super().__init__(**__snake_case ) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be""" """ removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , ) __magic_name__ : Dict =num_channels __magic_name__ : str =num_encoder_blocks __magic_name__ : List[Any] =depths __magic_name__ : Optional[Any] =sr_ratios __magic_name__ : List[str] =hidden_sizes __magic_name__ : List[str] =patch_sizes __magic_name__ : Any =strides __magic_name__ : Optional[Any] =mlp_ratios __magic_name__ : str =num_attention_heads __magic_name__ : int =hidden_act __magic_name__ : List[Any] =hidden_dropout_prob __magic_name__ : Optional[Any] =attention_probs_dropout_prob __magic_name__ : Optional[Any] =classifier_dropout_prob __magic_name__ : List[str] =initializer_range __magic_name__ : List[str] =drop_path_rate __magic_name__ : List[Any] =layer_norm_eps __magic_name__ : List[str] =decoder_hidden_size __magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case ) __magic_name__ : Dict =semantic_loss_ignore_index class __A ( UpperCamelCase__ ): UpperCamelCase = version.parse("""1.11""" ) @property def A__ ( self :List[str] ): '''simple docstring''' return OrderedDict( [ ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}), ] ) @property def A__ ( self :Any ): '''simple docstring''' return 1E-4 @property def A__ ( self :int ): '''simple docstring''' return 12
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1
import argparse import collections import json from pathlib import Path import requests import torch import yaml from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( MobileViTImageProcessor, MobileViTVaConfig, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, ) from transformers.utils import logging logging.set_verbosity_info() UpperCAmelCase_ : str = logging.get_logger(__name__) def lowerCAmelCase_ ( lowerCamelCase ): print("""Loading config file...""" ) def flatten_yaml_as_dict(lowerCamelCase , lowerCamelCase="" , lowerCamelCase="." ): __magic_name__ : int =[] for k, v in d.items(): __magic_name__ : Dict =parent_key + sep + k if parent_key else k if isinstance(lowerCamelCase , collections.abc.MutableMapping ): items.extend(flatten_yaml_as_dict(lowerCamelCase , lowerCamelCase , sep=lowerCamelCase ).items() ) else: items.append((new_key, v) ) return dict(lowerCamelCase ) __magic_name__ : List[Any] =argparse.Namespace() with open(lowerCamelCase , """r""" ) as yaml_file: try: __magic_name__ : List[Any] =yaml.load(lowerCamelCase , Loader=yaml.FullLoader ) __magic_name__ : List[Any] =flatten_yaml_as_dict(lowerCamelCase ) for k, v in flat_cfg.items(): setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase ) except yaml.YAMLError as exc: logger.error("""Error while loading config file: {}. Error message: {}""".format(lowerCamelCase , str(lowerCamelCase ) ) ) return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ : Union[str, Any] =MobileViTVaConfig() __magic_name__ : Any =False # dataset if task_name.startswith("""imagenet1k_""" ): __magic_name__ : Optional[Any] =1000 if int(task_name.strip().split("""_""" )[-1] ) == 384: __magic_name__ : Dict =384 else: __magic_name__ : Dict =256 __magic_name__ : Tuple ="""imagenet-1k-id2label.json""" elif task_name.startswith("""imagenet21k_to_1k_""" ): __magic_name__ : Optional[Any] =21000 if int(task_name.strip().split("""_""" )[-1] ) == 384: __magic_name__ : Optional[Any] =384 else: __magic_name__ : List[Any] =256 __magic_name__ : Union[str, Any] ="""imagenet-22k-id2label.json""" elif task_name.startswith("""ade20k_""" ): __magic_name__ : List[str] =151 __magic_name__ : Union[str, Any] =512 __magic_name__ : List[str] ="""ade20k-id2label.json""" __magic_name__ : Optional[int] =True elif task_name.startswith("""voc_""" ): __magic_name__ : Dict =21 __magic_name__ : Optional[Any] =512 __magic_name__ : Tuple ="""pascal-voc-id2label.json""" __magic_name__ : int =True # orig_config __magic_name__ : int =load_orig_config_file(lowerCamelCase ) assert getattr(lowerCamelCase , """model.classification.name""" , -1 ) == "mobilevit_v2", "Invalid model" __magic_name__ : List[str] =getattr(lowerCamelCase , """model.classification.mitv2.width_multiplier""" , 1.0 ) assert ( getattr(lowerCamelCase , """model.classification.mitv2.attn_norm_layer""" , -1 ) == "layer_norm_2d" ), "Norm layers other than layer_norm_2d is not supported" __magic_name__ : Dict =getattr(lowerCamelCase , """model.classification.activation.name""" , """swish""" ) # config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256) if is_segmentation_model: __magic_name__ : Optional[int] =getattr(lowerCamelCase , """model.segmentation.output_stride""" , 16 ) if "_deeplabv3" in task_name: __magic_name__ : str =getattr(lowerCamelCase , """model.segmentation.deeplabv3.aspp_rates""" , [12, 24, 36] ) __magic_name__ : int =getattr(lowerCamelCase , """model.segmentation.deeplabv3.aspp_out_channels""" , 512 ) __magic_name__ : str =getattr(lowerCamelCase , """model.segmentation.deeplabv3.aspp_dropout""" , 0.1 ) # id2label __magic_name__ : Tuple ="""huggingface/label-files""" __magic_name__ : Dict =json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) ) __magic_name__ : Optional[int] ={int(lowerCamelCase ): v for k, v in idalabel.items()} __magic_name__ : List[str] =idalabel __magic_name__ : str ={v: k for k, v in idalabel.items()} return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Optional[Any] =dct.pop(lowerCamelCase ) __magic_name__ : Optional[int] =val def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): if base_model: __magic_name__ : Tuple ="""""" else: __magic_name__ : int ="""mobilevitv2.""" __magic_name__ : Optional[Any] =[] for k in state_dict.keys(): if k[:8] == "encoder.": __magic_name__ : List[Any] =k[8:] else: __magic_name__ : int =k if ".block." in k: __magic_name__ : Dict =k_new.replace(""".block.""" , """.""" ) if ".conv." in k: __magic_name__ : List[str] =k_new.replace(""".conv.""" , """.convolution.""" ) if ".norm." in k: __magic_name__ : List[Any] =k_new.replace(""".norm.""" , """.normalization.""" ) if "conv_1." in k: __magic_name__ : Optional[int] =k_new.replace("""conv_1.""" , F"{model_prefix}conv_stem." ) for i in [1, 2]: if F"layer_{i}." in k: __magic_name__ : Optional[Any] =k_new.replace(F"layer_{i}." , F"{model_prefix}encoder.layer.{i-1}.layer." ) if ".exp_1x1." in k: __magic_name__ : Optional[Any] =k_new.replace(""".exp_1x1.""" , """.expand_1x1.""" ) if ".red_1x1." in k: __magic_name__ : Any =k_new.replace(""".red_1x1.""" , """.reduce_1x1.""" ) for i in [3, 4, 5]: if F"layer_{i}.0." in k: __magic_name__ : Optional[Any] =k_new.replace(F"layer_{i}.0." , F"{model_prefix}encoder.layer.{i-1}.downsampling_layer." ) if F"layer_{i}.1.local_rep.0." in k: __magic_name__ : List[str] =k_new.replace(F"layer_{i}.1.local_rep.0." , F"{model_prefix}encoder.layer.{i-1}.conv_kxk." ) if F"layer_{i}.1.local_rep.1." in k: __magic_name__ : Any =k_new.replace(F"layer_{i}.1.local_rep.1." , F"{model_prefix}encoder.layer.{i-1}.conv_1x1." ) for i in [3, 4, 5]: if i == 3: __magic_name__ : int =[0, 1] elif i == 4: __magic_name__ : Tuple =[0, 1, 2, 3] elif i == 5: __magic_name__ : str =[0, 1, 2] for j in j_in: if F"layer_{i}.1.global_rep.{j}." in k: __magic_name__ : List[Any] =k_new.replace( F"layer_{i}.1.global_rep.{j}." , F"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}." ) if F"layer_{i}.1.global_rep.{j+1}." in k: __magic_name__ : int =k_new.replace( F"layer_{i}.1.global_rep.{j+1}." , F"{model_prefix}encoder.layer.{i-1}.layernorm." ) if F"layer_{i}.1.conv_proj." in k: __magic_name__ : List[Any] =k_new.replace(F"layer_{i}.1.conv_proj." , F"{model_prefix}encoder.layer.{i-1}.conv_projection." ) if "pre_norm_attn.0." in k: __magic_name__ : Dict =k_new.replace("""pre_norm_attn.0.""" , """layernorm_before.""" ) if "pre_norm_attn.1." in k: __magic_name__ : Tuple =k_new.replace("""pre_norm_attn.1.""" , """attention.""" ) if "pre_norm_ffn.0." in k: __magic_name__ : List[Any] =k_new.replace("""pre_norm_ffn.0.""" , """layernorm_after.""" ) if "pre_norm_ffn.1." in k: __magic_name__ : int =k_new.replace("""pre_norm_ffn.1.""" , """ffn.conv1.""" ) if "pre_norm_ffn.3." in k: __magic_name__ : Union[str, Any] =k_new.replace("""pre_norm_ffn.3.""" , """ffn.conv2.""" ) if "classifier.1." in k: __magic_name__ : int =k_new.replace("""classifier.1.""" , """classifier.""" ) if "seg_head." in k: __magic_name__ : Any =k_new.replace("""seg_head.""" , """segmentation_head.""" ) if ".aspp_layer." in k: __magic_name__ : List[Any] =k_new.replace(""".aspp_layer.""" , """.""" ) if ".aspp_pool." in k: __magic_name__ : str =k_new.replace(""".aspp_pool.""" , """.""" ) rename_keys.append((k, k_new) ) return rename_keys def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Tuple =[] for k in state_dict.keys(): if k.startswith("""seg_head.aux_head.""" ): keys_to_ignore.append(lowerCamelCase ) for k in keys_to_ignore: state_dict.pop(lowerCamelCase , lowerCamelCase ) def lowerCAmelCase_ ( ): __magic_name__ : List[Any] ="""http://images.cocodataset.org/val2017/000000039769.jpg""" # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg" __magic_name__ : Optional[Any] =Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw ) return im @torch.no_grad() def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Optional[int] =get_mobilevitva_config(lowerCamelCase , lowerCamelCase ) # load original state_dict __magic_name__ : Union[str, Any] =torch.load(lowerCamelCase , map_location="""cpu""" ) # load huggingface model if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ): __magic_name__ : Union[str, Any] =MobileViTVaForSemanticSegmentation(lowerCamelCase ).eval() __magic_name__ : Any =False else: __magic_name__ : Tuple =MobileViTVaForImageClassification(lowerCamelCase ).eval() __magic_name__ : Dict =False # remove and rename some keys of load the original model __magic_name__ : Tuple =checkpoint remove_unused_keys(lowerCamelCase ) __magic_name__ : List[str] =create_rename_keys(lowerCamelCase , base_model=lowerCamelCase ) for rename_key_src, rename_key_dest in rename_keys: rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase ) # load modified state_dict model.load_state_dict(lowerCamelCase ) # Check outputs on an image, prepared by MobileViTImageProcessor __magic_name__ : Optional[int] =MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 ) __magic_name__ : Optional[int] =image_processor(images=prepare_img() , return_tensors="""pt""" ) __magic_name__ : Tuple =model(**lowerCamelCase ) # verify classification model if task_name.startswith("""imagenet""" ): __magic_name__ : int =outputs.logits __magic_name__ : str =logits.argmax(-1 ).item() print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] ) if task_name.startswith("""imagenet1k_256""" ) and config.width_multiplier == 1.0: # expected_logits for base variant __magic_name__ : str =torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] ) assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4 ) Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase ) print(F"Saving model {task_name} to {pytorch_dump_folder_path}" ) model.save_pretrained(lowerCamelCase ) print(F"Saving image processor to {pytorch_dump_folder_path}" ) image_processor.save_pretrained(lowerCamelCase ) if __name__ == "__main__": UpperCAmelCase_ : Any = argparse.ArgumentParser() # Required parameters parser.add_argument( "--task", default="imagenet1k_256", type=str, help=( "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . " "\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n " ), choices=[ "imagenet1k_256", "imagenet1k_384", "imagenet21k_to_1k_256", "imagenet21k_to_1k_384", "ade20k_deeplabv3", "voc_deeplabv3", ], ) parser.add_argument( "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)." ) parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.") parser.add_argument( "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory." ) UpperCAmelCase_ : Optional[int] = parser.parse_args() convert_mobilevitva_checkpoint( args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path )
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import heapq def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : list[list] =[] # 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(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] ) # chosen_vertices = set of chosen vertices __magic_name__ : Tuple =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 __magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0] chosen_vertices.add(lowerCamelCase ) # 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]: __magic_name__ : Tuple =elem[1][1].index(lowerCamelCase ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(lowerCamelCase ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : Optional[int] = {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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1
import argparse import os import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType from accelerate.local_sgd import LocalSGD ######################################################################## # This is a fully working simple example to use Accelerate # with LocalSGD, which is a method to synchronize model # parameters every K batches. It is different, but complementary # to gradient accumulation. # # This example trains a Bert base model on GLUE MRPC # in any of the following settings (with the same script): # - single CPU or single GPU # - multi GPUS (using PyTorch distributed mode) # - (multi) TPUs # - fp16 (mixed-precision) or fp32 (normal precision) # # To run it in each of these various modes, follow the instructions # in the readme for examples: # https://github.com/huggingface/accelerate/tree/main/examples # ######################################################################## UpperCAmelCase_ : Any = 16 UpperCAmelCase_ : int = 32 def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 16 ): __magic_name__ : List[str] =AutoTokenizer.from_pretrained("""bert-base-cased""" ) __magic_name__ : str =load_dataset("""glue""" , """mrpc""" ) def tokenize_function(lowerCamelCase ): # max_length=None => use the model max length (it's actually the default) __magic_name__ : List[str] =tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase ) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset # starting with the main process first: with accelerator.main_process_first(): __magic_name__ : int =datasets.map( lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library __magic_name__ : Any =tokenized_datasets.rename_column("""label""" , """labels""" ) def collate_fn(lowerCamelCase ): # On TPU it's best to pad everything to the same length or training will be very slow. __magic_name__ : List[Any] =128 if accelerator.distributed_type == DistributedType.TPU else None # When using mixed precision we want round multiples of 8/16 if accelerator.mixed_precision == "fp8": __magic_name__ : List[str] =16 elif accelerator.mixed_precision != "no": __magic_name__ : Tuple =8 else: __magic_name__ : Dict =None return tokenizer.pad( lowerCamelCase , padding="""longest""" , max_length=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_tensors="""pt""" , ) # Instantiate dataloaders. __magic_name__ : Tuple =DataLoader( tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase ) __magic_name__ : Dict =DataLoader( tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase ) return train_dataloader, eval_dataloader # For testing only if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": from accelerate.test_utils.training import mocked_dataloaders UpperCAmelCase_ : Tuple = mocked_dataloaders # noqa: F811 def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): # For testing only if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowerCamelCase ) == "1": __magic_name__ : Tuple =2 # New Code # __magic_name__ : List[Any] =int(args.gradient_accumulation_steps ) __magic_name__ : int =int(args.local_sgd_steps ) # Initialize accelerator __magic_name__ : List[str] =Accelerator( cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowerCamelCase ) if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]: raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" ) # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs __magic_name__ : List[str] =config["""lr"""] __magic_name__ : Dict =int(config["""num_epochs"""] ) __magic_name__ : Optional[int] =int(config["""seed"""] ) __magic_name__ : Tuple =int(config["""batch_size"""] ) __magic_name__ : Dict =evaluate.load("""glue""" , """mrpc""" ) set_seed(lowerCamelCase ) __magic_name__ , __magic_name__ : Union[str, Any] =get_dataloaders(lowerCamelCase , lowerCamelCase ) # Instantiate the model (we build the model here so that the seed also control new weights initialization) __magic_name__ : str =AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCamelCase ) # We could avoid this line since the accelerator is set with `device_placement=True` (default value). # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that). __magic_name__ : List[str] =model.to(accelerator.device ) # Instantiate optimizer __magic_name__ : Tuple =AdamW(params=model.parameters() , lr=lowerCamelCase ) # Instantiate scheduler __magic_name__ : Dict =get_linear_schedule_with_warmup( optimizer=lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(lowerCamelCase ) * num_epochs) , ) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =accelerator.prepare( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) # Now we train the model for epoch in range(lowerCamelCase ): model.train() with LocalSGD( accelerator=lowerCamelCase , model=lowerCamelCase , local_sgd_steps=lowerCamelCase , enabled=local_sgd_steps is not None ) as local_sgd: for step, batch in enumerate(lowerCamelCase ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) # New code # # We use the new `accumulate` context manager to perform gradient accumulation # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests. with accelerator.accumulate(lowerCamelCase ): __magic_name__ : Tuple =model(**lowerCamelCase ) __magic_name__ : Optional[int] =output.loss accelerator.backward(lowerCamelCase ) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # LocalSGD-specific line local_sgd.step() model.eval() for step, batch in enumerate(lowerCamelCase ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) with torch.no_grad(): __magic_name__ : int =model(**lowerCamelCase ) __magic_name__ : int =outputs.logits.argmax(dim=-1 ) __magic_name__ , __magic_name__ : Optional[int] =accelerator.gather_for_metrics((predictions, batch["""labels"""]) ) metric.add_batch( predictions=lowerCamelCase , references=lowerCamelCase , ) __magic_name__ : Tuple =metric.compute() # Use accelerator.print to print only on the main process. accelerator.print(F"epoch {epoch}:" , lowerCamelCase ) def lowerCAmelCase_ ( ): __magic_name__ : List[str] =argparse.ArgumentParser(description="""Simple example of training script.""" ) parser.add_argument( """--mixed_precision""" , type=lowerCamelCase , default=lowerCamelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose""" """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.""" """and an Nvidia Ampere GPU.""" , ) # New Code # parser.add_argument( """--gradient_accumulation_steps""" , type=lowerCamelCase , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , ) parser.add_argument( """--local_sgd_steps""" , type=lowerCamelCase , default=8 , help="""Number of local SGD steps or None to disable local SGD""" ) parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" ) __magic_name__ : Tuple =parser.parse_args() __magic_name__ : Any ={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16} training_function(lowerCamelCase , lowerCamelCase ) if __name__ == "__main__": main()
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UpperCAmelCase_ : int = range(2, 20 + 1) UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)] UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) ) __magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) ) __magic_name__ , __magic_name__ : Tuple =0, 0 __magic_name__ : Optional[Any] =n - i __magic_name__ : Union[str, Any] =memo.get(lowerCamelCase ) if sub_memo is not None: __magic_name__ : int =sub_memo.get(lowerCamelCase ) if jumps is not None and len(lowerCamelCase ) > 0: # find and make the largest jump without going over __magic_name__ : Dict =-1 for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ): if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: __magic_name__ : Optional[Any] =_k break if max_jump >= 0: __magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump] # since the difference between jumps is cached, add c __magic_name__ : Tuple =diff + c for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ): __magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 ) if new_c > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) else: __magic_name__ : str =[] else: __magic_name__ : List[str] ={c: []} __magic_name__ : List[str] =sub_memo if dn >= max_dn or c + diff >= base[k]: return diff, dn if k > ks[0]: while True: # keep doing smaller jumps __magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped if dn >= max_dn or c + diff >= base[k]: break else: # would be too small a jump, just compute sequential terms instead __magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped __magic_name__ : Tuple =sub_memo[c] # keep jumps sorted by # of terms skipped __magic_name__ : List[Any] =0 while j < len(lowerCamelCase ): if jumps[j][1] > dn: break j += 1 # cache the jump for this value digitsum(b) and c sub_memo[c].insert(lowerCamelCase , (diff, dn, k) ) return (diff, dn) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): if i >= n: return 0, i if k > len(lowerCamelCase ): a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] ) # note: a_i -> b * 10^k + c # ds_b -> digitsum(b) # ds_c -> digitsum(c) __magic_name__ : Tuple =i __magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0 for j in range(len(lowerCamelCase ) ): if j >= k: ds_b += a_i[j] else: ds_c += a_i[j] while i < n: i += 1 __magic_name__ : Optional[Any] =ds_c + ds_b diff += addend __magic_name__ : str =0 for j in range(lowerCamelCase ): __magic_name__ : int =a_i[j] + addend __magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 ) ds_c += a_i[j] if addend > 0: break if addend > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) return diff, i - start_i def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): for j in range(lowerCamelCase , len(lowerCamelCase ) ): __magic_name__ : Tuple =digits[j] + addend if s >= 10: __magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 ) __magic_name__ : int =addend // 10 + quotient else: __magic_name__ : Dict =s __magic_name__ : Any =addend // 10 if addend == 0: break while addend > 0: __magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 ) digits.append(lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase = 10**15 ): __magic_name__ : List[str] =[1] __magic_name__ : str =1 __magic_name__ : str =0 while True: __magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase ) dn += terms_jumped if dn == n - i: break __magic_name__ : int =0 for j in range(len(lowerCamelCase ) ): a_n += digits[j] * 10**j return a_n if __name__ == "__main__": print(F"""{solution() = }""")
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1
UpperCAmelCase_ : Tuple = 0 # The first color of the flag. UpperCAmelCase_ : Any = 1 # The second color of the flag. UpperCAmelCase_ : str = 2 # The third color of the flag. UpperCAmelCase_ : Tuple = (red, white, blue) def lowerCAmelCase_ ( lowerCamelCase ): if not sequence: return [] if len(lowerCamelCase ) == 1: return list(lowerCamelCase ) __magic_name__ : int =0 __magic_name__ : str =len(lowerCamelCase ) - 1 __magic_name__ : Optional[Any] =0 while mid <= high: if sequence[mid] == colors[0]: __magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low] low += 1 mid += 1 elif sequence[mid] == colors[1]: mid += 1 elif sequence[mid] == colors[2]: __magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid] high -= 1 else: __magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values" raise ValueError(lowerCamelCase ) return sequence if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip() UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")] print(F"""{dutch_national_flag_sort(unsorted)}""")
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from typing import List from .keymap import KEYMAP, get_character def lowerCAmelCase_ ( lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] ) handle += [key] setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator def lowerCAmelCase_ ( *lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] ) handle += keys setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator class __A ( UpperCamelCase__ ): def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ): '''simple docstring''' __magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case ) if not hasattr(__snake_case , """key_handler""" ): setattr(__snake_case , """key_handler""" , {} ) setattr(__snake_case , """handle_input""" , KeyHandler.handle_input ) for value in attrs.values(): __magic_name__ : int =getattr(__snake_case , """handle_key""" , [] ) for key in handled_keys: __magic_name__ : List[str] =value return new_cls @staticmethod def A__ ( cls :Optional[int] ): '''simple docstring''' __magic_name__ : Union[str, Any] =get_character() if char != KEYMAP["undefined"]: __magic_name__ : Optional[int] =ord(__snake_case ) __magic_name__ : int =cls.key_handler.get(__snake_case ) if handler: __magic_name__ : Dict =char return handler(cls ) else: return None def lowerCAmelCase_ ( cls ): return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
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1
import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( MobileViTConfig, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTImageProcessor, ) from transformers.utils import logging logging.set_verbosity_info() UpperCAmelCase_ : List[str] = logging.get_logger(__name__) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Tuple =MobileViTConfig() # size of the architecture if "mobilevit_s" in mobilevit_name: __magic_name__ : Any =[144, 192, 240] __magic_name__ : Union[str, Any] =[16, 32, 64, 96, 128, 160, 640] elif "mobilevit_xs" in mobilevit_name: __magic_name__ : Dict =[96, 120, 144] __magic_name__ : str =[16, 32, 48, 64, 80, 96, 384] elif "mobilevit_xxs" in mobilevit_name: __magic_name__ : List[str] =[64, 80, 96] __magic_name__ : List[str] =[16, 16, 24, 48, 64, 80, 320] __magic_name__ : Optional[int] =0.0_5 __magic_name__ : int =2.0 if mobilevit_name.startswith("""deeplabv3_""" ): __magic_name__ : int =512 __magic_name__ : Dict =16 __magic_name__ : Union[str, Any] =21 __magic_name__ : int ="""pascal-voc-id2label.json""" else: __magic_name__ : Union[str, Any] =1000 __magic_name__ : List[Any] ="""imagenet-1k-id2label.json""" __magic_name__ : List[Any] ="""huggingface/label-files""" __magic_name__ : Dict =json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) ) __magic_name__ : Any ={int(lowerCamelCase ): v for k, v in idalabel.items()} __magic_name__ : int =idalabel __magic_name__ : Any ={v: k for k, v in idalabel.items()} return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): for i in range(1 , 6 ): if F"layer_{i}." in name: __magic_name__ : Dict =name.replace(F"layer_{i}." , F"encoder.layer.{i - 1}." ) if "conv_1." in name: __magic_name__ : List[Any] =name.replace("""conv_1.""" , """conv_stem.""" ) if ".block." in name: __magic_name__ : int =name.replace(""".block.""" , """.""" ) if "exp_1x1" in name: __magic_name__ : str =name.replace("""exp_1x1""" , """expand_1x1""" ) if "red_1x1" in name: __magic_name__ : Dict =name.replace("""red_1x1""" , """reduce_1x1""" ) if ".local_rep.conv_3x3." in name: __magic_name__ : List[str] =name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" ) if ".local_rep.conv_1x1." in name: __magic_name__ : Optional[Any] =name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" ) if ".norm." in name: __magic_name__ : Tuple =name.replace(""".norm.""" , """.normalization.""" ) if ".conv." in name: __magic_name__ : Any =name.replace(""".conv.""" , """.convolution.""" ) if ".conv_proj." in name: __magic_name__ : Optional[Any] =name.replace(""".conv_proj.""" , """.conv_projection.""" ) for i in range(0 , 2 ): for j in range(0 , 4 ): if F".{i}.{j}." in name: __magic_name__ : int =name.replace(F".{i}.{j}." , F".{i}.layer.{j}." ) for i in range(2 , 6 ): for j in range(0 , 4 ): if F".{i}.{j}." in name: __magic_name__ : Dict =name.replace(F".{i}.{j}." , F".{i}." ) if "expand_1x1" in name: __magic_name__ : Optional[int] =name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" ) if "conv_3x3" in name: __magic_name__ : Optional[int] =name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" ) if "reduce_1x1" in name: __magic_name__ : Any =name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" ) for i in range(2 , 5 ): if F".global_rep.{i}.weight" in name: __magic_name__ : Dict =name.replace(F".global_rep.{i}.weight" , """.layernorm.weight""" ) if F".global_rep.{i}.bias" in name: __magic_name__ : Tuple =name.replace(F".global_rep.{i}.bias" , """.layernorm.bias""" ) if ".global_rep." in name: __magic_name__ : List[str] =name.replace(""".global_rep.""" , """.transformer.""" ) if ".pre_norm_mha.0." in name: __magic_name__ : int =name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" ) if ".pre_norm_mha.1.out_proj." in name: __magic_name__ : Optional[Any] =name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" ) if ".pre_norm_ffn.0." in name: __magic_name__ : List[str] =name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" ) if ".pre_norm_ffn.1." in name: __magic_name__ : Any =name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" ) if ".pre_norm_ffn.4." in name: __magic_name__ : Optional[int] =name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" ) if ".transformer." in name: __magic_name__ : int =name.replace(""".transformer.""" , """.transformer.layer.""" ) if ".aspp_layer." in name: __magic_name__ : List[Any] =name.replace(""".aspp_layer.""" , """.""" ) if ".aspp_pool." in name: __magic_name__ : List[Any] =name.replace(""".aspp_pool.""" , """.""" ) if "seg_head." in name: __magic_name__ : Tuple =name.replace("""seg_head.""" , """segmentation_head.""" ) if "segmentation_head.classifier.classifier." in name: __magic_name__ : Dict =name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" ) if "classifier.fc." in name: __magic_name__ : Tuple =name.replace("""classifier.fc.""" , """classifier.""" ) elif (not base_model) and ("segmentation_head." not in name): __magic_name__ : Any ="""mobilevit.""" + name return name def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=False ): if base_model: __magic_name__ : Any ="""""" else: __magic_name__ : Tuple ="""mobilevit.""" for key in orig_state_dict.copy().keys(): __magic_name__ : Dict =orig_state_dict.pop(lowerCamelCase ) if key[:8] == "encoder.": __magic_name__ : Optional[int] =key[8:] if "qkv" in key: __magic_name__ : Tuple =key.split(""".""" ) __magic_name__ : Dict =int(key_split[0][6:] ) - 1 __magic_name__ : Dict =int(key_split[3] ) __magic_name__ : Optional[int] =model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}" ) __magic_name__ : List[Any] =layer.transformer.layer[transformer_num].attention.attention.all_head_size __magic_name__ : Tuple =( F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention." ) if "weight" in key: __magic_name__ : Optional[int] =val[:dim, :] __magic_name__ : Tuple =val[dim : dim * 2, :] __magic_name__ : List[str] =val[-dim:, :] else: __magic_name__ : Tuple =val[:dim] __magic_name__ : str =val[dim : dim * 2] __magic_name__ : int =val[-dim:] else: __magic_name__ : Optional[int] =val return orig_state_dict def lowerCAmelCase_ ( ): __magic_name__ : List[Any] ="""http://images.cocodataset.org/val2017/000000039769.jpg""" __magic_name__ : str =Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw ) return im @torch.no_grad() def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =get_mobilevit_config(lowerCamelCase ) # load original state_dict __magic_name__ : List[Any] =torch.load(lowerCamelCase , map_location="""cpu""" ) # load 🤗 model if mobilevit_name.startswith("""deeplabv3_""" ): __magic_name__ : Union[str, Any] =MobileViTForSemanticSegmentation(lowerCamelCase ).eval() else: __magic_name__ : Tuple =MobileViTForImageClassification(lowerCamelCase ).eval() __magic_name__ : Dict =convert_state_dict(lowerCamelCase , lowerCamelCase ) model.load_state_dict(lowerCamelCase ) # Check outputs on an image, prepared by MobileViTImageProcessor __magic_name__ : Any =MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 ) __magic_name__ : Optional[int] =image_processor(images=prepare_img() , return_tensors="""pt""" ) __magic_name__ : List[Any] =model(**lowerCamelCase ) __magic_name__ : Union[str, Any] =outputs.logits if mobilevit_name.startswith("""deeplabv3_""" ): assert logits.shape == (1, 21, 32, 32) if mobilevit_name == "deeplabv3_mobilevit_s": __magic_name__ : Any =torch.tensor( [ [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]], [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]], [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]], ] ) elif mobilevit_name == "deeplabv3_mobilevit_xs": __magic_name__ : Dict =torch.tensor( [ [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]], [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]], [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]], ] ) elif mobilevit_name == "deeplabv3_mobilevit_xxs": __magic_name__ : Dict =torch.tensor( [ [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]], [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]], [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]], ] ) else: raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" ) assert torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1E-4 ) else: assert logits.shape == (1, 1000) if mobilevit_name == "mobilevit_s": __magic_name__ : List[str] =torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1] ) elif mobilevit_name == "mobilevit_xs": __magic_name__ : Dict =torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7] ) elif mobilevit_name == "mobilevit_xxs": __magic_name__ : Optional[Any] =torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] ) else: raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" ) assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4 ) Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase ) print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}" ) model.save_pretrained(lowerCamelCase ) print(F"Saving image processor to {pytorch_dump_folder_path}" ) image_processor.save_pretrained(lowerCamelCase ) if push_to_hub: __magic_name__ : List[Any] ={ """mobilevit_s""": """mobilevit-small""", """mobilevit_xs""": """mobilevit-x-small""", """mobilevit_xxs""": """mobilevit-xx-small""", """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""", """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""", """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""", } print("""Pushing to the hub...""" ) __magic_name__ : str =model_mapping[mobilevit_name] image_processor.push_to_hub(lowerCamelCase , organization="""apple""" ) model.push_to_hub(lowerCamelCase , organization="""apple""" ) if __name__ == "__main__": UpperCAmelCase_ : Tuple = argparse.ArgumentParser() # Required parameters parser.add_argument( "--mobilevit_name", default="mobilevit_s", type=str, help=( "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs'," " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'." ), ) parser.add_argument( "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)." ) parser.add_argument( "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) UpperCAmelCase_ : int = parser.parse_args() convert_movilevit_checkpoint( args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub )
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import os import jsonlines import numpy as np from tqdm import tqdm UpperCAmelCase_ : Dict = 2048 UpperCAmelCase_ : int = 4096 UpperCAmelCase_ : Any = 42 UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false") UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4} def lowerCAmelCase_ ( lowerCamelCase ): def choose_first(lowerCamelCase , lowerCamelCase=False ): assert isinstance(lowerCamelCase , lowerCamelCase ) if len(lowerCamelCase ) == 1: __magic_name__ : List[str] =answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: __magic_name__ : Tuple ={k: [a[k]] for k in a} if len(a["""start_token"""] ) > 0: break return a __magic_name__ : str ={"""id""": example["""id"""]} __magic_name__ : List[Any] =example["""annotations"""] __magic_name__ : List[str] =annotation["""yes_no_answer"""] if 0 in yes_no_answer or 1 in yes_no_answer: __magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""] __magic_name__ : List[str] =[] __magic_name__ : Dict =[] __magic_name__ : str =["""<cls>"""] else: __magic_name__ : Tuple =["""short"""] __magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] ) if len(out["""start_token"""] ) == 0: # answer will be long if short is not available __magic_name__ : Tuple =["""long"""] __magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase ) __magic_name__ : List[Any] =[] answer.update(lowerCamelCase ) # disregard some samples if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]: __magic_name__ : Any =True else: __magic_name__ : List[str] =False __magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""] if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ): raise ValueError("""Issue in ID""" , example["""id"""] ) return answer def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase ) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : Any =example["""document"""]["""tokens"""] __magic_name__ : str =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": -100, # ignore index in cross-entropy "end_token": -100, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples __magic_name__ : Dict =["""start_token""", """end_token"""] answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10 __magic_name__ : Tuple =example["""document"""]["""tokens"""] __magic_name__ : Optional[int] =answer["""start_token"""] __magic_name__ : List[Any] =answer["""end_token"""] __magic_name__ : Optional[Any] =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 __magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] ) # checking above code if assertion: __magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] ) if new != old: print("""ID:""" , example["""id"""] ) print("""New:""" , lowerCamelCase , end="""\n""" ) print("""Old:""" , lowerCamelCase , end="""\n\n""" ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ): # overlap will be of doc_stride - q_len __magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase ) __magic_name__ : Union[str, Any] =out["""answer"""] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } __magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids __magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : List[str] =[] __magic_name__ : int =[] __magic_name__ : List[str] =input_ids[:q_len] __magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Tuple =input_ids[i:end_index] inputs.append(q_indices + slice ) category.append(answer["""category"""][0] ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-100] * len(lowerCamelCase ), "end_token": [-100] * len(lowerCamelCase ), "category": category, }, } __magic_name__ : int =out["""context"""].split() __magic_name__ : Any =splitted_context[answer["""end_token"""]] __magic_name__ : str =len( tokenizer( """ """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids ) __magic_name__ : Optional[int] =len( tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids ) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token __magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids ) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 __magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive __magic_name__ : Dict =answer["""start_token"""] __magic_name__ : int =answer["""end_token"""] if assertion: __magic_name__ : Any =tokenizer.decode(lowerCamelCase ) if answer["span"] != new: print("""ISSUE IN TOKENIZATION""" ) print("""OLD:""" , answer["""span"""] ) print("""NEW:""" , lowerCamelCase , end="""\n\n""" ) if len(lowerCamelCase ) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } __magic_name__ : Any =input_ids[:q_len] __magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) __magic_name__ : Any =[] __magic_name__ : List[str] =[] __magic_name__ : List[str] =[] __magic_name__ : str =[] # null, yes, no, long, short for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Dict =input_ids[i:end_index] inputs.append(q_indices + slice ) assert len(inputs[-1] ) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: __magic_name__ : List[Any] =start_token - i + q_len __magic_name__ : Optional[Any] =end_token - i + q_len answers_category.append(answer["""category"""][0] ) # ["short"] -> "short" else: __magic_name__ : Optional[Any] =-100 __magic_name__ : Optional[Any] =-100 answers_category.append("""null""" ) __magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1] answers_start_token.append(lowerCamelCase ) answers_end_token.append(lowerCamelCase ) if assertion: if new != old and new != [tokenizer.cls_token_id]: print("""ISSUE in strided for ID:""" , example["""id"""] ) print("""New:""" , tokenizer.decode(lowerCamelCase ) ) print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ): __magic_name__ : List[Any] =get_strided_contexts_and_ans( lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , ) return example def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): with jsonlines.open(lowerCamelCase , """a""" ) as writer: for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ): __magic_name__ : int =example["""labels"""] for ids, start, end, cat in zip( example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { """input_ids""": ids, """start_token""": start, """end_token""": end, """category""": CATEGORY_MAPPING[cat], } ) if __name__ == "__main__": from datasets import load_dataset from transformers import BigBirdTokenizer UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions") UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"] UpperCAmelCase_ : Optional[int] = { "tokenizer": tokenizer, "doc_stride": DOC_STRIDE, "max_length": MAX_LENGTH, "assertion": False, } UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs) UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"]) print(data) np.random.seed(SEED) UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl" save_to_disk(data, file_name=cache_file_name)
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def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): global f # a global dp table for knapsack if f[i][j] < 0: if j < wt[i - 1]: __magic_name__ : Union[str, Any] =mf_knapsack(i - 1 , lowerCamelCase , lowerCamelCase , lowerCamelCase ) else: __magic_name__ : Tuple =max( mf_knapsack(i - 1 , lowerCamelCase , lowerCamelCase , lowerCamelCase ) , mf_knapsack(i - 1 , lowerCamelCase , lowerCamelCase , j - wt[i - 1] ) + val[i - 1] , ) __magic_name__ : Any =val return f[i][j] def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Dict =[[0] * (w + 1) for _ in range(n + 1 )] for i in range(1 , n + 1 ): for w_ in range(1 , w + 1 ): if wt[i - 1] <= w_: __magic_name__ : Optional[Any] =max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] ) else: __magic_name__ : str =dp[i - 1][w_] return dp[n][w_], dp def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): if not (isinstance(lowerCamelCase , (list, tuple) ) and isinstance(lowerCamelCase , (list, tuple) )): raise ValueError( """Both the weights and values vectors must be either lists or tuples""" ) __magic_name__ : str =len(lowerCamelCase ) if num_items != len(lowerCamelCase ): __magic_name__ : Union[str, Any] =( """The number of weights must be the same as the number of values.\n""" F"But got {num_items} weights and {len(lowerCamelCase )} values" ) raise ValueError(lowerCamelCase ) for i in range(lowerCamelCase ): if not isinstance(wt[i] , lowerCamelCase ): __magic_name__ : List[str] =( """All weights must be integers but got weight of """ F"type {type(wt[i] )} at index {i}" ) raise TypeError(lowerCamelCase ) __magic_name__ , __magic_name__ : List[Any] =knapsack(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) __magic_name__ : set =set() _construct_solution(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) return optimal_val, example_optional_set def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): # for the current item i at a maximum weight j to be part of an optimal subset, # the optimal value at (i, j) must be greater than the optimal value at (i-1, j). # where i - 1 means considering only the previous items at the given maximum weight if i > 0 and j > 0: if dp[i - 1][j] == dp[i][j]: _construct_solution(lowerCamelCase , lowerCamelCase , i - 1 , lowerCamelCase , lowerCamelCase ) else: optimal_set.add(lowerCamelCase ) _construct_solution(lowerCamelCase , lowerCamelCase , i - 1 , j - wt[i - 1] , lowerCamelCase ) if __name__ == "__main__": UpperCAmelCase_ : Dict = [3, 2, 4, 4] UpperCAmelCase_ : str = [4, 3, 2, 3] UpperCAmelCase_ : Any = 4 UpperCAmelCase_ : List[str] = 6 UpperCAmelCase_ : Tuple = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)] UpperCAmelCase_ , UpperCAmelCase_ : Dict = knapsack(w, wt, val, n) print(optimal_solution) print(mf_knapsack(n, wt, val, w)) # switched the n and w # testing the dynamic programming problem with example # the optimal subset for the above example are items 3 and 4 UpperCAmelCase_ , UpperCAmelCase_ : Any = knapsack_with_example_solution(w, wt, val) assert optimal_solution == 8 assert optimal_subset == {3, 4} print("optimal_value = ", optimal_solution) print("An optimal subset corresponding to the optimal value", optimal_subset)
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Tuple = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json", "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json", # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl } class __A ( UpperCamelCase__ ): UpperCamelCase = """xlm-roberta-xl""" def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ): '''simple docstring''' super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case ) __magic_name__ : List[str] =vocab_size __magic_name__ : List[str] =hidden_size __magic_name__ : Union[str, Any] =num_hidden_layers __magic_name__ : Any =num_attention_heads __magic_name__ : Any =hidden_act __magic_name__ : List[str] =intermediate_size __magic_name__ : Any =hidden_dropout_prob __magic_name__ : Union[str, Any] =attention_probs_dropout_prob __magic_name__ : Any =max_position_embeddings __magic_name__ : Any =type_vocab_size __magic_name__ : List[str] =initializer_range __magic_name__ : Optional[int] =layer_norm_eps __magic_name__ : Dict =position_embedding_type __magic_name__ : Any =use_cache __magic_name__ : Dict =classifier_dropout class __A ( UpperCamelCase__ ): @property def A__ ( self :Dict ): '''simple docstring''' if self.task == "multiple-choice": __magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""} else: __magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ] )
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import operator def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None ): __magic_name__ : Any =operator.lt if reverse else operator.gt __magic_name__ : Union[str, Any] =solution or [] if not arr: return solution __magic_name__ : Optional[Any] =[arr.pop(0 )] for i, item in enumerate(lowerCamelCase ): if _operator(lowerCamelCase , sublist[-1] ): sublist.append(lowerCamelCase ) arr.pop(lowerCamelCase ) # merging sublist into solution list if not solution: solution.extend(lowerCamelCase ) else: while sublist: __magic_name__ : List[Any] =sublist.pop(0 ) for i, xx in enumerate(lowerCamelCase ): if not _operator(lowerCamelCase , lowerCamelCase ): solution.insert(lowerCamelCase , lowerCamelCase ) break else: solution.append(lowerCamelCase ) strand_sort(lowerCamelCase , lowerCamelCase , lowerCamelCase ) return solution if __name__ == "__main__": assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5] assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
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from pathlib import Path import fire from tqdm import tqdm def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ): try: import datasets except (ModuleNotFoundError, ImportError): raise ImportError("""run pip install datasets""" ) __magic_name__ : Dict =F"{src_lang}-{tgt_lang}" print(F"Converting {dataset}-{pair}" ) __magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase ) if save_dir is None: __magic_name__ : Optional[int] =F"{dataset}-{pair}" __magic_name__ : int =Path(lowerCamelCase ) save_dir.mkdir(exist_ok=lowerCamelCase ) for split in ds.keys(): print(F"Splitting {split} with {ds[split].num_rows} records" ) # to save to val.source, val.target like summary datasets __magic_name__ : Dict ="""val""" if split == """validation""" else split __magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" ) __magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" ) __magic_name__ : Optional[Any] =src_path.open("""w+""" ) __magic_name__ : List[Any] =tgt_path.open("""w+""" ) # reader is the bottleneck so writing one record at a time doesn't slow things down for x in tqdm(ds[split] ): __magic_name__ : str =x["""translation"""] src_fp.write(ex[src_lang] + """\n""" ) tgt_fp.write(ex[tgt_lang] + """\n""" ) print(F"Saved {dataset} dataset to {save_dir}" ) if __name__ == "__main__": fire.Fire(download_wmt_dataset)
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from typing import List, Optional, Union from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType class __A ( UpperCamelCase__ ): UpperCamelCase = ["""image_processor""", """tokenizer"""] UpperCamelCase = """Pix2StructImageProcessor""" UpperCamelCase = ("""T5Tokenizer""", """T5TokenizerFast""") def __init__( self :Optional[Any] , __snake_case :int , __snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[int] =False super().__init__(__snake_case , __snake_case ) def __call__( self :Any , __snake_case :List[Any]=None , __snake_case :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __snake_case :bool = True , __snake_case :Union[bool, str, PaddingStrategy] = False , __snake_case :Union[bool, str, TruncationStrategy] = None , __snake_case :Optional[int] = None , __snake_case :Optional[int] = 20_48 , __snake_case :int = 0 , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :bool = False , __snake_case :bool = False , __snake_case :bool = False , __snake_case :bool = False , __snake_case :bool = False , __snake_case :bool = True , __snake_case :Optional[Union[str, TensorType]] = None , **__snake_case :Any , ): '''simple docstring''' if images is None and text is None: raise ValueError("""You have to specify either images or text.""" ) # Get only text if images is None and not self.image_processor.is_vqa: __magic_name__ : Any =self.tokenizer __magic_name__ : Dict =self.tokenizer( text=__snake_case , add_special_tokens=__snake_case , padding=__snake_case , truncation=__snake_case , max_length=__snake_case , stride=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , return_overflowing_tokens=__snake_case , return_special_tokens_mask=__snake_case , return_offsets_mapping=__snake_case , return_token_type_ids=__snake_case , return_length=__snake_case , verbose=__snake_case , return_tensors=__snake_case , **__snake_case , ) return text_encoding if not self.image_processor.is_vqa: # add pixel_values __magic_name__ : Dict =self.image_processor( __snake_case , return_tensors=__snake_case , max_patches=__snake_case , **__snake_case ) else: # add pixel_values and bbox __magic_name__ : Optional[int] =self.image_processor( __snake_case , return_tensors=__snake_case , max_patches=__snake_case , header_text=__snake_case , **__snake_case ) if text is not None and not self.image_processor.is_vqa: __magic_name__ : int =self.tokenizer( text=__snake_case , add_special_tokens=__snake_case , padding=__snake_case , truncation=__snake_case , max_length=__snake_case , stride=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , return_overflowing_tokens=__snake_case , return_special_tokens_mask=__snake_case , return_offsets_mapping=__snake_case , return_token_type_ids=__snake_case , return_length=__snake_case , verbose=__snake_case , return_tensors=__snake_case , **__snake_case , ) if "attention_mask" in text_encoding: __magic_name__ : Tuple =text_encoding.pop("""attention_mask""" ) if "input_ids" in text_encoding: __magic_name__ : List[str] =text_encoding.pop("""input_ids""" ) else: __magic_name__ : Any =None if text_encoding is not None: encoding_image_processor.update(__snake_case ) return encoding_image_processor def A__ ( self :List[Any] , *__snake_case :Union[str, Any] , **__snake_case :List[Any] ): '''simple docstring''' return self.tokenizer.batch_decode(*__snake_case , **__snake_case ) def A__ ( self :Optional[Any] , *__snake_case :Optional[int] , **__snake_case :str ): '''simple docstring''' return self.tokenizer.decode(*__snake_case , **__snake_case ) @property def A__ ( self :int ): '''simple docstring''' __magic_name__ : int =self.tokenizer.model_input_names __magic_name__ : Any =self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
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from __future__ import annotations from fractions import Fraction def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): return ( num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =[] __magic_name__ : List[Any] =11 __magic_name__ : Tuple =int("""1""" + """0""" * digit_len ) for num in range(lowerCamelCase , lowerCamelCase ): while den <= 99: if (num != den) and (num % 10 == den // 10) and (den % 10 != 0): if is_digit_cancelling(lowerCamelCase , lowerCamelCase ): solutions.append(F"{num}/{den}" ) den += 1 num += 1 __magic_name__ : List[str] =10 return solutions def lowerCAmelCase_ ( lowerCamelCase = 2 ): __magic_name__ : str =1.0 for fraction in fraction_list(lowerCamelCase ): __magic_name__ : int =Fraction(lowerCamelCase ) result *= frac.denominator / frac.numerator return int(lowerCamelCase ) if __name__ == "__main__": print(solution())
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def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): return numa ^ numa < 0 if __name__ == "__main__": import doctest doctest.testmod()
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from collections.abc import Callable from math import pi, sqrt from random import uniform from statistics import mean def lowerCAmelCase_ ( lowerCamelCase ): # A local function to see if a dot lands in the circle. def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool: __magic_name__ : Dict =sqrt((x**2) + (y**2) ) # Our circle has a radius of 1, so a distance # greater than 1 would land outside the circle. return distance_from_centre <= 1 # The proportion of guesses that landed in the circle __magic_name__ : Union[str, Any] =mean( int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) ) for _ in range(lowerCamelCase ) ) # The ratio of the area for circle to square is pi/4. __magic_name__ : List[Any] =proportion * 4 print(F"The estimated value of pi is {pi_estimate}" ) print(F"The numpy value of pi is {pi}" ) print(F"The total error is {abs(pi - pi_estimate )}" ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ): return mean( function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ): def identity_function(lowerCamelCase ) -> float: return x __magic_name__ : Optional[int] =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) __magic_name__ : str =(max_value * max_value - min_value * min_value) / 2 print("""******************""" ) print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {expected_value}" ) print(F"Total error is {abs(estimated_value - expected_value )}" ) print("""******************""" ) def lowerCAmelCase_ ( lowerCamelCase ): def function_to_integrate(lowerCamelCase ) -> float: return sqrt(4.0 - x * x ) __magic_name__ : Dict =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , 0.0 , 2.0 ) print("""******************""" ) print("""Estimating pi using area_under_curve_estimator""" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {pi}" ) print(F"Total error is {abs(estimated_value - pi )}" ) print("""******************""" ) if __name__ == "__main__": import doctest doctest.testmod()
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import logging from dataclasses import dataclass, field from pathlib import Path from typing import Optional, Union from .generation.configuration_utils import GenerationConfig from .training_args import TrainingArguments from .utils import add_start_docstrings UpperCAmelCase_ : Tuple = logging.getLogger(__name__) @dataclass @add_start_docstrings(TrainingArguments.__doc__ ) class __A ( UpperCamelCase__ ): UpperCamelCase = field(default=UpperCamelCase__ , metadata={"""help""": """Whether to use SortishSampler or not."""} ) UpperCamelCase = field( default=UpperCamelCase__ , metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""} ) UpperCamelCase = field( default=UpperCamelCase__ , metadata={ """help""": ( """The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default """ """to the `max_length` value of the model configuration.""" ) } , ) UpperCamelCase = field( default=UpperCamelCase__ , metadata={ """help""": ( """The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default """ """to the `num_beams` value of the model configuration.""" ) } , ) UpperCamelCase = field( default=UpperCamelCase__ , metadata={ """help""": """Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.""" } , ) def A__ ( self :str ): '''simple docstring''' __magic_name__ : Tuple =super().to_dict() for k, v in d.items(): if isinstance(__snake_case , __snake_case ): __magic_name__ : List[Any] =v.to_dict() return d
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import os from typing import Dict, List, Union import tensorflow as tf from keras_nlp.tokenizers import BytePairTokenizer from tensorflow_text import pad_model_inputs from .tokenization_gpta import GPTaTokenizer class __A ( tf.keras.layers.Layer ): def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ): '''simple docstring''' super().__init__() __magic_name__ : Optional[int] =pad_token_id __magic_name__ : List[Any] =max_length __magic_name__ : Dict =vocab __magic_name__ : int =merges __magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case ) @classmethod def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ): '''simple docstring''' __magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()] __magic_name__ : str =tokenizer.get_vocab() return cls(__snake_case , __snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ): '''simple docstring''' __magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case ) return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Optional[Any] , __snake_case :List[Any] ): '''simple docstring''' return cls(**__snake_case ) def A__ ( self :Union[str, Any] ): '''simple docstring''' return { "vocab": self.vocab, "merges": self.merges, "max_length": self.max_length, "pad_token_id": self.pad_token_id, } def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ): '''simple docstring''' __magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case ) __magic_name__ : Tuple =tf.ones_like(__snake_case ) if self.pad_token_id is not None: # pad the tokens up to max length __magic_name__ : Tuple =max_length if max_length is not None else self.max_length if max_length is not None: __magic_name__ , __magic_name__ : Tuple =pad_model_inputs( __snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id ) return {"attention_mask": attention_mask, "input_ids": input_ids}
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class __A : def __init__( self :Optional[int] , __snake_case :int ): '''simple docstring''' __magic_name__ : Optional[Any] =size __magic_name__ : Union[str, Any] =[0] * size __magic_name__ : Optional[int] =[0] * size @staticmethod def A__ ( __snake_case :int ): '''simple docstring''' return index | (index + 1) @staticmethod def A__ ( __snake_case :int ): '''simple docstring''' return (index & (index + 1)) - 1 def A__ ( self :Optional[Any] , __snake_case :int , __snake_case :int ): '''simple docstring''' __magic_name__ : Optional[int] =value while index < self.size: __magic_name__ : List[Any] =self.get_prev(__snake_case ) + 1 if current_left_border == index: __magic_name__ : str =value else: __magic_name__ : Tuple =max(__snake_case , __snake_case , __snake_case ) __magic_name__ : Tuple =self.get_next(__snake_case ) def A__ ( self :List[Any] , __snake_case :int , __snake_case :int ): '''simple docstring''' right -= 1 # Because of right is exclusive __magic_name__ : Optional[Any] =0 while left <= right: __magic_name__ : int =self.get_prev(__snake_case ) if left <= current_left: __magic_name__ : str =max(__snake_case , self.tree[right] ) __magic_name__ : int =current_left else: __magic_name__ : int =max(__snake_case , self.arr[right] ) right -= 1 return result if __name__ == "__main__": import doctest doctest.testmod()
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import math import tensorflow as tf from packaging import version def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype ) __magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase ) return x * tf.tanh(tf.math.softplus(lowerCamelCase ) ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype ) return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) )) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype ) return x * tf.math.sigmoid(coeff * x ) def lowerCAmelCase_ ( lowerCamelCase ): return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ): __magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase ) return a * tf.math.sigmoid(lowerCamelCase ) if version.parse(tf.version.VERSION) >= version.parse("2.4"): def lowerCAmelCase_ ( lowerCamelCase ): return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase ) UpperCAmelCase_ : List[str] = tf.keras.activations.gelu UpperCAmelCase_ : Dict = approximate_gelu_wrap else: UpperCAmelCase_ : Dict = _gelu UpperCAmelCase_ : str = _gelu_new UpperCAmelCase_ : Any = { "gelu": gelu, "gelu_10": gelu_aa, "gelu_fast": gelu_fast, "gelu_new": gelu_new, "glu": glu, "mish": mish, "quick_gelu": quick_gelu, "relu": tf.keras.activations.relu, "sigmoid": tf.keras.activations.sigmoid, "silu": tf.keras.activations.swish, "swish": tf.keras.activations.swish, "tanh": tf.keras.activations.tanh, } def lowerCAmelCase_ ( lowerCamelCase ): if activation_string in ACTaFN: return ACTaFN[activation_string] else: raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
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from __future__ import annotations def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : str =[] __magic_name__ , __magic_name__ : str =input_list[low:mid], input_list[mid : high + 1] while left and right: result.append((left if left[0] <= right[0] else right).pop(0 ) ) __magic_name__ : int =result + left + right return input_list def lowerCAmelCase_ ( lowerCamelCase ): if len(lowerCamelCase ) <= 1: return input_list __magic_name__ : Any =list(lowerCamelCase ) # iteration for two-way merging __magic_name__ : Optional[Any] =2 while p <= len(lowerCamelCase ): # getting low, high and middle value for merge-sort of single list for i in range(0 , len(lowerCamelCase ) , lowerCamelCase ): __magic_name__ : Union[str, Any] =i __magic_name__ : Union[str, Any] =i + p - 1 __magic_name__ : Dict =(low + high + 1) // 2 __magic_name__ : str =merge(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) # final merge of last two parts if p * 2 >= len(lowerCamelCase ): __magic_name__ : Any =i __magic_name__ : Any =merge(lowerCamelCase , 0 , lowerCamelCase , len(lowerCamelCase ) - 1 ) break p *= 2 return input_list if __name__ == "__main__": UpperCAmelCase_ : Dict = input("Enter numbers separated by a comma:\n").strip() if user_input == "": UpperCAmelCase_ : Optional[Any] = [] else: UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")] print(iter_merge_sort(unsorted))
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from collections.abc import Sequence def lowerCAmelCase_ ( lowerCamelCase = None ): if nums is None or not nums: raise ValueError("""Input sequence should not be empty""" ) __magic_name__ : str =nums[0] for i in range(1 , len(lowerCamelCase ) ): __magic_name__ : Any =nums[i] __magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase ) return ans if __name__ == "__main__": import doctest doctest.testmod() # Try on a sample input from the user UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip()) UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n] print(max_subsequence_sum(array))
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import math from typing import Optional import numpy as np from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : List[Any] = { "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 __A ( UpperCamelCase__ ): UpperCamelCase = """encodec""" def __init__( self :str , __snake_case :Any=[1.5, 3.0, 6.0, 12.0, 24.0] , __snake_case :Dict=2_40_00 , __snake_case :Optional[int]=1 , __snake_case :str=False , __snake_case :Optional[int]=None , __snake_case :int=None , __snake_case :Any=1_28 , __snake_case :Dict=32 , __snake_case :Optional[int]=1 , __snake_case :Union[str, Any]=[8, 5, 4, 2] , __snake_case :Optional[int]="weight_norm" , __snake_case :Dict=7 , __snake_case :str=7 , __snake_case :int=3 , __snake_case :Union[str, Any]=2 , __snake_case :Optional[int]=True , __snake_case :Any="reflect" , __snake_case :List[Any]=2 , __snake_case :Any=2 , __snake_case :Tuple=1.0 , __snake_case :int=10_24 , __snake_case :Optional[int]=None , __snake_case :str=True , **__snake_case :Optional[int] , ): '''simple docstring''' __magic_name__ : Any =target_bandwidths __magic_name__ : Optional[Any] =sampling_rate __magic_name__ : Any =audio_channels __magic_name__ : List[Any] =normalize __magic_name__ : List[str] =chunk_length_s __magic_name__ : Optional[int] =overlap __magic_name__ : Any =hidden_size __magic_name__ : List[Any] =num_filters __magic_name__ : List[str] =num_residual_layers __magic_name__ : Optional[int] =upsampling_ratios __magic_name__ : str =norm_type __magic_name__ : Optional[Any] =kernel_size __magic_name__ : List[str] =last_kernel_size __magic_name__ : List[str] =residual_kernel_size __magic_name__ : str =dilation_growth_rate __magic_name__ : Optional[Any] =use_causal_conv __magic_name__ : int =pad_mode __magic_name__ : Optional[int] =compress __magic_name__ : Any =num_lstm_layers __magic_name__ : Optional[Any] =trim_right_ratio __magic_name__ : Any =codebook_size __magic_name__ : Tuple =codebook_dim if codebook_dim is not None else hidden_size __magic_name__ : List[str] =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__(**__snake_case ) @property def A__ ( self :List[str] ): '''simple docstring''' if self.chunk_length_s is None: return None else: return int(self.chunk_length_s * self.sampling_rate ) @property def A__ ( self :List[str] ): '''simple docstring''' 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 A__ ( self :int ): '''simple docstring''' __magic_name__ : Any =np.prod(self.upsampling_ratios ) return math.ceil(self.sampling_rate / hop_length ) @property def A__ ( self :Tuple ): '''simple docstring''' return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
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from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union import pyarrow as pa if TYPE_CHECKING: from .features import FeatureType @dataclass class __A : UpperCamelCase = 42 UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def __call__( self :Union[str, Any] ): '''simple docstring''' return pa.struct({lang: pa.string() for lang in sorted(self.languages )} ) def A__ ( self :List[Any] ): '''simple docstring''' from .features import Value return {k: Value("""string""" ) for k in sorted(self.languages )} @dataclass class __A : UpperCamelCase = None UpperCamelCase = None UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None __magic_name__ : Optional[int] =len(self.languages ) if self.languages else None def __call__( self :List[str] ): '''simple docstring''' return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} ) def A__ ( self :str , __snake_case :str ): '''simple docstring''' __magic_name__ : Optional[int] =set(self.languages ) if self.languages and set(__snake_case ) - lang_set: raise ValueError( f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." ) # Convert dictionary into tuples, splitting out cases where there are # multiple translations for a single language. __magic_name__ : Any =[] for lang, text in translation_dict.items(): if isinstance(__snake_case , __snake_case ): translation_tuples.append((lang, text) ) else: translation_tuples.extend([(lang, el) for el in text] ) # Ensure translations are in ascending order by language code. __magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) ) return {"language": languages, "translation": translations} def A__ ( self :List[Any] ): '''simple docstring''' from .features import Sequence, Value return { "language": Sequence(Value("""string""" ) ), "translation": Sequence(Value("""string""" ) ), }
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import heapq def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : list[list] =[] # 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(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] ) # chosen_vertices = set of chosen vertices __magic_name__ : Tuple =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 __magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0] chosen_vertices.add(lowerCamelCase ) # 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]: __magic_name__ : Tuple =elem[1][1].index(lowerCamelCase ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(lowerCamelCase ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : Optional[int] = {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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from sklearn.metrics import matthews_corrcoef import datasets UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n" UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n" UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n" @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class __A ( datasets.Metric ): def A__ ( self :List[str] ): '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""int32""" ), """references""": datasets.Value("""int32""" ), } ) , reference_urls=[ """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html""" ] , ) def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ): '''simple docstring''' return { "matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ), }
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from abc import ABC, abstractmethod from argparse import ArgumentParser class __A ( UpperCamelCase__ ): @staticmethod @abstractmethod def A__ ( __snake_case :ArgumentParser ): '''simple docstring''' raise NotImplementedError() @abstractmethod def A__ ( self :List[str] ): '''simple docstring''' raise NotImplementedError()
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import importlib import torch import yaml from omegaconf import OmegaConf from taming.models.vqgan import VQModel def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase ) if display: print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) ) return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ): if conf_path is None: __magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml""" __magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase ) __magic_name__ : Tuple =VQModel(**config.model.params ) if ckpt_path is None: __magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt""" __magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase ) if ".ckpt" in ckpt_path: __magic_name__ : Any =sd["""state_dict"""] model.load_state_dict(lowerCamelCase , strict=lowerCamelCase ) model.to(lowerCamelCase ) del sd return model def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase ) print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" ) __magic_name__ : List[Any] =model.decode(lowerCamelCase ) return xrec def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 ) if reload: __magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase ) importlib.reload(lowerCamelCase ) return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls ) def lowerCAmelCase_ ( lowerCamelCase ): if "target" not in config: raise KeyError("""Expected key `target` to instantiate.""" ) return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ): __magic_name__ : str =instantiate_from_config(lowerCamelCase ) if sd is not None: model.load_state_dict(lowerCamelCase ) if gpu: model.cuda() if eval_mode: model.eval() return {"model": model} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): # load the specified checkpoint if ckpt: __magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" ) __magic_name__ : Any =pl_sd["""global_step"""] print(F"loaded model from global step {global_step}." ) else: __magic_name__ : List[Any] ={"""state_dict""": None} __magic_name__ : Optional[Any] =None __magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""] return model, global_step
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import sys import webbrowser import requests from bsa import BeautifulSoup from fake_useragent import UserAgent if __name__ == "__main__": print("Googling.....") UpperCAmelCase_ : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:]) UpperCAmelCase_ : int = requests.get(url, headers={"UserAgent": UserAgent().random}) # res.raise_for_status() with open("project1a.html", "wb") as out_file: # only for knowing the class for data in res.iter_content(10000): out_file.write(data) UpperCAmelCase_ : Optional[Any] = BeautifulSoup(res.text, "html.parser") UpperCAmelCase_ : Optional[Any] = list(soup.select(".eZt8xd"))[:5] print(len(links)) for link in links: if link.text == "Maps": webbrowser.open(link.get("href")) else: webbrowser.open(F"""https://google.com{link.get("href")}""")
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class __A ( unittest.TestCase ): def A__ ( self :Tuple ): '''simple docstring''' debug_launcher(test_script.main ) def A__ ( self :Dict ): '''simple docstring''' debug_launcher(test_ops.main )
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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, ) UpperCAmelCase_ : Tuple = {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : str = [ "OPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OPTForCausalLM", "OPTModel", "OPTPreTrainedModel", "OPTForSequenceClassification", "OPTForQuestionAnswering", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Dict = ["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Any = [ "FlaxOPTForCausalLM", "FlaxOPTModel", "FlaxOPTPreTrainedModel", ] if TYPE_CHECKING: from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_opt import ( OPT_PRETRAINED_MODEL_ARCHIVE_LIST, OPTForCausalLM, OPTForQuestionAnswering, OPTForSequenceClassification, OPTModel, OPTPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel else: import sys UpperCAmelCase_ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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UpperCAmelCase_ : Tuple = 0 # The first color of the flag. UpperCAmelCase_ : Any = 1 # The second color of the flag. UpperCAmelCase_ : str = 2 # The third color of the flag. UpperCAmelCase_ : Tuple = (red, white, blue) def lowerCAmelCase_ ( lowerCamelCase ): if not sequence: return [] if len(lowerCamelCase ) == 1: return list(lowerCamelCase ) __magic_name__ : int =0 __magic_name__ : str =len(lowerCamelCase ) - 1 __magic_name__ : Optional[Any] =0 while mid <= high: if sequence[mid] == colors[0]: __magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low] low += 1 mid += 1 elif sequence[mid] == colors[1]: mid += 1 elif sequence[mid] == colors[2]: __magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid] high -= 1 else: __magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values" raise ValueError(lowerCamelCase ) return sequence if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip() UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")] print(F"""{dutch_national_flag_sort(unsorted)}""")
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def lowerCAmelCase_ ( lowerCamelCase = 200 ): __magic_name__ : Tuple =[1, 2, 5, 10, 20, 50, 100, 200] __magic_name__ : Optional[int] =[0] * (pence + 1) __magic_name__ : int =1 # base case: 1 way to make 0 pence for coin in coins: for i in range(lowerCamelCase , pence + 1 , 1 ): number_of_ways[i] += number_of_ways[i - coin] return number_of_ways[pence] if __name__ == "__main__": assert solution(200) == 73682
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class __A ( UpperCamelCase__ , UpperCamelCase__ ): UpperCamelCase = 1 @register_to_config def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ): '''simple docstring''' __magic_name__ : Dict =None __magic_name__ : List[str] =None __magic_name__ : str =None def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ): '''simple docstring''' __magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case ) def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ): '''simple docstring''' if self.timesteps is None: raise ValueError( """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score __magic_name__ : int =( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) __magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) __magic_name__ : str =std.flatten() while len(std.shape ) < len(score.shape ): __magic_name__ : List[str] =std.unsqueeze(-1 ) __magic_name__ : Union[str, Any] =-score / std # compute __magic_name__ : Tuple =-1.0 / len(self.timesteps ) __magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) __magic_name__ : Dict =beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): __magic_name__ : Any =beta_t.unsqueeze(-1 ) __magic_name__ : Dict =-0.5 * beta_t * x __magic_name__ : Optional[int] =torch.sqrt(__snake_case ) __magic_name__ : int =drift - diffusion**2 * score __magic_name__ : List[str] =x + drift * dt # add noise __magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype ) __magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__( self :List[Any] ): '''simple docstring''' return self.config.num_train_timesteps
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import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_nllb import NllbTokenizer else: UpperCAmelCase_ : str = None UpperCAmelCase_ : List[str] = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} UpperCAmelCase_ : List[str] = { "vocab_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model" ), }, "tokenizer_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json" ), }, } UpperCAmelCase_ : Dict = { "facebook/nllb-large-en-ro": 1024, "facebook/nllb-200-distilled-600M": 1024, } # fmt: off UpperCAmelCase_ : Any = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"] class __A ( UpperCamelCase__ ): UpperCamelCase = VOCAB_FILES_NAMES UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase = ["""input_ids""", """attention_mask"""] UpperCamelCase = NllbTokenizer UpperCamelCase = [] UpperCamelCase = [] def __init__( self :Any , __snake_case :Union[str, Any]=None , __snake_case :Optional[int]=None , __snake_case :str="<s>" , __snake_case :Union[str, Any]="</s>" , __snake_case :Optional[int]="</s>" , __snake_case :Optional[Any]="<s>" , __snake_case :List[str]="<unk>" , __snake_case :List[str]="<pad>" , __snake_case :List[Any]="<mask>" , __snake_case :List[Any]=None , __snake_case :Any=None , __snake_case :Optional[Any]=None , __snake_case :Optional[Any]=False , **__snake_case :Optional[Any] , ): '''simple docstring''' __magic_name__ : Any =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token __magic_name__ : Any =legacy_behaviour super().__init__( vocab_file=__snake_case , tokenizer_file=__snake_case , bos_token=__snake_case , eos_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , unk_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , src_lang=__snake_case , tgt_lang=__snake_case , additional_special_tokens=__snake_case , legacy_behaviour=__snake_case , **__snake_case , ) __magic_name__ : Optional[int] =vocab_file __magic_name__ : List[str] =False if not self.vocab_file else True __magic_name__ : str =FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) __magic_name__ : Union[str, Any] ={ lang_code: self.convert_tokens_to_ids(__snake_case ) for lang_code in FAIRSEQ_LANGUAGE_CODES } __magic_name__ : Optional[Any] =src_lang if src_lang is not None else """eng_Latn""" __magic_name__ : Union[str, Any] =self.convert_tokens_to_ids(self._src_lang ) __magic_name__ : Any =tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def A__ ( self :int ): '''simple docstring''' return self._src_lang @src_lang.setter def A__ ( self :str , __snake_case :str ): '''simple docstring''' __magic_name__ : str =new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def A__ ( self :List[Any] , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ): '''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 A__ ( self :str , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ): '''simple docstring''' __magic_name__ : Union[str, Any] =[self.sep_token_id] __magic_name__ : List[str] =[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 A__ ( self :List[Any] , __snake_case :int , __snake_case :str , __snake_case :Optional[str] , __snake_case :Optional[str] , **__snake_case :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""" ) __magic_name__ : str =src_lang __magic_name__ : Any =self(__snake_case , add_special_tokens=__snake_case , return_tensors=__snake_case , **__snake_case ) __magic_name__ : Union[str, Any] =self.convert_tokens_to_ids(__snake_case ) __magic_name__ : Tuple =tgt_lang_id return inputs def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :str = "eng_Latn" , __snake_case :Optional[List[str]] = None , __snake_case :str = "fra_Latn" , **__snake_case :List[Any] , ): '''simple docstring''' __magic_name__ : Union[str, Any] =src_lang __magic_name__ : List[Any] =tgt_lang return super().prepare_seqaseq_batch(__snake_case , __snake_case , **__snake_case ) def A__ ( self :str ): '''simple docstring''' return self.set_src_lang_special_tokens(self.src_lang ) def A__ ( self :Optional[int] ): '''simple docstring''' return self.set_tgt_lang_special_tokens(self.tgt_lang ) def A__ ( self :Tuple , __snake_case :Any ): '''simple docstring''' __magic_name__ : Dict =self.convert_tokens_to_ids(__snake_case ) if self.legacy_behaviour: __magic_name__ : Any =[] __magic_name__ : str =[self.eos_token_id, self.cur_lang_code] else: __magic_name__ : Optional[int] =[self.cur_lang_code] __magic_name__ : Tuple =[self.eos_token_id] __magic_name__ : int =self.convert_ids_to_tokens(self.prefix_tokens ) __magic_name__ : Dict =self.convert_ids_to_tokens(self.suffix_tokens ) __magic_name__ : Union[str, Any] =processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def A__ ( self :Optional[int] , __snake_case :str ): '''simple docstring''' __magic_name__ : Optional[Any] =self.convert_tokens_to_ids(__snake_case ) if self.legacy_behaviour: __magic_name__ : Any =[] __magic_name__ : Optional[Any] =[self.eos_token_id, self.cur_lang_code] else: __magic_name__ : List[Any] =[self.cur_lang_code] __magic_name__ : Dict =[self.eos_token_id] __magic_name__ : Dict =self.convert_ids_to_tokens(self.prefix_tokens ) __magic_name__ : List[str] =self.convert_ids_to_tokens(self.suffix_tokens ) __magic_name__ : Optional[Any] =processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def A__ ( self :Union[str, Any] , __snake_case :str , __snake_case :Optional[str] = None ): '''simple docstring''' if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(__snake_case ): logger.error(f"Vocabulary path ({save_directory}) should be a directory." ) return __magic_name__ : str =os.path.join( __snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ): copyfile(self.vocab_file , __snake_case ) return (out_vocab_file,)
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from typing import Dict, List, Optional, Union import numpy as np from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy UpperCAmelCase_ : Dict = logging.get_logger(__name__) class __A ( UpperCamelCase__ ): def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =feature_size __magic_name__ : Union[str, Any] =sampling_rate __magic_name__ : List[Any] =padding_value __magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" ) __magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case ) super().__init__(**__snake_case ) def A__ ( self :Any , __snake_case :Union[ BatchFeature, List[BatchFeature], Dict[str, BatchFeature], Dict[str, List[BatchFeature]], List[Dict[str, BatchFeature]], ] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ): '''simple docstring''' if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ): __magic_name__ : Union[str, Any] ={ key: [example[key] for example in processed_features] for key in processed_features[0].keys() } # The model's main input name, usually `input_values`, has be passed for padding if self.model_input_names[0] not in processed_features: raise ValueError( """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`""" f" to this method that includes {self.model_input_names[0]}, but you provided" f" {list(processed_features.keys() )}" ) __magic_name__ : int =processed_features[self.model_input_names[0]] __magic_name__ : Union[str, Any] =( return_attention_mask if return_attention_mask is not None else self.return_attention_mask ) if len(__snake_case ) == 0: if return_attention_mask: __magic_name__ : List[str] =[] return processed_features # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch __magic_name__ : Optional[int] =required_input[0] if isinstance(__snake_case , (list, tuple) ): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. __magic_name__ : Optional[Any] =0 while len(required_input[index] ) == 0: index += 1 if index < len(__snake_case ): __magic_name__ : List[str] =required_input[index][0] if return_tensors is None: if is_tf_tensor(__snake_case ): __magic_name__ : int ="""tf""" elif is_torch_tensor(__snake_case ): __magic_name__ : str ="""pt""" elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ): __magic_name__ : List[Any] ="""np""" else: raise ValueError( f"type of {first_element} unknown: {type(__snake_case )}. " """Should be one of a python, numpy, pytorch or tensorflow object.""" ) for key, value in processed_features.items(): if isinstance(value[0] , (int, float) ): __magic_name__ : List[str] =to_numpy(__snake_case ) else: __magic_name__ : str =[to_numpy(__snake_case ) for v in value] # Convert padding_strategy in PaddingStrategy __magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case ) __magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]] __magic_name__ : Dict =len(__snake_case ) if not all(len(__snake_case ) == batch_size for v in processed_features.values() ): raise ValueError("""Some items in the output dictionary have a different batch size than others.""" ) __magic_name__ : Optional[int] =[] for i in range(__snake_case ): __magic_name__ : Any ={k: v[i] for k, v in processed_features.items()} # truncation __magic_name__ : List[str] =self._truncate( __snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , ) truncated_inputs.append(__snake_case ) if padding_strategy == PaddingStrategy.LONGEST: # make sure that `max_length` cannot be longer than the longest truncated length __magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs ) __magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH __magic_name__ : str ={} for i in range(__snake_case ): # padding __magic_name__ : List[str] =self._pad( truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , ) for key, value in outputs.items(): if key not in batch_outputs: __magic_name__ : Dict =[] if value.dtype is np.dtype(np.floataa ): __magic_name__ : Optional[int] =value.astype(np.floataa ) batch_outputs[key].append(__snake_case ) return BatchFeature(__snake_case , tensor_type=__snake_case ) def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' __magic_name__ : Dict =processed_features[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: __magic_name__ : Any =len(__snake_case ) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length if return_attention_mask and "attention_mask" not in processed_features: __magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa ) if needs_to_be_padded: __magic_name__ : List[Any] =max_length - len(__snake_case ) if self.padding_side == "right": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (0, difference) ) __magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference) __magic_name__ : str =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) elif self.padding_side == "left": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (difference, 0) ) __magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0) __magic_name__ : List[Any] =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) else: raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) ) return processed_features def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' if not truncation: return processed_features elif truncation and max_length is None: raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" ) __magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]] # find `max_length` that fits `pad_to_multiple_of` if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : Any =len(__snake_case ) > max_length if needs_to_be_truncated: __magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length] if "attention_mask" in processed_features: __magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length] return processed_features def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ): '''simple docstring''' if padding is not False: if padding is True: __magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch elif not isinstance(__snake_case , __snake_case ): __magic_name__ : Optional[int] =PaddingStrategy(__snake_case ) elif isinstance(__snake_case , __snake_case ): __magic_name__ : Any =padding else: __magic_name__ : Any =PaddingStrategy.DO_NOT_PAD # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: raise ValueError( f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" ) # Test if we have a padding value if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None): raise ValueError( """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use""" """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" ) return padding_strategy
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1
import gc import unittest from transformers import CTRLConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin 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 ( CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, ) class __A : def __init__( self :Tuple , __snake_case :int , __snake_case :int=14 , __snake_case :Tuple=7 , __snake_case :Any=True , __snake_case :Dict=True , __snake_case :Any=True , __snake_case :Optional[Any]=True , __snake_case :Optional[Any]=True , __snake_case :Dict=99 , __snake_case :Any=32 , __snake_case :int=5 , __snake_case :Any=4 , __snake_case :str=37 , __snake_case :List[Any]="gelu" , __snake_case :str=0.1 , __snake_case :str=0.1 , __snake_case :str=5_12 , __snake_case :int=16 , __snake_case :Optional[Any]=2 , __snake_case :Dict=0.02 , __snake_case :Tuple=3 , __snake_case :List[str]=4 , __snake_case :Any=None , ): '''simple docstring''' __magic_name__ : int =parent __magic_name__ : str =batch_size __magic_name__ : str =seq_length __magic_name__ : Tuple =is_training __magic_name__ : List[str] =use_token_type_ids __magic_name__ : Tuple =use_input_mask __magic_name__ : Any =use_labels __magic_name__ : str =use_mc_token_ids __magic_name__ : List[Any] =vocab_size __magic_name__ : List[str] =hidden_size __magic_name__ : Tuple =num_hidden_layers __magic_name__ : Union[str, Any] =num_attention_heads __magic_name__ : Tuple =intermediate_size __magic_name__ : Optional[int] =hidden_act __magic_name__ : Tuple =hidden_dropout_prob __magic_name__ : str =attention_probs_dropout_prob __magic_name__ : Dict =max_position_embeddings __magic_name__ : Dict =type_vocab_size __magic_name__ : Any =type_sequence_label_size __magic_name__ : Optional[Any] =initializer_range __magic_name__ : Optional[Any] =num_labels __magic_name__ : Any =num_choices __magic_name__ : List[str] =scope __magic_name__ : Optional[Any] =self.vocab_size - 1 def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __magic_name__ : Any =None if self.use_input_mask: __magic_name__ : Optional[int] =random_attention_mask([self.batch_size, self.seq_length] ) __magic_name__ : Any =None if self.use_token_type_ids: __magic_name__ : int =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) __magic_name__ : Tuple =None if self.use_mc_token_ids: __magic_name__ : Tuple =ids_tensor([self.batch_size, self.num_choices] , self.seq_length ) __magic_name__ : Optional[Any] =None __magic_name__ : str =None __magic_name__ : List[str] =None if self.use_labels: __magic_name__ : int =ids_tensor([self.batch_size] , self.type_sequence_label_size ) __magic_name__ : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) __magic_name__ : List[Any] =ids_tensor([self.batch_size] , self.num_choices ) __magic_name__ : Optional[int] =self.get_config() __magic_name__ : List[Any] =ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 ) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def A__ ( self :List[Any] ): '''simple docstring''' return CTRLConfig( vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , ) def A__ ( self :int , __snake_case :Dict , __snake_case :Optional[Any] , __snake_case :Tuple , __snake_case :Dict , __snake_case :Dict , *__snake_case :str ): '''simple docstring''' __magic_name__ : Optional[int] =CTRLModel(config=__snake_case ) model.to(__snake_case ) model.eval() model(__snake_case , token_type_ids=__snake_case , head_mask=__snake_case ) model(__snake_case , token_type_ids=__snake_case ) __magic_name__ : int =model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertEqual(len(result.past_key_values ) , config.n_layer ) def A__ ( self :Dict , __snake_case :Tuple , __snake_case :str , __snake_case :Dict , __snake_case :Tuple , __snake_case :List[Any] , *__snake_case :List[str] ): '''simple docstring''' __magic_name__ : str =CTRLLMHeadModel(__snake_case ) model.to(__snake_case ) model.eval() __magic_name__ : Any =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.loss.shape , () ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ : Tuple =self.prepare_config_and_inputs() ( ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ) : Any =config_and_inputs __magic_name__ : str ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask} return config, inputs_dict def A__ ( self :Union[str, Any] , __snake_case :Union[str, Any] , __snake_case :str , __snake_case :Optional[int] , __snake_case :str , *__snake_case :List[str] ): '''simple docstring''' __magic_name__ : Tuple =self.num_labels __magic_name__ : str =CTRLForSequenceClassification(__snake_case ) model.to(__snake_case ) model.eval() __magic_name__ : str =ids_tensor([self.batch_size] , self.type_sequence_label_size ) __magic_name__ : Any =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) @require_torch class __A ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else () UpperCamelCase = (CTRLLMHeadModel,) if is_torch_available() else () UpperCamelCase = ( { """feature-extraction""": CTRLModel, """text-classification""": CTRLForSequenceClassification, """text-generation""": CTRLLMHeadModel, """zero-shot""": CTRLForSequenceClassification, } if is_torch_available() else {} ) UpperCamelCase = True UpperCamelCase = False UpperCamelCase = False def A__ ( self :Any , __snake_case :List[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :List[str] , __snake_case :int ): '''simple docstring''' if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests": # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers. # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny # config could not be created. return True return False def A__ ( self :str ): '''simple docstring''' __magic_name__ : Any =CTRLModelTester(self ) __magic_name__ : Optional[int] =ConfigTester(self , config_class=__snake_case , n_embd=37 ) def A__ ( self :Any ): '''simple docstring''' super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() torch.cuda.empty_cache() def A__ ( self :Any ): '''simple docstring''' self.config_tester.run_common_tests() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : Tuple =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_ctrl_model(*__snake_case ) def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Dict =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head_model(*__snake_case ) @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" ) def A__ ( self :Tuple ): '''simple docstring''' pass @slow def A__ ( self :Any ): '''simple docstring''' for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: __magic_name__ : Tuple =CTRLModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) @unittest.skip("""The model doesn't support left padding""" ) # and it's not used enough to be worth fixing :) def A__ ( self :List[Any] ): '''simple docstring''' pass @require_torch class __A ( unittest.TestCase ): def A__ ( self :Dict ): '''simple docstring''' super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() torch.cuda.empty_cache() @slow def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : int =CTRLLMHeadModel.from_pretrained("""ctrl""" ) model.to(__snake_case ) __magic_name__ : Optional[Any] =torch.tensor( [[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=__snake_case ) # Legal the president is __magic_name__ : Optional[int] =[ 1_18_59, 0, 16_11, 8, 5, 1_50, 2_64_49, 2, 19, 3_48, 4_69, 3, 25_95, 48, 2_07_40, 24_65_33, 24_65_33, 19, 30, 5, ] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a __magic_name__ : str =model.generate(__snake_case , do_sample=__snake_case ) self.assertListEqual(output_ids[0].tolist() , __snake_case )
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import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class __A ( nn.Module ): def __init__( self :List[Any] ): '''simple docstring''' super().__init__() __magic_name__ : Tuple =nn.Linear(3 , 4 ) __magic_name__ : Union[str, Any] =nn.BatchNormad(4 ) __magic_name__ : List[str] =nn.Linear(4 , 5 ) def A__ ( self :Dict , __snake_case :Tuple ): '''simple docstring''' return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) ) class __A ( UpperCamelCase__ ): def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ): '''simple docstring''' return (args[0] + 1,) + args[1:], kwargs class __A ( UpperCamelCase__ ): def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ): '''simple docstring''' return output + 1 class __A ( unittest.TestCase ): def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : Tuple =ModelHook() add_hook_to_module(__snake_case , __snake_case ) self.assertEqual(test_model._hf_hook , __snake_case ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : List[str] =ModelHook() add_hook_to_module(__snake_case , __snake_case ) add_hook_to_module(__snake_case , __snake_case , append=__snake_case ) self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case ) self.assertEqual(len(test_model._hf_hook.hooks ) , 2 ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() __magic_name__ : Any =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(x + 1 ) __magic_name__ : Optional[Any] =test_model(x + 2 ) __magic_name__ : int =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : int =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : str =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : List[str] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) assert torch.allclose(__snake_case , __snake_case , atol=1E-5 ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() __magic_name__ : Dict =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(__snake_case ) __magic_name__ : Dict =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Optional[int] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) assert torch.allclose(__snake_case , output + 2 , atol=1E-5 ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : Tuple =ModelForTest() __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Dict =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 ) ) self.assertTrue(outputa.requires_grad ) __magic_name__ : Any =True __magic_name__ : Any =test_model(__snake_case ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) ) self.assertEqual(model.lineara.weight.device , torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[Any] =model(__snake_case ) self.assertEqual(output.device , torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) ) __magic_name__ : int =torch.randn(2 , 3 ).to(0 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , torch.device(0 ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : int =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True} add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : Union[str, Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload __magic_name__ : Tuple ={ """execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True, """offload_buffers""": True, } add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Tuple =torch.randn(2 , 3 ) __magic_name__ : int =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : str =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Optional[int] =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Dict =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Any =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
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1
from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast from ...utils import logging if TYPE_CHECKING: from ...feature_extraction_utils import FeatureExtractionMixin from ...tokenization_utils_base import PreTrainedTokenizerBase from ...utils import TensorType UpperCAmelCase_ : str = logging.get_logger(__name__) UpperCAmelCase_ : Tuple = { "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json", } # fmt: off UpperCAmelCase_ : str = [ 1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 357, 366, 438, 532, 685, 705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377, 1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211, 4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786, 11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791, 17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409, 34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361 ] UpperCAmelCase_ : Union[str, Any] = [ 1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362 ] class __A ( UpperCamelCase__ ): UpperCamelCase = """whisper""" UpperCamelCase = ["""past_key_values"""] UpperCamelCase = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""} def __init__( self :Any , __snake_case :Any=5_18_65 , __snake_case :Optional[int]=80 , __snake_case :str=6 , __snake_case :List[str]=4 , __snake_case :List[Any]=6 , __snake_case :Any=4 , __snake_case :Union[str, Any]=15_36 , __snake_case :List[Any]=15_36 , __snake_case :str=0.0 , __snake_case :Optional[Any]=0.0 , __snake_case :int=5_02_57 , __snake_case :Optional[int]=True , __snake_case :List[str]=True , __snake_case :str="gelu" , __snake_case :Optional[Any]=2_56 , __snake_case :Union[str, Any]=0.0 , __snake_case :Any=0.0 , __snake_case :Optional[int]=0.0 , __snake_case :str=0.02 , __snake_case :Union[str, Any]=False , __snake_case :int=15_00 , __snake_case :List[Any]=4_48 , __snake_case :Optional[int]=5_02_56 , __snake_case :Tuple=5_02_56 , __snake_case :Any=5_02_56 , __snake_case :Dict=None , __snake_case :int=[2_20, 5_02_56] , __snake_case :List[Any]=False , __snake_case :Optional[Any]=2_56 , __snake_case :Tuple=False , __snake_case :int=0.05 , __snake_case :List[Any]=10 , __snake_case :Optional[int]=2 , __snake_case :Tuple=0.0 , __snake_case :Union[str, Any]=10 , __snake_case :Dict=0 , __snake_case :List[Any]=7 , **__snake_case :List[str] , ): '''simple docstring''' __magic_name__ : Any =vocab_size __magic_name__ : Optional[Any] =num_mel_bins __magic_name__ : Union[str, Any] =d_model __magic_name__ : List[Any] =encoder_layers __magic_name__ : str =encoder_attention_heads __magic_name__ : List[str] =decoder_layers __magic_name__ : List[str] =decoder_attention_heads __magic_name__ : int =decoder_ffn_dim __magic_name__ : Tuple =encoder_ffn_dim __magic_name__ : Tuple =dropout __magic_name__ : List[Any] =attention_dropout __magic_name__ : Any =activation_dropout __magic_name__ : List[Any] =activation_function __magic_name__ : Union[str, Any] =init_std __magic_name__ : Tuple =encoder_layerdrop __magic_name__ : Optional[Any] =decoder_layerdrop __magic_name__ : List[str] =use_cache __magic_name__ : Any =encoder_layers __magic_name__ : str =scale_embedding # scale factor will be sqrt(d_model) if True __magic_name__ : Optional[int] =max_source_positions __magic_name__ : str =max_target_positions # Audio Classification-specific parameters. Feel free to ignore for other classes. __magic_name__ : Any =classifier_proj_size __magic_name__ : Tuple =use_weighted_layer_sum # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 __magic_name__ : Any =apply_spec_augment __magic_name__ : Optional[Any] =mask_time_prob __magic_name__ : str =mask_time_length __magic_name__ : List[str] =mask_time_min_masks __magic_name__ : List[str] =mask_feature_prob __magic_name__ : Dict =mask_feature_length __magic_name__ : Dict =mask_feature_min_masks __magic_name__ : Tuple =median_filter_width super().__init__( pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , is_encoder_decoder=__snake_case , decoder_start_token_id=__snake_case , suppress_tokens=__snake_case , begin_suppress_tokens=__snake_case , **__snake_case , ) class __A ( UpperCamelCase__ ): @property def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : int =OrderedDict( [ ("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}), ] ) if self.use_past: __magic_name__ : int ={0: """batch"""} else: __magic_name__ : Union[str, Any] ={0: """batch""", 1: """decoder_sequence"""} if self.use_past: self.fill_with_past_key_values_(__snake_case , direction="""inputs""" ) return common_inputs def A__ ( self :List[str] , __snake_case :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , __snake_case :int = -1 , __snake_case :int = -1 , __snake_case :bool = False , __snake_case :Optional["TensorType"] = None , __snake_case :int = 2_20_50 , __snake_case :float = 5.0 , __snake_case :int = 2_20 , ): '''simple docstring''' __magic_name__ : List[Any] =OrderedDict() __magic_name__ : Optional[int] =OnnxConfig.generate_dummy_inputs( self , preprocessor=preprocessor.feature_extractor , batch_size=__snake_case , framework=__snake_case , sampling_rate=__snake_case , time_duration=__snake_case , frequency=__snake_case , ) __magic_name__ : List[Any] =encoder_inputs["""input_features"""].shape[2] __magic_name__ : Tuple =encoder_sequence_length // 2 if self.use_past else seq_length __magic_name__ : Union[str, Any] =super().generate_dummy_inputs( preprocessor.tokenizer , __snake_case , __snake_case , __snake_case , __snake_case ) __magic_name__ : Optional[Any] =encoder_inputs.pop("""input_features""" ) __magic_name__ : Dict =decoder_inputs.pop("""decoder_input_ids""" ) if "past_key_values" in decoder_inputs: __magic_name__ : Optional[int] =decoder_inputs.pop("""past_key_values""" ) return dummy_inputs @property def A__ ( self :Union[str, Any] ): '''simple docstring''' return 1E-3
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import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import XLMRobertaTokenizerFast from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = KandinskyInpaintPipeline UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""] UpperCamelCase = [ """prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image""", ] UpperCamelCase = [ """generator""", """height""", """width""", """latents""", """guidance_scale""", """negative_prompt""", """num_inference_steps""", """return_dict""", """guidance_scale""", """num_images_per_prompt""", """output_type""", """return_dict""", ] UpperCamelCase = False @property def A__ ( self :Union[str, Any] ): '''simple docstring''' return 32 @property def A__ ( self :Optional[Any] ): '''simple docstring''' return 32 @property def A__ ( self :List[Any] ): '''simple docstring''' return self.time_input_dim @property def A__ ( self :Dict ): '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self :List[Any] ): '''simple docstring''' return 1_00 @property def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" ) return tokenizer @property def A__ ( self :str ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : str =MCLIPConfig( numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , ) __magic_name__ : Tuple =MultilingualCLIP(__snake_case ) __magic_name__ : Optional[int] =text_encoder.eval() return text_encoder @property def A__ ( self :Dict ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Optional[Any] ={ """in_channels""": 9, # Out channels is double in channels because predicts mean and variance """out_channels""": 8, """addition_embed_type""": """text_image""", """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""), """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""), """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""", """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2), """layers_per_block""": 1, """encoder_hid_dim""": self.text_embedder_hidden_size, """encoder_hid_dim_type""": """text_image_proj""", """cross_attention_dim""": self.cross_attention_dim, """attention_head_dim""": 4, """resnet_time_scale_shift""": """scale_shift""", """class_embed_type""": None, } __magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case ) return model @property def A__ ( self :List[str] ): '''simple docstring''' return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def A__ ( self :Tuple ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs ) return model def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[str] =self.dummy_text_encoder __magic_name__ : Optional[Any] =self.dummy_tokenizer __magic_name__ : Optional[Any] =self.dummy_unet __magic_name__ : Tuple =self.dummy_movq __magic_name__ : List[str] =DDIMScheduler( num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , ) __magic_name__ : str ={ """text_encoder""": text_encoder, """tokenizer""": tokenizer, """unet""": unet, """scheduler""": scheduler, """movq""": movq, } return components def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ): '''simple docstring''' __magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case ) # create init_image __magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0] __magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) ) # create mask __magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa ) __magic_name__ : Any =0 if str(__snake_case ).startswith("""mps""" ): __magic_name__ : Dict =torch.manual_seed(__snake_case ) else: __magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case ) __magic_name__ : List[Any] ={ """prompt""": """horse""", """image""": init_image, """mask_image""": mask, """image_embeds""": image_embeds, """negative_image_embeds""": negative_image_embeds, """generator""": generator, """height""": 64, """width""": 64, """num_inference_steps""": 2, """guidance_scale""": 4.0, """output_type""": """np""", } return inputs def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Tuple ="""cpu""" __magic_name__ : List[Any] =self.get_dummy_components() __magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case ) __magic_name__ : Tuple =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) ) __magic_name__ : List[Any] =output.images __magic_name__ : Any =pipe( **self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0] __magic_name__ : int =image[0, -3:, -3:, -1] __magic_name__ : str =image_from_tuple[0, -3:, -3:, -1] print(f"image.shape {image.shape}" ) assert image.shape == (1, 64, 64, 3) __magic_name__ : Optional[Any] =np.array( [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" def A__ ( self :Dict ): '''simple docstring''' super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) @slow @require_torch_gpu class __A ( unittest.TestCase ): def A__ ( self :List[Any] ): '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : List[str] =load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" ) __magic_name__ : int =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" ) __magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa ) __magic_name__ : Any =0 __magic_name__ : int ="""a hat""" __magic_name__ : int =KandinskyPriorPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa ) pipe_prior.to(__snake_case ) __magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa ) __magic_name__ : int =pipeline.to(__snake_case ) pipeline.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 ) __magic_name__ , __magic_name__ : Dict =pipe_prior( __snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple() __magic_name__ : Optional[Any] =pipeline( __snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , ) __magic_name__ : Optional[int] =output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(__snake_case , __snake_case )
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class __A : def __init__( self :List[str] , __snake_case :str , __snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : int =name __magic_name__ : Optional[int] =val def __str__( self :Any ): '''simple docstring''' return f"{self.__class__.__name__}({self.name}, {self.val})" def __lt__( self :List[Any] , __snake_case :Any ): '''simple docstring''' return self.val < other.val class __A : def __init__( self :List[str] , __snake_case :int ): '''simple docstring''' __magic_name__ : Optional[Any] ={} __magic_name__ : Optional[int] ={} __magic_name__ : Union[str, Any] =self.build_heap(__snake_case ) def __getitem__( self :Union[str, Any] , __snake_case :int ): '''simple docstring''' return self.get_value(__snake_case ) def A__ ( self :Dict , __snake_case :List[str] ): '''simple docstring''' return (idx - 1) // 2 def A__ ( self :Any , __snake_case :Dict ): '''simple docstring''' return idx * 2 + 1 def A__ ( self :int , __snake_case :Dict ): '''simple docstring''' return idx * 2 + 2 def A__ ( self :str , __snake_case :Optional[Any] ): '''simple docstring''' return self.heap_dict[key] def A__ ( self :Any , __snake_case :Union[str, Any] ): '''simple docstring''' __magic_name__ : Optional[int] =len(__snake_case ) - 1 __magic_name__ : List[Any] =self.get_parent_idx(__snake_case ) for idx, i in enumerate(__snake_case ): __magic_name__ : Dict =idx __magic_name__ : str =i.val for i in range(__snake_case , -1 , -1 ): self.sift_down(__snake_case , __snake_case ) return array def A__ ( self :Dict , __snake_case :Optional[Any] , __snake_case :Optional[Any] ): '''simple docstring''' while True: __magic_name__ : int =self.get_left_child_idx(__snake_case ) # noqa: E741 __magic_name__ : List[str] =self.get_right_child_idx(__snake_case ) __magic_name__ : Tuple =idx if l < len(__snake_case ) and array[l] < array[idx]: __magic_name__ : Dict =l if r < len(__snake_case ) and array[r] < array[smallest]: __magic_name__ : List[str] =r if smallest != idx: __magic_name__ , __magic_name__ : int =array[smallest], array[idx] ( ( __magic_name__ ) , ( __magic_name__ ) , ) : int =( self.idx_of_element[array[smallest]], self.idx_of_element[array[idx]], ) __magic_name__ : Any =smallest else: break def A__ ( self :int , __snake_case :Tuple ): '''simple docstring''' __magic_name__ : Optional[int] =self.get_parent_idx(__snake_case ) while p >= 0 and self.heap[p] > self.heap[idx]: __magic_name__ , __magic_name__ : str =self.heap[idx], self.heap[p] __magic_name__ , __magic_name__ : Dict =( self.idx_of_element[self.heap[idx]], self.idx_of_element[self.heap[p]], ) __magic_name__ : Union[str, Any] =p __magic_name__ : Tuple =self.get_parent_idx(__snake_case ) def A__ ( self :List[Any] ): '''simple docstring''' return self.heap[0] def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ , __magic_name__ : List[Any] =self.heap[-1], self.heap[0] __magic_name__ , __magic_name__ : Optional[Any] =( self.idx_of_element[self.heap[-1]], self.idx_of_element[self.heap[0]], ) __magic_name__ : Tuple =self.heap.pop() del self.idx_of_element[x] self.sift_down(0 , self.heap ) return x def A__ ( self :List[Any] , __snake_case :Any ): '''simple docstring''' self.heap.append(__snake_case ) __magic_name__ : Dict =len(self.heap ) - 1 __magic_name__ : List[Any] =node.val self.sift_up(len(self.heap ) - 1 ) def A__ ( self :Optional[Any] ): '''simple docstring''' return len(self.heap ) == 0 def A__ ( self :int , __snake_case :List[Any] , __snake_case :Tuple ): '''simple docstring''' assert ( self.heap[self.idx_of_element[node]].val > new_value ), "newValue must be less that current value" __magic_name__ : Dict =new_value __magic_name__ : List[str] =new_value self.sift_up(self.idx_of_element[node] ) UpperCAmelCase_ : List[str] = Node("R", -1) UpperCAmelCase_ : Optional[Any] = Node("B", 6) UpperCAmelCase_ : Optional[int] = Node("A", 3) UpperCAmelCase_ : Optional[Any] = Node("X", 1) UpperCAmelCase_ : List[Any] = Node("E", 4) # Use one of these two ways to generate Min-Heap # Generating Min-Heap from array UpperCAmelCase_ : Any = 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, -17) # After for i in my_min_heap.heap: print(i) if __name__ == "__main__": import doctest doctest.testmod()
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import inspect import unittest import numpy as np from tests.test_modeling_common import floats_tensor from transformers import MaskaFormerConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel if is_vision_available(): from transformers import MaskaFormerImageProcessor if is_vision_available(): from PIL import Image class __A : def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ): '''simple docstring''' __magic_name__ : Optional[int] =parent __magic_name__ : List[Any] =batch_size __magic_name__ : List[str] =is_training __magic_name__ : List[str] =use_auxiliary_loss __magic_name__ : Union[str, Any] =num_queries __magic_name__ : str =num_channels __magic_name__ : Union[str, Any] =min_size __magic_name__ : Union[str, Any] =max_size __magic_name__ : Optional[int] =num_labels __magic_name__ : Tuple =hidden_dim __magic_name__ : Any =hidden_dim def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to( __snake_case ) __magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case ) __magic_name__ : List[str] =( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5 ).float() __magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long() __magic_name__ : str =self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Dict =MaskaFormerConfig( hidden_size=self.hidden_dim , ) __magic_name__ : str =self.num_queries __magic_name__ : Dict =self.num_labels __magic_name__ : int =[1, 1, 1, 1] __magic_name__ : List[str] =self.num_channels __magic_name__ : str =64 __magic_name__ : List[str] =1_28 __magic_name__ : Optional[Any] =self.hidden_dim __magic_name__ : Tuple =self.hidden_dim __magic_name__ : Optional[int] =self.hidden_dim return config def A__ ( self :Any ): '''simple docstring''' __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs() __magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask} return config, inputs_dict def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ): '''simple docstring''' __magic_name__ : int =output.encoder_hidden_states __magic_name__ : List[str] =output.pixel_decoder_hidden_states __magic_name__ : int =output.transformer_decoder_hidden_states self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , config.decoder_layers ) def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ): '''simple docstring''' with torch.no_grad(): __magic_name__ : List[str] =MaskaFormerModel(config=__snake_case ) model.to(__snake_case ) model.eval() __magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case ) self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(output.encoder_last_hidden_state is not None ) if output_hidden_states: self.check_output_hidden_state(__snake_case , __snake_case ) def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ): '''simple docstring''' __magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case ) model.to(__snake_case ) model.eval() def comm_check_on_output(__snake_case :List[str] ): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.encoder_last_hidden_state is not None ) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): __magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : List[str] =model(__snake_case ) comm_check_on_output(__snake_case ) __magic_name__ : Any =model( pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) comm_check_on_output(__snake_case ) self.parent.assertTrue(result.loss is not None ) self.parent.assertEqual(result.loss.shape , torch.Size([1] ) ) @require_torch class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else () UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {} UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False def A__ ( self :str ): '''simple docstring''' __magic_name__ : Any =MaskaFormerModelTester(self ) __magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case ) def A__ ( self :Dict ): '''simple docstring''' self.config_tester.run_common_tests() def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case ) @unittest.skip(reason="""Mask2Former does not use inputs_embeds""" ) def A__ ( self :List[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" ) def A__ ( self :Dict ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former is not a generative model""" ) def A__ ( self :Optional[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not use token embeddings""" ) def A__ ( self :int ): '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip( reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" ) def A__ ( self :Tuple ): '''simple docstring''' pass @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" ) def A__ ( self :Union[str, Any] ): '''simple docstring''' pass def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : Tuple =model_class(__snake_case ) __magic_name__ : Optional[Any] =inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic __magic_name__ : Tuple =[*signature.parameters.keys()] __magic_name__ : Optional[Any] =["""pixel_values"""] self.assertListEqual(arg_names[:1] , __snake_case ) @slow def A__ ( self :Tuple ): '''simple docstring''' for model_name in ["facebook/mask2former-swin-small-coco-instance"]: __magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =(self.model_tester.min_size,) * 2 __magic_name__ : Union[str, Any] ={ """pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ), """mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ), """class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(), } __magic_name__ : Optional[Any] =self.model_tester.get_config() __magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case ) __magic_name__ : Any =model(**__snake_case ) self.assertTrue(outputs.loss is not None ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case ) __magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case ) self.assertTrue(outputs.attentions is not None ) def A__ ( self :int ): '''simple docstring''' if not self.model_tester.is_training: return __magic_name__ : List[Any] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Dict =model_class(__snake_case ) model.to(__snake_case ) model.train() __magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss loss.backward() def A__ ( self :int ): '''simple docstring''' __magic_name__ : List[str] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Tuple =True __magic_name__ : Optional[int] =True __magic_name__ : int =model_class(__snake_case ).to(__snake_case ) model.train() __magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) __magic_name__ : Optional[int] =outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() __magic_name__ : Optional[int] =outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=__snake_case ) self.assertIsNotNone(encoder_hidden_states.grad ) self.assertIsNotNone(pixel_decoder_hidden_states.grad ) self.assertIsNotNone(transformer_decoder_hidden_states.grad ) self.assertIsNotNone(attentions.grad ) UpperCAmelCase_ : Dict = 1e-4 def lowerCAmelCase_ ( ): __magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_vision @slow class __A ( unittest.TestCase ): @cached_property def A__ ( self :int ): '''simple docstring''' return "facebook/mask2former-swin-small-coco-instance" @cached_property def A__ ( self :int ): '''simple docstring''' return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case ) __magic_name__ : int =self.default_image_processor __magic_name__ : List[Any] =prepare_img() __magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Dict =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : List[str] =model(**__snake_case ) __magic_name__ : Any =torch.tensor( [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Dict =torch.tensor( [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Any =torch.tensor( [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Optional[int] =self.default_image_processor __magic_name__ : Tuple =prepare_img() __magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : str =model(**__snake_case ) # masks_queries_logits __magic_name__ : List[Any] =outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ) __magic_name__ : List[Any] =[ [-8.7839, -9.0056, -8.8121], [-7.4104, -7.0313, -6.5401], [-6.6105, -6.3427, -6.4675], ] __magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) # class_queries_logits __magic_name__ : Any =outputs.class_queries_logits self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) ) __magic_name__ : int =torch.tensor( [ [1.8324, -8.0835, -4.1922], [0.8450, -9.0050, -3.6053], [0.3045, -7.7293, -3.0275], ] ).to(__snake_case ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Any =self.default_image_processor __magic_name__ : Union[str, Any] =image_processor( [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , ) __magic_name__ : str =inputs["""pixel_values"""].to(__snake_case ) __magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]] __magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]] with torch.no_grad(): __magic_name__ : Dict =model(**__snake_case ) self.assertTrue(outputs.loss is not None )
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1
import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel from ...utils import logging UpperCAmelCase_ : List[str] = logging.get_logger(__name__) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ : Tuple =nn.functional.normalize(lowerCamelCase ) __magic_name__ : Optional[int] =nn.functional.normalize(lowerCamelCase ) return torch.mm(lowerCamelCase , normalized_text_embeds.t() ) class __A ( UpperCamelCase__ ): UpperCamelCase = CLIPConfig UpperCamelCase = ["""CLIPEncoderLayer"""] def __init__( self :Any , __snake_case :CLIPConfig ): '''simple docstring''' super().__init__(__snake_case ) __magic_name__ : Optional[Any] =CLIPVisionModel(config.vision_config ) __magic_name__ : Any =nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=__snake_case ) __magic_name__ : List[Any] =nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=__snake_case ) __magic_name__ : int =nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=__snake_case ) __magic_name__ : List[str] =nn.Parameter(torch.ones(17 ) , requires_grad=__snake_case ) __magic_name__ : Tuple =nn.Parameter(torch.ones(3 ) , requires_grad=__snake_case ) @torch.no_grad() def A__ ( self :Optional[Any] , __snake_case :List[Any] , __snake_case :str ): '''simple docstring''' __magic_name__ : Union[str, Any] =self.vision_model(__snake_case )[1] # pooled_output __magic_name__ : Tuple =self.visual_projection(__snake_case ) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 __magic_name__ : List[Any] =cosine_distance(__snake_case , self.special_care_embeds ).cpu().float().numpy() __magic_name__ : int =cosine_distance(__snake_case , self.concept_embeds ).cpu().float().numpy() __magic_name__ : Dict =[] __magic_name__ : Any =image_embeds.shape[0] for i in range(__snake_case ): __magic_name__ : Optional[Any] ={"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []} # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign images __magic_name__ : Dict =0.0 for concept_idx in range(len(special_cos_dist[0] ) ): __magic_name__ : Optional[Any] =special_cos_dist[i][concept_idx] __magic_name__ : Any =self.special_care_embeds_weights[concept_idx].item() __magic_name__ : Tuple =round(concept_cos - concept_threshold + adjustment , 3 ) if result_img["special_scores"][concept_idx] > 0: result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]} ) __magic_name__ : int =0.01 for concept_idx in range(len(cos_dist[0] ) ): __magic_name__ : Any =cos_dist[i][concept_idx] __magic_name__ : Union[str, Any] =self.concept_embeds_weights[concept_idx].item() __magic_name__ : Optional[int] =round(concept_cos - concept_threshold + adjustment , 3 ) if result_img["concept_scores"][concept_idx] > 0: result_img["bad_concepts"].append(__snake_case ) result.append(__snake_case ) __magic_name__ : Optional[int] =[len(res["""bad_concepts"""] ) > 0 for res in result] return images, has_nsfw_concepts @torch.no_grad() def A__ ( self :Tuple , __snake_case :torch.FloatTensor , __snake_case :torch.FloatTensor ): '''simple docstring''' __magic_name__ : Dict =self.vision_model(__snake_case )[1] # pooled_output __magic_name__ : Union[str, Any] =self.visual_projection(__snake_case ) __magic_name__ : Optional[Any] =cosine_distance(__snake_case , self.special_care_embeds ) __magic_name__ : str =cosine_distance(__snake_case , self.concept_embeds ) # increase this value to create a stronger `nsfw` filter # at the cost of increasing the possibility of filtering benign images __magic_name__ : str =0.0 __magic_name__ : Union[str, Any] =special_cos_dist - self.special_care_embeds_weights + adjustment # special_scores = special_scores.round(decimals=3) __magic_name__ : Tuple =torch.any(special_scores > 0 , dim=1 ) __magic_name__ : List[Any] =special_care * 0.01 __magic_name__ : int =special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] ) __magic_name__ : Optional[int] =(cos_dist - self.concept_embeds_weights) + special_adjustment # concept_scores = concept_scores.round(decimals=3) __magic_name__ : int =torch.any(concept_scores > 0 , dim=1 ) return images, has_nsfw_concepts
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import warnings from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "nvidia/segformer-b0-finetuned-ade-512-512": ( "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json" ), # See all SegFormer models at https://huggingface.co/models?filter=segformer } class __A ( UpperCamelCase__ ): UpperCamelCase = """segformer""" def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ): '''simple docstring''' super().__init__(**__snake_case ) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be""" """ removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , ) __magic_name__ : Dict =num_channels __magic_name__ : str =num_encoder_blocks __magic_name__ : List[Any] =depths __magic_name__ : Optional[Any] =sr_ratios __magic_name__ : List[str] =hidden_sizes __magic_name__ : List[str] =patch_sizes __magic_name__ : Any =strides __magic_name__ : Optional[Any] =mlp_ratios __magic_name__ : str =num_attention_heads __magic_name__ : int =hidden_act __magic_name__ : List[Any] =hidden_dropout_prob __magic_name__ : Optional[Any] =attention_probs_dropout_prob __magic_name__ : Optional[Any] =classifier_dropout_prob __magic_name__ : List[str] =initializer_range __magic_name__ : List[str] =drop_path_rate __magic_name__ : List[Any] =layer_norm_eps __magic_name__ : List[str] =decoder_hidden_size __magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case ) __magic_name__ : Dict =semantic_loss_ignore_index class __A ( UpperCamelCase__ ): UpperCamelCase = version.parse("""1.11""" ) @property def A__ ( self :List[str] ): '''simple docstring''' return OrderedDict( [ ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}), ] ) @property def A__ ( self :Any ): '''simple docstring''' return 1E-4 @property def A__ ( self :int ): '''simple docstring''' return 12
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from __future__ import annotations import unittest from transformers import DebertaVaConfig, 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 ( TFDebertaVaForMaskedLM, TFDebertaVaForQuestionAnswering, TFDebertaVaForSequenceClassification, TFDebertaVaForTokenClassification, TFDebertaVaModel, ) class __A : def __init__( self :List[str] , __snake_case :Optional[int] , __snake_case :str=13 , __snake_case :int=7 , __snake_case :List[str]=True , __snake_case :Tuple=True , __snake_case :List[str]=True , __snake_case :Optional[int]=True , __snake_case :Tuple=99 , __snake_case :Tuple=32 , __snake_case :Optional[int]=2 , __snake_case :str=4 , __snake_case :List[Any]=37 , __snake_case :List[Any]="gelu" , __snake_case :str=0.1 , __snake_case :List[str]=0.1 , __snake_case :Tuple=5_12 , __snake_case :List[Any]=16 , __snake_case :Union[str, Any]=2 , __snake_case :Tuple=0.02 , __snake_case :int=False , __snake_case :Optional[Any]=True , __snake_case :Union[str, Any]="None" , __snake_case :str=3 , __snake_case :Any=4 , __snake_case :Dict=None , ): '''simple docstring''' __magic_name__ : Optional[int] =parent __magic_name__ : int =batch_size __magic_name__ : str =seq_length __magic_name__ : Optional[int] =is_training __magic_name__ : Dict =use_input_mask __magic_name__ : Any =use_token_type_ids __magic_name__ : List[str] =use_labels __magic_name__ : List[str] =vocab_size __magic_name__ : Union[str, Any] =hidden_size __magic_name__ : str =num_hidden_layers __magic_name__ : Union[str, Any] =num_attention_heads __magic_name__ : int =intermediate_size __magic_name__ : List[Any] =hidden_act __magic_name__ : int =hidden_dropout_prob __magic_name__ : Dict =attention_probs_dropout_prob __magic_name__ : int =max_position_embeddings __magic_name__ : str =type_vocab_size __magic_name__ : List[Any] =type_sequence_label_size __magic_name__ : Union[str, Any] =initializer_range __magic_name__ : int =num_labels __magic_name__ : str =num_choices __magic_name__ : int =relative_attention __magic_name__ : int =position_biased_input __magic_name__ : Union[str, Any] =pos_att_type __magic_name__ : List[str] =scope def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __magic_name__ : Any =None if self.use_input_mask: __magic_name__ : str =random_attention_mask([self.batch_size, self.seq_length] ) __magic_name__ : Union[str, Any] =None if self.use_token_type_ids: __magic_name__ : Any =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) __magic_name__ : str =None __magic_name__ : Optional[int] =None __magic_name__ : Tuple =None if self.use_labels: __magic_name__ : Optional[Any] =ids_tensor([self.batch_size] , self.type_sequence_label_size ) __magic_name__ : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) __magic_name__ : str =DebertaVaConfig( 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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=__snake_case , ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A__ ( self :int , __snake_case :Any , __snake_case :Union[str, Any] , __snake_case :List[Any] , __snake_case :Tuple , __snake_case :Union[str, Any] , __snake_case :List[str] , __snake_case :List[str] ): '''simple docstring''' __magic_name__ : Optional[Any] =TFDebertaVaModel(config=__snake_case ) __magic_name__ : Tuple ={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids} __magic_name__ : List[str] =[input_ids, input_mask] __magic_name__ : List[str] =model(__snake_case ) __magic_name__ : Union[str, Any] =model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A__ ( self :List[Any] , __snake_case :List[str] , __snake_case :List[str] , __snake_case :Union[str, Any] , __snake_case :List[str] , __snake_case :Optional[Any] , __snake_case :Optional[Any] , __snake_case :Tuple ): '''simple docstring''' __magic_name__ : List[str] =TFDebertaVaForMaskedLM(config=__snake_case ) __magic_name__ : Optional[int] ={ """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } __magic_name__ : int =model(__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def A__ ( self :Any , __snake_case :Tuple , __snake_case :List[str] , __snake_case :int , __snake_case :Any , __snake_case :List[str] , __snake_case :int , __snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =self.num_labels __magic_name__ : Union[str, Any] =TFDebertaVaForSequenceClassification(config=__snake_case ) __magic_name__ : Tuple ={ """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } __magic_name__ : int =model(__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def A__ ( self :Dict , __snake_case :List[Any] , __snake_case :Optional[int] , __snake_case :str , __snake_case :Dict , __snake_case :int , __snake_case :str , __snake_case :Any ): '''simple docstring''' __magic_name__ : Dict =self.num_labels __magic_name__ : Optional[Any] =TFDebertaVaForTokenClassification(config=__snake_case ) __magic_name__ : Optional[Any] ={ """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } __magic_name__ : int =model(__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def A__ ( self :List[str] , __snake_case :str , __snake_case :Dict , __snake_case :int , __snake_case :Dict , __snake_case :Dict , __snake_case :Optional[int] , __snake_case :str ): '''simple docstring''' __magic_name__ : List[str] =TFDebertaVaForQuestionAnswering(config=__snake_case ) __magic_name__ : str ={ """input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids, } __magic_name__ : List[Any] =model(__snake_case ) 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 :int ): '''simple docstring''' __magic_name__ : Optional[int] =self.prepare_config_and_inputs() ( ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ( __magic_name__ ) , ) : Union[str, Any] =config_and_inputs __magic_name__ : List[Any] ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask} return config, inputs_dict @require_tf class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = ( ( TFDebertaVaModel, TFDebertaVaForMaskedLM, TFDebertaVaForQuestionAnswering, TFDebertaVaForSequenceClassification, TFDebertaVaForTokenClassification, ) if is_tf_available() else () ) UpperCamelCase = ( { """feature-extraction""": TFDebertaVaModel, """fill-mask""": TFDebertaVaForMaskedLM, """question-answering""": TFDebertaVaForQuestionAnswering, """text-classification""": TFDebertaVaForSequenceClassification, """token-classification""": TFDebertaVaForTokenClassification, """zero-shot""": TFDebertaVaForSequenceClassification, } if is_tf_available() else {} ) UpperCamelCase = False UpperCamelCase = False def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ : List[str] =TFDebertaVaModelTester(self ) __magic_name__ : Any =ConfigTester(self , config_class=__snake_case , hidden_size=37 ) def A__ ( self :Union[str, Any] ): '''simple docstring''' self.config_tester.run_common_tests() def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : Any =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : str =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__snake_case ) def A__ ( self :str ): '''simple docstring''' __magic_name__ : str =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__snake_case ) def A__ ( self :int ): '''simple docstring''' __magic_name__ : str =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__snake_case ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : List[str] =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__snake_case ) @slow def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Any =TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" ) self.assertIsNotNone(__snake_case ) @require_tf class __A ( unittest.TestCase ): @unittest.skip(reason="""Model not available yet""" ) def A__ ( self :str ): '''simple docstring''' pass @slow def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ : str =TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" ) __magic_name__ : str =tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] ) __magic_name__ : List[str] =tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) __magic_name__ : Tuple =model(__snake_case , attention_mask=__snake_case )[0] __magic_name__ : Any =tf.constant( [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] ) tf.debugging.assert_near(output[:, 1:4, 1:4] , __snake_case , atol=1E-4 )
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import heapq def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : list[list] =[] # 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(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] ) # chosen_vertices = set of chosen vertices __magic_name__ : Tuple =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 __magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0] chosen_vertices.add(lowerCamelCase ) # 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]: __magic_name__ : Tuple =elem[1][1].index(lowerCamelCase ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(lowerCamelCase ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : Optional[int] = {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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def lowerCAmelCase_ ( lowerCamelCase = 1000 ): __magic_name__ : Optional[int] =2**power __magic_name__ : int =0 while n: __magic_name__ , __magic_name__ : List[str] =r + n % 10, n // 10 return r if __name__ == "__main__": print(solution(int(str(input()).strip())))
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UpperCAmelCase_ : int = range(2, 20 + 1) UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)] UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) ) __magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) ) __magic_name__ , __magic_name__ : Tuple =0, 0 __magic_name__ : Optional[Any] =n - i __magic_name__ : Union[str, Any] =memo.get(lowerCamelCase ) if sub_memo is not None: __magic_name__ : int =sub_memo.get(lowerCamelCase ) if jumps is not None and len(lowerCamelCase ) > 0: # find and make the largest jump without going over __magic_name__ : Dict =-1 for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ): if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: __magic_name__ : Optional[Any] =_k break if max_jump >= 0: __magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump] # since the difference between jumps is cached, add c __magic_name__ : Tuple =diff + c for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ): __magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 ) if new_c > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) else: __magic_name__ : str =[] else: __magic_name__ : List[str] ={c: []} __magic_name__ : List[str] =sub_memo if dn >= max_dn or c + diff >= base[k]: return diff, dn if k > ks[0]: while True: # keep doing smaller jumps __magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped if dn >= max_dn or c + diff >= base[k]: break else: # would be too small a jump, just compute sequential terms instead __magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped __magic_name__ : Tuple =sub_memo[c] # keep jumps sorted by # of terms skipped __magic_name__ : List[Any] =0 while j < len(lowerCamelCase ): if jumps[j][1] > dn: break j += 1 # cache the jump for this value digitsum(b) and c sub_memo[c].insert(lowerCamelCase , (diff, dn, k) ) return (diff, dn) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): if i >= n: return 0, i if k > len(lowerCamelCase ): a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] ) # note: a_i -> b * 10^k + c # ds_b -> digitsum(b) # ds_c -> digitsum(c) __magic_name__ : Tuple =i __magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0 for j in range(len(lowerCamelCase ) ): if j >= k: ds_b += a_i[j] else: ds_c += a_i[j] while i < n: i += 1 __magic_name__ : Optional[Any] =ds_c + ds_b diff += addend __magic_name__ : str =0 for j in range(lowerCamelCase ): __magic_name__ : int =a_i[j] + addend __magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 ) ds_c += a_i[j] if addend > 0: break if addend > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) return diff, i - start_i def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): for j in range(lowerCamelCase , len(lowerCamelCase ) ): __magic_name__ : Tuple =digits[j] + addend if s >= 10: __magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 ) __magic_name__ : int =addend // 10 + quotient else: __magic_name__ : Dict =s __magic_name__ : Any =addend // 10 if addend == 0: break while addend > 0: __magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 ) digits.append(lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase = 10**15 ): __magic_name__ : List[str] =[1] __magic_name__ : str =1 __magic_name__ : str =0 while True: __magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase ) dn += terms_jumped if dn == n - i: break __magic_name__ : int =0 for j in range(len(lowerCamelCase ) ): a_n += digits[j] * 10**j return a_n if __name__ == "__main__": print(F"""{solution() = }""")
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from typing import Optional from .. import Features, NamedSplit from ..packaged_modules.text.text import Text from ..utils.typing import NestedDataStructureLike, PathLike from .abc import AbstractDatasetReader class __A ( UpperCamelCase__ ): def __init__( self :Dict , __snake_case :NestedDataStructureLike[PathLike] , __snake_case :Optional[NamedSplit] = None , __snake_case :Optional[Features] = None , __snake_case :str = None , __snake_case :bool = False , __snake_case :bool = False , __snake_case :Optional[int] = None , **__snake_case :Optional[Any] , ): '''simple docstring''' super().__init__( __snake_case , split=__snake_case , features=__snake_case , cache_dir=__snake_case , keep_in_memory=__snake_case , streaming=__snake_case , num_proc=__snake_case , **__snake_case , ) __magic_name__ : str =path_or_paths if isinstance(__snake_case , __snake_case ) else {self.split: path_or_paths} __magic_name__ : Union[str, Any] =Text( cache_dir=__snake_case , data_files=__snake_case , features=__snake_case , **__snake_case , ) def A__ ( self :str ): '''simple docstring''' if self.streaming: __magic_name__ : Tuple =self.builder.as_streaming_dataset(split=self.split ) # Build regular (map-style) dataset else: __magic_name__ : List[Any] =None __magic_name__ : int =None __magic_name__ : Optional[Any] =None __magic_name__ : Optional[int] =None self.builder.download_and_prepare( download_config=__snake_case , download_mode=__snake_case , verification_mode=__snake_case , base_path=__snake_case , num_proc=self.num_proc , ) __magic_name__ : Dict =self.builder.as_dataset( split=self.split , verification_mode=__snake_case , in_memory=self.keep_in_memory ) return dataset
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from typing import List from .keymap import KEYMAP, get_character def lowerCAmelCase_ ( lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] ) handle += [key] setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator def lowerCAmelCase_ ( *lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] ) handle += keys setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator class __A ( UpperCamelCase__ ): def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ): '''simple docstring''' __magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case ) if not hasattr(__snake_case , """key_handler""" ): setattr(__snake_case , """key_handler""" , {} ) setattr(__snake_case , """handle_input""" , KeyHandler.handle_input ) for value in attrs.values(): __magic_name__ : int =getattr(__snake_case , """handle_key""" , [] ) for key in handled_keys: __magic_name__ : List[str] =value return new_cls @staticmethod def A__ ( cls :Optional[int] ): '''simple docstring''' __magic_name__ : Union[str, Any] =get_character() if char != KEYMAP["undefined"]: __magic_name__ : Optional[int] =ord(__snake_case ) __magic_name__ : int =cls.key_handler.get(__snake_case ) if handler: __magic_name__ : Dict =char return handler(cls ) else: return None def lowerCAmelCase_ ( cls ): return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
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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, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_vision_available, logging if is_vision_available(): import PIL UpperCAmelCase_ : Tuple = logging.get_logger(__name__) class __A ( UpperCamelCase__ ): UpperCamelCase = ["""pixel_values"""] def __init__( self :Union[str, Any] , __snake_case :bool = True , __snake_case :Dict[str, int] = None , __snake_case :float = None , __snake_case :PILImageResampling = PILImageResampling.BILINEAR , __snake_case :bool = True , __snake_case :Union[int, float] = 1 / 2_55 , __snake_case :bool = True , __snake_case :Optional[Union[float, List[float]]] = None , __snake_case :Optional[Union[float, List[float]]] = None , **__snake_case :str , ): '''simple docstring''' super().__init__(**__snake_case ) __magic_name__ : int =size if size is not None else {"""shortest_edge""": 3_84} __magic_name__ : str =get_size_dict(__snake_case , default_to_square=__snake_case ) __magic_name__ : Optional[int] =do_resize __magic_name__ : str =size # Default value set here for backwards compatibility where the value in config is None __magic_name__ : str =crop_pct if crop_pct is not None else 2_24 / 2_56 __magic_name__ : List[str] =resample __magic_name__ : Any =do_rescale __magic_name__ : Optional[int] =rescale_factor __magic_name__ : Tuple =do_normalize __magic_name__ : int =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN __magic_name__ : Optional[int] =image_std if image_std is not None else IMAGENET_STANDARD_STD def A__ ( self :Optional[Any] , __snake_case :np.ndarray , __snake_case :Dict[str, int] , __snake_case :float , __snake_case :PILImageResampling = PILImageResampling.BICUBIC , __snake_case :Optional[Union[str, ChannelDimension]] = None , **__snake_case :int , ): '''simple docstring''' __magic_name__ : Tuple =get_size_dict(__snake_case , default_to_square=__snake_case ) if "shortest_edge" not in size: raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" ) __magic_name__ : Dict =size["""shortest_edge"""] if shortest_edge < 3_84: # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct __magic_name__ : Optional[int] =int(shortest_edge / crop_pct ) __magic_name__ : str =get_resize_output_image_size(__snake_case , size=__snake_case , default_to_square=__snake_case ) __magic_name__ : int =resize(image=__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case ) # then crop to (shortest_edge, shortest_edge) return center_crop(image=__snake_case , size=(shortest_edge, shortest_edge) , data_format=__snake_case , **__snake_case ) else: # warping (no cropping) when evaluated at 384 or larger return resize( __snake_case , size=(shortest_edge, shortest_edge) , resample=__snake_case , data_format=__snake_case , **__snake_case ) def A__ ( self :int , __snake_case :np.ndarray , __snake_case :Union[int, float] , __snake_case :Optional[Union[str, ChannelDimension]] = None , **__snake_case :Any , ): '''simple docstring''' return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case ) def A__ ( self :List[str] , __snake_case :np.ndarray , __snake_case :Union[float, List[float]] , __snake_case :Union[float, List[float]] , __snake_case :Optional[Union[str, ChannelDimension]] = None , **__snake_case :List[Any] , ): '''simple docstring''' return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case ) def A__ ( self :List[str] , __snake_case :ImageInput , __snake_case :bool = None , __snake_case :Dict[str, int] = None , __snake_case :float = None , __snake_case :PILImageResampling = None , __snake_case :bool = None , __snake_case :float = None , __snake_case :bool = None , __snake_case :Optional[Union[float, List[float]]] = None , __snake_case :Optional[Union[float, List[float]]] = None , __snake_case :Optional[Union[str, TensorType]] = None , __snake_case :ChannelDimension = ChannelDimension.FIRST , **__snake_case :Union[str, Any] , ): '''simple docstring''' __magic_name__ : str =do_resize if do_resize is not None else self.do_resize __magic_name__ : Optional[int] =crop_pct if crop_pct is not None else self.crop_pct __magic_name__ : Optional[int] =resample if resample is not None else self.resample __magic_name__ : Union[str, Any] =do_rescale if do_rescale is not None else self.do_rescale __magic_name__ : Optional[Any] =rescale_factor if rescale_factor is not None else self.rescale_factor __magic_name__ : Tuple =do_normalize if do_normalize is not None else self.do_normalize __magic_name__ : Union[str, Any] =image_mean if image_mean is not None else self.image_mean __magic_name__ : Optional[Any] =image_std if image_std is not None else self.image_std __magic_name__ : Optional[int] =size if size is not None else self.size __magic_name__ : int =get_size_dict(__snake_case , default_to_square=__snake_case ) __magic_name__ : Optional[int] =make_list_of_images(__snake_case ) if not valid_images(__snake_case ): 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 or resample is None: raise ValueError("""Size and resample must be specified if do_resize is True.""" ) if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None: raise ValueError("""crop_pct must be specified if size < 384.""" ) 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. __magic_name__ : Union[str, Any] =[to_numpy_array(__snake_case ) for image in images] if do_resize: __magic_name__ : List[str] =[self.resize(image=__snake_case , size=__snake_case , crop_pct=__snake_case , resample=__snake_case ) for image in images] if do_rescale: __magic_name__ : List[str] =[self.rescale(image=__snake_case , scale=__snake_case ) for image in images] if do_normalize: __magic_name__ : Dict =[self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case ) for image in images] __magic_name__ : int =[to_channel_dimension_format(__snake_case , __snake_case ) for image in images] __magic_name__ : List[str] ={"""pixel_values""": images} return BatchFeature(data=__snake_case , tensor_type=__snake_case )
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import os import jsonlines import numpy as np from tqdm import tqdm UpperCAmelCase_ : Dict = 2048 UpperCAmelCase_ : int = 4096 UpperCAmelCase_ : Any = 42 UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false") UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4} def lowerCAmelCase_ ( lowerCamelCase ): def choose_first(lowerCamelCase , lowerCamelCase=False ): assert isinstance(lowerCamelCase , lowerCamelCase ) if len(lowerCamelCase ) == 1: __magic_name__ : List[str] =answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: __magic_name__ : Tuple ={k: [a[k]] for k in a} if len(a["""start_token"""] ) > 0: break return a __magic_name__ : str ={"""id""": example["""id"""]} __magic_name__ : List[Any] =example["""annotations"""] __magic_name__ : List[str] =annotation["""yes_no_answer"""] if 0 in yes_no_answer or 1 in yes_no_answer: __magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""] __magic_name__ : List[str] =[] __magic_name__ : Dict =[] __magic_name__ : str =["""<cls>"""] else: __magic_name__ : Tuple =["""short"""] __magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] ) if len(out["""start_token"""] ) == 0: # answer will be long if short is not available __magic_name__ : Tuple =["""long"""] __magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase ) __magic_name__ : List[Any] =[] answer.update(lowerCamelCase ) # disregard some samples if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]: __magic_name__ : Any =True else: __magic_name__ : List[str] =False __magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""] if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ): raise ValueError("""Issue in ID""" , example["""id"""] ) return answer def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase ) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : Any =example["""document"""]["""tokens"""] __magic_name__ : str =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": -100, # ignore index in cross-entropy "end_token": -100, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples __magic_name__ : Dict =["""start_token""", """end_token"""] answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10 __magic_name__ : Tuple =example["""document"""]["""tokens"""] __magic_name__ : Optional[int] =answer["""start_token"""] __magic_name__ : List[Any] =answer["""end_token"""] __magic_name__ : Optional[Any] =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 __magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] ) # checking above code if assertion: __magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] ) if new != old: print("""ID:""" , example["""id"""] ) print("""New:""" , lowerCamelCase , end="""\n""" ) print("""Old:""" , lowerCamelCase , end="""\n\n""" ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ): # overlap will be of doc_stride - q_len __magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase ) __magic_name__ : Union[str, Any] =out["""answer"""] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } __magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids __magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : List[str] =[] __magic_name__ : int =[] __magic_name__ : List[str] =input_ids[:q_len] __magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Tuple =input_ids[i:end_index] inputs.append(q_indices + slice ) category.append(answer["""category"""][0] ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-100] * len(lowerCamelCase ), "end_token": [-100] * len(lowerCamelCase ), "category": category, }, } __magic_name__ : int =out["""context"""].split() __magic_name__ : Any =splitted_context[answer["""end_token"""]] __magic_name__ : str =len( tokenizer( """ """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids ) __magic_name__ : Optional[int] =len( tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids ) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token __magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids ) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 __magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive __magic_name__ : Dict =answer["""start_token"""] __magic_name__ : int =answer["""end_token"""] if assertion: __magic_name__ : Any =tokenizer.decode(lowerCamelCase ) if answer["span"] != new: print("""ISSUE IN TOKENIZATION""" ) print("""OLD:""" , answer["""span"""] ) print("""NEW:""" , lowerCamelCase , end="""\n\n""" ) if len(lowerCamelCase ) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } __magic_name__ : Any =input_ids[:q_len] __magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) __magic_name__ : Any =[] __magic_name__ : List[str] =[] __magic_name__ : List[str] =[] __magic_name__ : str =[] # null, yes, no, long, short for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Dict =input_ids[i:end_index] inputs.append(q_indices + slice ) assert len(inputs[-1] ) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: __magic_name__ : List[Any] =start_token - i + q_len __magic_name__ : Optional[Any] =end_token - i + q_len answers_category.append(answer["""category"""][0] ) # ["short"] -> "short" else: __magic_name__ : Optional[Any] =-100 __magic_name__ : Optional[Any] =-100 answers_category.append("""null""" ) __magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1] answers_start_token.append(lowerCamelCase ) answers_end_token.append(lowerCamelCase ) if assertion: if new != old and new != [tokenizer.cls_token_id]: print("""ISSUE in strided for ID:""" , example["""id"""] ) print("""New:""" , tokenizer.decode(lowerCamelCase ) ) print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ): __magic_name__ : List[Any] =get_strided_contexts_and_ans( lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , ) return example def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): with jsonlines.open(lowerCamelCase , """a""" ) as writer: for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ): __magic_name__ : int =example["""labels"""] for ids, start, end, cat in zip( example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { """input_ids""": ids, """start_token""": start, """end_token""": end, """category""": CATEGORY_MAPPING[cat], } ) if __name__ == "__main__": from datasets import load_dataset from transformers import BigBirdTokenizer UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions") UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"] UpperCAmelCase_ : Optional[int] = { "tokenizer": tokenizer, "doc_stride": DOC_STRIDE, "max_length": MAX_LENGTH, "assertion": False, } UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs) UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"]) print(data) np.random.seed(SEED) UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl" save_to_disk(data, file_name=cache_file_name)
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import math UpperCAmelCase_ : List[Any] = 10 UpperCAmelCase_ : Tuple = 7 UpperCAmelCase_ : Any = BALLS_PER_COLOUR * NUM_COLOURS def lowerCAmelCase_ ( lowerCamelCase = 20 ): __magic_name__ : Union[str, Any] =math.comb(lowerCamelCase , lowerCamelCase ) __magic_name__ : Tuple =math.comb(NUM_BALLS - BALLS_PER_COLOUR , lowerCamelCase ) __magic_name__ : Any =NUM_COLOURS * (1 - missing_colour / total) return F"{result:.9f}" if __name__ == "__main__": print(solution(20))
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Tuple = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json", "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json", # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl } class __A ( UpperCamelCase__ ): UpperCamelCase = """xlm-roberta-xl""" def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ): '''simple docstring''' super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case ) __magic_name__ : List[str] =vocab_size __magic_name__ : List[str] =hidden_size __magic_name__ : Union[str, Any] =num_hidden_layers __magic_name__ : Any =num_attention_heads __magic_name__ : Any =hidden_act __magic_name__ : List[str] =intermediate_size __magic_name__ : Any =hidden_dropout_prob __magic_name__ : Union[str, Any] =attention_probs_dropout_prob __magic_name__ : Any =max_position_embeddings __magic_name__ : Any =type_vocab_size __magic_name__ : List[str] =initializer_range __magic_name__ : Optional[int] =layer_norm_eps __magic_name__ : Dict =position_embedding_type __magic_name__ : Any =use_cache __magic_name__ : Dict =classifier_dropout class __A ( UpperCamelCase__ ): @property def A__ ( self :Dict ): '''simple docstring''' if self.task == "multiple-choice": __magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""} else: __magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ] )
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def lowerCAmelCase_ ( lowerCamelCase ): if not numbers: return 0 if not isinstance(lowerCamelCase , (list, tuple) ) or not all( isinstance(lowerCamelCase , lowerCamelCase ) for number in numbers ): raise ValueError("""numbers must be an iterable of integers""" ) __magic_name__ : List[Any] =numbers[0] for i in range(1 , len(lowerCamelCase ) ): # update the maximum and minimum subarray products __magic_name__ : Dict =numbers[i] if number < 0: __magic_name__ , __magic_name__ : str =min_till_now, max_till_now __magic_name__ : Union[str, Any] =max(lowerCamelCase , max_till_now * number ) __magic_name__ : Optional[Any] =min(lowerCamelCase , min_till_now * number ) # update the maximum product found till now __magic_name__ : Union[str, Any] =max(lowerCamelCase , lowerCamelCase ) return max_prod
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from pathlib import Path import fire from tqdm import tqdm def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ): try: import datasets except (ModuleNotFoundError, ImportError): raise ImportError("""run pip install datasets""" ) __magic_name__ : Dict =F"{src_lang}-{tgt_lang}" print(F"Converting {dataset}-{pair}" ) __magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase ) if save_dir is None: __magic_name__ : Optional[int] =F"{dataset}-{pair}" __magic_name__ : int =Path(lowerCamelCase ) save_dir.mkdir(exist_ok=lowerCamelCase ) for split in ds.keys(): print(F"Splitting {split} with {ds[split].num_rows} records" ) # to save to val.source, val.target like summary datasets __magic_name__ : Dict ="""val""" if split == """validation""" else split __magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" ) __magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" ) __magic_name__ : Optional[Any] =src_path.open("""w+""" ) __magic_name__ : List[Any] =tgt_path.open("""w+""" ) # reader is the bottleneck so writing one record at a time doesn't slow things down for x in tqdm(ds[split] ): __magic_name__ : str =x["""translation"""] src_fp.write(ex[src_lang] + """\n""" ) tgt_fp.write(ex[tgt_lang] + """\n""" ) print(F"Saved {dataset} dataset to {save_dir}" ) if __name__ == "__main__": fire.Fire(download_wmt_dataset)
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from typing import List, Optional, TypeVar from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets from .dataset_dict import DatasetDict, IterableDatasetDict from .info import DatasetInfo from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets from .splits import NamedSplit from .utils import logging from .utils.py_utils import Literal UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : Dict = TypeVar("DatasetType", Dataset, IterableDataset) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = "first_exhausted" , ): from .arrow_dataset import Dataset from .iterable_dataset import IterableDataset if not datasets: raise ValueError("""Unable to interleave an empty list of datasets.""" ) for i, dataset in enumerate(lowerCamelCase ): if not isinstance(lowerCamelCase , (Dataset, IterableDataset) ): if isinstance(lowerCamelCase , (DatasetDict, IterableDatasetDict) ): if not dataset: raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} " """is an empty dataset dictionary.""" ) raise ValueError( F"Dataset at position {i} has at least one split: {list(lowerCamelCase )}\n" F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowerCamelCase ) )}']" ) raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase ).__name__}." ) if i == 0: __magic_name__ , __magic_name__ : List[str] =( (Dataset, IterableDataset) if isinstance(lowerCamelCase , lowerCamelCase ) else (IterableDataset, Dataset) ) elif not isinstance(lowerCamelCase , lowerCamelCase ): raise ValueError( F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." ) if stopping_strategy not in ["first_exhausted", "all_exhausted"]: raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." ) if dataset_type is Dataset: return _interleave_map_style_datasets( lowerCamelCase , lowerCamelCase , lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , stopping_strategy=lowerCamelCase ) else: return _interleave_iterable_datasets( lowerCamelCase , lowerCamelCase , lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , stopping_strategy=lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = 0 , ): if not dsets: raise ValueError("""Unable to concatenate an empty list of datasets.""" ) for i, dataset in enumerate(lowerCamelCase ): if not isinstance(lowerCamelCase , (Dataset, IterableDataset) ): if isinstance(lowerCamelCase , (DatasetDict, IterableDatasetDict) ): if not dataset: raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} " """is an empty dataset dictionary.""" ) raise ValueError( F"Dataset at position {i} has at least one split: {list(lowerCamelCase )}\n" F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowerCamelCase ) )}']" ) raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase ).__name__}." ) if i == 0: __magic_name__ , __magic_name__ : List[Any] =( (Dataset, IterableDataset) if isinstance(lowerCamelCase , lowerCamelCase ) else (IterableDataset, Dataset) ) elif not isinstance(lowerCamelCase , lowerCamelCase ): raise ValueError( F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." ) if dataset_type is Dataset: return _concatenate_map_style_datasets(lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , axis=lowerCamelCase ) else: return _concatenate_iterable_datasets(lowerCamelCase , info=lowerCamelCase , split=lowerCamelCase , axis=lowerCamelCase )
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from __future__ import annotations from fractions import Fraction def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): return ( num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =[] __magic_name__ : List[Any] =11 __magic_name__ : Tuple =int("""1""" + """0""" * digit_len ) for num in range(lowerCamelCase , lowerCamelCase ): while den <= 99: if (num != den) and (num % 10 == den // 10) and (den % 10 != 0): if is_digit_cancelling(lowerCamelCase , lowerCamelCase ): solutions.append(F"{num}/{den}" ) den += 1 num += 1 __magic_name__ : List[str] =10 return solutions def lowerCAmelCase_ ( lowerCamelCase = 2 ): __magic_name__ : str =1.0 for fraction in fraction_list(lowerCamelCase ): __magic_name__ : int =Fraction(lowerCamelCase ) result *= frac.denominator / frac.numerator return int(lowerCamelCase ) if __name__ == "__main__": print(solution())
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from collections.abc import Callable from math import pi, sqrt from random import uniform from statistics import mean def lowerCAmelCase_ ( lowerCamelCase ): # A local function to see if a dot lands in the circle. def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool: __magic_name__ : Dict =sqrt((x**2) + (y**2) ) # Our circle has a radius of 1, so a distance # greater than 1 would land outside the circle. return distance_from_centre <= 1 # The proportion of guesses that landed in the circle __magic_name__ : Union[str, Any] =mean( int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) ) for _ in range(lowerCamelCase ) ) # The ratio of the area for circle to square is pi/4. __magic_name__ : List[Any] =proportion * 4 print(F"The estimated value of pi is {pi_estimate}" ) print(F"The numpy value of pi is {pi}" ) print(F"The total error is {abs(pi - pi_estimate )}" ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ): return mean( function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ): def identity_function(lowerCamelCase ) -> float: return x __magic_name__ : Optional[int] =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) __magic_name__ : str =(max_value * max_value - min_value * min_value) / 2 print("""******************""" ) print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {expected_value}" ) print(F"Total error is {abs(estimated_value - expected_value )}" ) print("""******************""" ) def lowerCAmelCase_ ( lowerCamelCase ): def function_to_integrate(lowerCamelCase ) -> float: return sqrt(4.0 - x * x ) __magic_name__ : Dict =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , 0.0 , 2.0 ) print("""******************""" ) print("""Estimating pi using area_under_curve_estimator""" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {pi}" ) print(F"Total error is {abs(estimated_value - pi )}" ) print("""******************""" ) if __name__ == "__main__": import doctest doctest.testmod()
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from collections.abc import Callable from math import pi, sqrt from random import uniform from statistics import mean def lowerCAmelCase_ ( lowerCamelCase ): # A local function to see if a dot lands in the circle. def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool: __magic_name__ : Dict =sqrt((x**2) + (y**2) ) # Our circle has a radius of 1, so a distance # greater than 1 would land outside the circle. return distance_from_centre <= 1 # The proportion of guesses that landed in the circle __magic_name__ : Union[str, Any] =mean( int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) ) for _ in range(lowerCamelCase ) ) # The ratio of the area for circle to square is pi/4. __magic_name__ : List[Any] =proportion * 4 print(F"The estimated value of pi is {pi_estimate}" ) print(F"The numpy value of pi is {pi}" ) print(F"The total error is {abs(pi - pi_estimate )}" ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ): return mean( function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ): def identity_function(lowerCamelCase ) -> float: return x __magic_name__ : Optional[int] =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) __magic_name__ : str =(max_value * max_value - min_value * min_value) / 2 print("""******************""" ) print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {expected_value}" ) print(F"Total error is {abs(estimated_value - expected_value )}" ) print("""******************""" ) def lowerCAmelCase_ ( lowerCamelCase ): def function_to_integrate(lowerCamelCase ) -> float: return sqrt(4.0 - x * x ) __magic_name__ : Dict =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , 0.0 , 2.0 ) print("""******************""" ) print("""Estimating pi using area_under_curve_estimator""" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {pi}" ) print(F"Total error is {abs(estimated_value - pi )}" ) print("""******************""" ) if __name__ == "__main__": import doctest doctest.testmod()
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from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Features, Sequence, Value from .base import TaskTemplate @dataclass(frozen=UpperCamelCase__ ) class __A ( UpperCamelCase__ ): # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization UpperCamelCase = field(default="""question-answering-extractive""" , metadata={"""include_in_asdict_even_if_is_default""": True} ) UpperCamelCase = Features({"""question""": Value("""string""" ), """context""": Value("""string""" )} ) UpperCamelCase = Features( { """answers""": Sequence( { """text""": Value("""string""" ), """answer_start""": Value("""int32""" ), } ) } ) UpperCamelCase = "question" UpperCamelCase = "context" UpperCamelCase = "answers" @property def A__ ( self :int ): '''simple docstring''' return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
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import os from typing import Dict, List, Union import tensorflow as tf from keras_nlp.tokenizers import BytePairTokenizer from tensorflow_text import pad_model_inputs from .tokenization_gpta import GPTaTokenizer class __A ( tf.keras.layers.Layer ): def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ): '''simple docstring''' super().__init__() __magic_name__ : Optional[int] =pad_token_id __magic_name__ : List[Any] =max_length __magic_name__ : Dict =vocab __magic_name__ : int =merges __magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case ) @classmethod def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ): '''simple docstring''' __magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()] __magic_name__ : str =tokenizer.get_vocab() return cls(__snake_case , __snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ): '''simple docstring''' __magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case ) return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Optional[Any] , __snake_case :List[Any] ): '''simple docstring''' return cls(**__snake_case ) def A__ ( self :Union[str, Any] ): '''simple docstring''' return { "vocab": self.vocab, "merges": self.merges, "max_length": self.max_length, "pad_token_id": self.pad_token_id, } def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ): '''simple docstring''' __magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case ) __magic_name__ : Tuple =tf.ones_like(__snake_case ) if self.pad_token_id is not None: # pad the tokens up to max length __magic_name__ : Tuple =max_length if max_length is not None else self.max_length if max_length is not None: __magic_name__ , __magic_name__ : Tuple =pad_model_inputs( __snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id ) return {"attention_mask": attention_mask, "input_ids": input_ids}
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import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline from diffusers.pipelines.shap_e import ShapERenderer from diffusers.utils import load_numpy, slow from diffusers.utils.testing_utils import require_torch_gpu, torch_device from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = ShapEPipeline UpperCamelCase = ["""prompt"""] UpperCamelCase = ["""prompt"""] UpperCamelCase = [ """num_images_per_prompt""", """num_inference_steps""", """generator""", """latents""", """guidance_scale""", """frame_size""", """output_type""", """return_dict""", ] UpperCamelCase = False @property def A__ ( self :Dict ): '''simple docstring''' return 32 @property def A__ ( self :Optional[Any] ): '''simple docstring''' return 32 @property def A__ ( self :Any ): '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self :Optional[int] ): '''simple docstring''' return 8 @property def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Any =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" ) return tokenizer @property def A__ ( self :List[Any] ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Any =CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) return CLIPTextModelWithProjection(__snake_case ) @property def A__ ( self :List[Any] ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Union[str, Any] ={ """num_attention_heads""": 2, """attention_head_dim""": 16, """embedding_dim""": self.time_input_dim, """num_embeddings""": 32, """embedding_proj_dim""": self.text_embedder_hidden_size, """time_embed_dim""": self.time_embed_dim, """num_layers""": 1, """clip_embed_dim""": self.time_input_dim * 2, """additional_embeddings""": 0, """time_embed_act_fn""": """gelu""", """norm_in_type""": """layer""", """encoder_hid_proj_type""": None, """added_emb_type""": None, } __magic_name__ : Optional[Any] =PriorTransformer(**__snake_case ) return model @property def A__ ( self :int ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Optional[int] ={ """param_shapes""": ( (self.renderer_dim, 93), (self.renderer_dim, 8), (self.renderer_dim, 8), (self.renderer_dim, 8), ), """d_latent""": self.time_input_dim, """d_hidden""": self.renderer_dim, """n_output""": 12, """background""": ( 0.1, 0.1, 0.1, ), } __magic_name__ : Any =ShapERenderer(**__snake_case ) return model def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : List[str] =self.dummy_prior __magic_name__ : str =self.dummy_text_encoder __magic_name__ : Dict =self.dummy_tokenizer __magic_name__ : Union[str, Any] =self.dummy_renderer __magic_name__ : List[str] =HeunDiscreteScheduler( beta_schedule="""exp""" , num_train_timesteps=10_24 , prediction_type="""sample""" , use_karras_sigmas=__snake_case , clip_sample=__snake_case , clip_sample_range=1.0 , ) __magic_name__ : Union[str, Any] ={ """prior""": prior, """text_encoder""": text_encoder, """tokenizer""": tokenizer, """renderer""": renderer, """scheduler""": scheduler, } return components def A__ ( self :Any , __snake_case :Optional[Any] , __snake_case :Optional[int]=0 ): '''simple docstring''' if str(__snake_case ).startswith("""mps""" ): __magic_name__ : Union[str, Any] =torch.manual_seed(__snake_case ) else: __magic_name__ : List[Any] =torch.Generator(device=__snake_case ).manual_seed(__snake_case ) __magic_name__ : str ={ """prompt""": """horse""", """generator""": generator, """num_inference_steps""": 1, """frame_size""": 32, """output_type""": """np""", } return inputs def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : str ="""cpu""" __magic_name__ : str =self.get_dummy_components() __magic_name__ : Optional[int] =self.pipeline_class(**__snake_case ) __magic_name__ : Dict =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : str =pipe(**self.get_dummy_inputs(__snake_case ) ) __magic_name__ : Dict =output.images[0] __magic_name__ : str =image[0, -3:, -3:, -1] assert image.shape == (20, 32, 32, 3) __magic_name__ : Union[str, Any] =np.array( [ 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, 0.00039216, ] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A__ ( self :Optional[int] ): '''simple docstring''' self._test_inference_batch_consistent(batch_sizes=[1, 2] ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Optional[int] =torch_device == """cpu""" __magic_name__ : Optional[int] =True self._test_inference_batch_single_identical( batch_size=2 , test_max_difference=__snake_case , relax_max_difference=__snake_case , ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : int =self.get_dummy_components() __magic_name__ : List[Any] =self.pipeline_class(**__snake_case ) __magic_name__ : int =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : List[Any] =1 __magic_name__ : Optional[int] =2 __magic_name__ : Any =self.get_dummy_inputs(__snake_case ) for key in inputs.keys(): if key in self.batch_params: __magic_name__ : List[str] =batch_size * [inputs[key]] __magic_name__ : List[str] =pipe(**__snake_case , num_images_per_prompt=__snake_case )[0] assert images.shape[0] == batch_size * num_images_per_prompt @slow @require_torch_gpu class __A ( unittest.TestCase ): def A__ ( self :str ): '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Any =load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/test_shap_e_np_out.npy""" ) __magic_name__ : List[Any] =ShapEPipeline.from_pretrained("""openai/shap-e""" ) __magic_name__ : str =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : int =torch.Generator(device=__snake_case ).manual_seed(0 ) __magic_name__ : List[Any] =pipe( """a shark""" , generator=__snake_case , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0] assert images.shape == (20, 64, 64, 3) assert_mean_pixel_difference(__snake_case , __snake_case )
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import math import tensorflow as tf from packaging import version def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype ) __magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase ) return x * tf.tanh(tf.math.softplus(lowerCamelCase ) ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype ) return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) )) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype ) return x * tf.math.sigmoid(coeff * x ) def lowerCAmelCase_ ( lowerCamelCase ): return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ): __magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase ) return a * tf.math.sigmoid(lowerCamelCase ) if version.parse(tf.version.VERSION) >= version.parse("2.4"): def lowerCAmelCase_ ( lowerCamelCase ): return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase ) UpperCAmelCase_ : List[str] = tf.keras.activations.gelu UpperCAmelCase_ : Dict = approximate_gelu_wrap else: UpperCAmelCase_ : Dict = _gelu UpperCAmelCase_ : str = _gelu_new UpperCAmelCase_ : Any = { "gelu": gelu, "gelu_10": gelu_aa, "gelu_fast": gelu_fast, "gelu_new": gelu_new, "glu": glu, "mish": mish, "quick_gelu": quick_gelu, "relu": tf.keras.activations.relu, "sigmoid": tf.keras.activations.sigmoid, "silu": tf.keras.activations.swish, "swish": tf.keras.activations.swish, "tanh": tf.keras.activations.tanh, } def lowerCAmelCase_ ( lowerCamelCase ): if activation_string in ACTaFN: return ACTaFN[activation_string] else: raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
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import warnings from ...utils import logging from .image_processing_segformer import SegformerImageProcessor UpperCAmelCase_ : Tuple = logging.get_logger(__name__) class __A ( UpperCamelCase__ ): def __init__( self :int , *__snake_case :int , **__snake_case :Optional[Any] ): '''simple docstring''' warnings.warn( """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.""" """ Please use SegformerImageProcessor instead.""" , __snake_case , ) super().__init__(*__snake_case , **__snake_case )
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from collections.abc import Sequence def lowerCAmelCase_ ( lowerCamelCase = None ): if nums is None or not nums: raise ValueError("""Input sequence should not be empty""" ) __magic_name__ : str =nums[0] for i in range(1 , len(lowerCamelCase ) ): __magic_name__ : Any =nums[i] __magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase ) return ans if __name__ == "__main__": import doctest doctest.testmod() # Try on a sample input from the user UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip()) UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n] print(max_subsequence_sum(array))
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class __A ( UpperCamelCase__ , UpperCamelCase__ ): UpperCamelCase = 1 @register_to_config def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ): '''simple docstring''' __magic_name__ : Dict =None __magic_name__ : List[str] =None __magic_name__ : str =None def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ): '''simple docstring''' __magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case ) def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ): '''simple docstring''' if self.timesteps is None: raise ValueError( """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score __magic_name__ : int =( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) __magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) __magic_name__ : str =std.flatten() while len(std.shape ) < len(score.shape ): __magic_name__ : List[str] =std.unsqueeze(-1 ) __magic_name__ : Union[str, Any] =-score / std # compute __magic_name__ : Tuple =-1.0 / len(self.timesteps ) __magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) __magic_name__ : Dict =beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): __magic_name__ : Any =beta_t.unsqueeze(-1 ) __magic_name__ : Dict =-0.5 * beta_t * x __magic_name__ : Optional[int] =torch.sqrt(__snake_case ) __magic_name__ : int =drift - diffusion**2 * score __magic_name__ : List[str] =x + drift * dt # add noise __magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype ) __magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__( self :List[Any] ): '''simple docstring''' return self.config.num_train_timesteps
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from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union import pyarrow as pa if TYPE_CHECKING: from .features import FeatureType @dataclass class __A : UpperCamelCase = 42 UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def __call__( self :Union[str, Any] ): '''simple docstring''' return pa.struct({lang: pa.string() for lang in sorted(self.languages )} ) def A__ ( self :List[Any] ): '''simple docstring''' from .features import Value return {k: Value("""string""" ) for k in sorted(self.languages )} @dataclass class __A : UpperCamelCase = None UpperCamelCase = None UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None __magic_name__ : Optional[int] =len(self.languages ) if self.languages else None def __call__( self :List[str] ): '''simple docstring''' return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} ) def A__ ( self :str , __snake_case :str ): '''simple docstring''' __magic_name__ : Optional[int] =set(self.languages ) if self.languages and set(__snake_case ) - lang_set: raise ValueError( f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." ) # Convert dictionary into tuples, splitting out cases where there are # multiple translations for a single language. __magic_name__ : Any =[] for lang, text in translation_dict.items(): if isinstance(__snake_case , __snake_case ): translation_tuples.append((lang, text) ) else: translation_tuples.extend([(lang, el) for el in text] ) # Ensure translations are in ascending order by language code. __magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) ) return {"language": languages, "translation": translations} def A__ ( self :List[Any] ): '''simple docstring''' from .features import Sequence, Value return { "language": Sequence(Value("""string""" ) ), "translation": Sequence(Value("""string""" ) ), }
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from __future__ import annotations def lowerCAmelCase_ ( lowerCamelCase ): # preprocessing the first row for i in range(1 , len(matrix[0] ) ): matrix[0][i] += matrix[0][i - 1] # preprocessing the first column for i in range(1 , len(lowerCamelCase ) ): matrix[i][0] += matrix[i - 1][0] # updating the path cost for current position for i in range(1 , len(lowerCamelCase ) ): for j in range(1 , len(matrix[0] ) ): matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] ) return matrix[-1][-1] if __name__ == "__main__": import doctest doctest.testmod()
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from sklearn.metrics import matthews_corrcoef import datasets UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n" UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n" UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n" @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class __A ( datasets.Metric ): def A__ ( self :List[str] ): '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""int32""" ), """references""": datasets.Value("""int32""" ), } ) , reference_urls=[ """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html""" ] , ) def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ): '''simple docstring''' return { "matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ), }
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def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): if number < 0 or shift_amount < 0: raise ValueError("""both inputs must be positive integers""" ) __magic_name__ : Tuple =str(bin(lowerCamelCase ) ) binary_number += "0" * shift_amount return binary_number def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): if number < 0 or shift_amount < 0: raise ValueError("""both inputs must be positive integers""" ) __magic_name__ : List[Any] =str(bin(lowerCamelCase ) )[2:] if shift_amount >= len(lowerCamelCase ): return "0b0" __magic_name__ : Tuple =binary_number[: len(lowerCamelCase ) - shift_amount] return "0b" + shifted_binary_number def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): if number >= 0: # Get binary representation of positive number __magic_name__ : List[str] ="""0""" + str(bin(lowerCamelCase ) ).strip("""-""" )[2:] else: # Get binary (2's complement) representation of negative number __magic_name__ : str =len(bin(lowerCamelCase )[3:] ) # Find 2's complement of number __magic_name__ : List[Any] =bin(abs(lowerCamelCase ) - (1 << binary_number_length) )[3:] __magic_name__ : Union[str, Any] =( """1""" + """0""" * (binary_number_length - len(lowerCamelCase )) + binary_number ) if shift_amount >= len(lowerCamelCase ): return "0b" + binary_number[0] * len(lowerCamelCase ) return ( "0b" + binary_number[0] * shift_amount + binary_number[: len(lowerCamelCase ) - shift_amount] ) if __name__ == "__main__": import doctest doctest.testmod()
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import importlib import torch import yaml from omegaconf import OmegaConf from taming.models.vqgan import VQModel def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase ) if display: print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) ) return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ): if conf_path is None: __magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml""" __magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase ) __magic_name__ : Tuple =VQModel(**config.model.params ) if ckpt_path is None: __magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt""" __magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase ) if ".ckpt" in ckpt_path: __magic_name__ : Any =sd["""state_dict"""] model.load_state_dict(lowerCamelCase , strict=lowerCamelCase ) model.to(lowerCamelCase ) del sd return model def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase ) print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" ) __magic_name__ : List[Any] =model.decode(lowerCamelCase ) return xrec def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 ) if reload: __magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase ) importlib.reload(lowerCamelCase ) return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls ) def lowerCAmelCase_ ( lowerCamelCase ): if "target" not in config: raise KeyError("""Expected key `target` to instantiate.""" ) return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ): __magic_name__ : str =instantiate_from_config(lowerCamelCase ) if sd is not None: model.load_state_dict(lowerCamelCase ) if gpu: model.cuda() if eval_mode: model.eval() return {"model": model} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): # load the specified checkpoint if ckpt: __magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" ) __magic_name__ : Any =pl_sd["""global_step"""] print(F"loaded model from global step {global_step}." ) else: __magic_name__ : List[Any] ={"""state_dict""": None} __magic_name__ : Optional[Any] =None __magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""] return model, global_step
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, is_vision_available, ) UpperCAmelCase_ : Union[str, Any] = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]} try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : List[str] = ["ViTFeatureExtractor"] UpperCAmelCase_ : Any = ["ViTImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Optional[Any] = [ "VIT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTForImageClassification", "ViTForMaskedImageModeling", "ViTModel", "ViTPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : List[str] = [ "TFViTForImageClassification", "TFViTModel", "TFViTPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Union[str, Any] = [ "FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel", ] if TYPE_CHECKING: from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_vit import ViTFeatureExtractor from .image_processing_vit import ViTImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_vit import ( VIT_PRETRAINED_MODEL_ARCHIVE_LIST, ViTForImageClassification, ViTForMaskedImageModeling, ViTModel, ViTPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel else: import sys UpperCAmelCase_ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class __A ( unittest.TestCase ): def A__ ( self :Tuple ): '''simple docstring''' debug_launcher(test_script.main ) def A__ ( self :Dict ): '''simple docstring''' debug_launcher(test_ops.main )
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UpperCAmelCase_ : dict[str, float] = { "joule": 1.0, "kilojoule": 1000, "megajoule": 1000000, "gigajoule": 1000000000, "wattsecond": 1.0, "watthour": 3600, "kilowatthour": 3600000, "newtonmeter": 1.0, "calorie_nutr": 4186.8, "kilocalorie_nutr": 4186800.00, "electronvolt": 1.602_176_634e-19, "britishthermalunit_it": 1055.05585, "footpound": 1.35_5818, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION: __magic_name__ : Any =( F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n" F"Valid values are: {', '.join(lowerCamelCase )}" ) raise ValueError(lowerCamelCase ) return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type] if __name__ == "__main__": import doctest doctest.testmod()
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UpperCAmelCase_ : Tuple = 0 # The first color of the flag. UpperCAmelCase_ : Any = 1 # The second color of the flag. UpperCAmelCase_ : str = 2 # The third color of the flag. UpperCAmelCase_ : Tuple = (red, white, blue) def lowerCAmelCase_ ( lowerCamelCase ): if not sequence: return [] if len(lowerCamelCase ) == 1: return list(lowerCamelCase ) __magic_name__ : int =0 __magic_name__ : str =len(lowerCamelCase ) - 1 __magic_name__ : Optional[Any] =0 while mid <= high: if sequence[mid] == colors[0]: __magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low] low += 1 mid += 1 elif sequence[mid] == colors[1]: mid += 1 elif sequence[mid] == colors[2]: __magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid] high -= 1 else: __magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values" raise ValueError(lowerCamelCase ) return sequence if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip() UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")] print(F"""{dutch_national_flag_sort(unsorted)}""")
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import argparse import os import re UpperCAmelCase_ : Union[str, Any] = "src/transformers" # Pattern that looks at the indentation in a line. UpperCAmelCase_ : str = re.compile(R"^(\s*)\S") # Pattern that matches `"key":" and puts `key` in group 0. UpperCAmelCase_ : Dict = re.compile(R"^\s*\"([^\"]+)\":") # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. UpperCAmelCase_ : List[Any] = re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]") # Pattern that matches `"key",` and puts `key` in group 0. UpperCAmelCase_ : str = re.compile(R"^\s*\"([^\"]+)\",\s*$") # Pattern that matches any `[stuff]` and puts `stuff` in group 0. UpperCAmelCase_ : Tuple = re.compile(R"\[([^\]]+)\]") def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =_re_indent.search(lowerCamelCase ) return "" if search is None else search.groups()[0] def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase="" , lowerCamelCase=None , lowerCamelCase=None ): __magic_name__ : List[str] =0 __magic_name__ : Dict =code.split("""\n""" ) if start_prompt is not None: while not lines[index].startswith(lowerCamelCase ): index += 1 __magic_name__ : Optional[Any] =["""\n""".join(lines[:index] )] else: __magic_name__ : str =[] # We split into blocks until we get to the `end_prompt` (or the end of the block). __magic_name__ : Dict =[lines[index]] index += 1 while index < len(lowerCamelCase ) and (end_prompt is None or not lines[index].startswith(lowerCamelCase )): if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level: if len(lowerCamelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ): current_block.append(lines[index] ) blocks.append("""\n""".join(lowerCamelCase ) ) if index < len(lowerCamelCase ) - 1: __magic_name__ : Optional[Any] =[lines[index + 1]] index += 1 else: __magic_name__ : Optional[Any] =[] else: blocks.append("""\n""".join(lowerCamelCase ) ) __magic_name__ : Optional[int] =[lines[index]] else: current_block.append(lines[index] ) index += 1 # Adds current block if it's nonempty. if len(lowerCamelCase ) > 0: blocks.append("""\n""".join(lowerCamelCase ) ) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(lowerCamelCase ): blocks.append("""\n""".join(lines[index:] ) ) return blocks def lowerCAmelCase_ ( lowerCamelCase ): def _inner(lowerCamelCase ): return key(lowerCamelCase ).lower().replace("""_""" , """""" ) return _inner def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None ): # If no key is provided, we use a noop. def noop(lowerCamelCase ): return x if key is None: __magic_name__ : Optional[int] =noop # Constants are all uppercase, they go first. __magic_name__ : List[Any] =[obj for obj in objects if key(lowerCamelCase ).isupper()] # Classes are not all uppercase but start with a capital, they go second. __magic_name__ : int =[obj for obj in objects if key(lowerCamelCase )[0].isupper() and not key(lowerCamelCase ).isupper()] # Functions begin with a lowercase, they go last. __magic_name__ : Tuple =[obj for obj in objects if not key(lowerCamelCase )[0].isupper()] __magic_name__ : int =ignore_underscore(lowerCamelCase ) return sorted(lowerCamelCase , key=lowerCamelCase ) + sorted(lowerCamelCase , key=lowerCamelCase ) + sorted(lowerCamelCase , key=lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase ): # This inner function sort imports between [ ]. def _replace(lowerCamelCase ): __magic_name__ : Optional[Any] =match.groups()[0] if "," not in imports: return F"[{imports}]" __magic_name__ : Optional[int] =[part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: __magic_name__ : Tuple =keys[:-1] return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(lowerCamelCase )] ) + "]" __magic_name__ : Any =import_statement.split("""\n""" ) if len(lowerCamelCase ) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. __magic_name__ : Dict =2 if lines[1].strip() == """[""" else 1 __magic_name__ : Optional[int] =[(i, _re_strip_line.search(lowerCamelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )] __magic_name__ : Union[str, Any] =sort_objects(lowerCamelCase , key=lambda lowerCamelCase : x[1] ) __magic_name__ : Dict =[lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] ) elif len(lowerCamelCase ) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1] ) is not None: __magic_name__ : Union[str, Any] =_re_bracket_content.sub(_replace , lines[1] ) else: __magic_name__ : Optional[int] =[part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: __magic_name__ : Tuple =keys[:-1] __magic_name__ : List[Any] =get_indent(lines[1] ) + """, """.join([F"\"{k}\"" for k in sort_objects(lowerCamelCase )] ) return "\n".join(lowerCamelCase ) else: # Finally we have to deal with imports fitting on one line __magic_name__ : Dict =_re_bracket_content.sub(_replace , lowerCamelCase ) return import_statement def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=True ): with open(lowerCamelCase , encoding="""utf-8""" ) as f: __magic_name__ : Union[str, Any] =f.read() if "_import_structure" not in code: return # Blocks of indent level 0 __magic_name__ : Dict =split_code_in_indented_blocks( lowerCamelCase , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" ) # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt). for block_idx in range(1 , len(lowerCamelCase ) - 1 ): # Check if the block contains some `_import_structure`s thingy to sort. __magic_name__ : Optional[Any] =main_blocks[block_idx] __magic_name__ : Optional[Any] =block.split("""\n""" ) # Get to the start of the imports. __magic_name__ : Tuple =0 while line_idx < len(lowerCamelCase ) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: __magic_name__ : List[Any] =len(lowerCamelCase ) else: line_idx += 1 if line_idx >= len(lowerCamelCase ): continue # Ignore beginning and last line: they don't contain anything. __magic_name__ : Dict ="""\n""".join(block_lines[line_idx:-1] ) __magic_name__ : int =get_indent(block_lines[1] ) # Slit the internal block into blocks of indent level 1. __magic_name__ : Dict =split_code_in_indented_blocks(lowerCamelCase , indent_level=lowerCamelCase ) # We have two categories of import key: list or _import_structure[key].append/extend __magic_name__ : str =_re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or just comments. __magic_name__ : Optional[int] =[(pattern.search(lowerCamelCase ).groups()[0] if pattern.search(lowerCamelCase ) is not None else None) for b in internal_blocks] # We only sort the lines with a key. __magic_name__ : List[Any] =[(i, key) for i, key in enumerate(lowerCamelCase ) if key is not None] __magic_name__ : Dict =[x[0] for x in sorted(lowerCamelCase , key=lambda lowerCamelCase : x[1] )] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. __magic_name__ : Any =0 __magic_name__ : List[Any] =[] for i in range(len(lowerCamelCase ) ): if keys[i] is None: reorderded_blocks.append(internal_blocks[i] ) else: __magic_name__ : Optional[Any] =sort_objects_in_import(internal_blocks[sorted_indices[count]] ) reorderded_blocks.append(lowerCamelCase ) count += 1 # And we put our main block back together with its first and last line. __magic_name__ : Union[str, Any] ="""\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] ) if code != "\n".join(lowerCamelCase ): if check_only: return True else: print(F"Overwriting {file}." ) with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f: f.write("""\n""".join(lowerCamelCase ) ) def lowerCAmelCase_ ( lowerCamelCase=True ): __magic_name__ : Union[str, Any] =[] for root, _, files in os.walk(lowerCamelCase ): if "__init__.py" in files: __magic_name__ : Dict =sort_imports(os.path.join(lowerCamelCase , """__init__.py""" ) , check_only=lowerCamelCase ) if result: __magic_name__ : Any =[os.path.join(lowerCamelCase , """__init__.py""" )] if len(lowerCamelCase ) > 0: raise ValueError(F"Would overwrite {len(lowerCamelCase )} files, run `make style`." ) if __name__ == "__main__": UpperCAmelCase_ : Optional[int] = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") UpperCAmelCase_ : int = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class __A ( UpperCamelCase__ , UpperCamelCase__ ): UpperCamelCase = 1 @register_to_config def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ): '''simple docstring''' __magic_name__ : Dict =None __magic_name__ : List[str] =None __magic_name__ : str =None def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ): '''simple docstring''' __magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case ) def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ): '''simple docstring''' if self.timesteps is None: raise ValueError( """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score __magic_name__ : int =( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) __magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) __magic_name__ : str =std.flatten() while len(std.shape ) < len(score.shape ): __magic_name__ : List[str] =std.unsqueeze(-1 ) __magic_name__ : Union[str, Any] =-score / std # compute __magic_name__ : Tuple =-1.0 / len(self.timesteps ) __magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) __magic_name__ : Dict =beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): __magic_name__ : Any =beta_t.unsqueeze(-1 ) __magic_name__ : Dict =-0.5 * beta_t * x __magic_name__ : Optional[int] =torch.sqrt(__snake_case ) __magic_name__ : int =drift - diffusion**2 * score __magic_name__ : List[str] =x + drift * dt # add noise __magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype ) __magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__( self :List[Any] ): '''simple docstring''' return self.config.num_train_timesteps
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1
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #################################################################################################### # # Note: If when running this conversion script you're getting an exception: # ModuleNotFoundError: No module named 'megatron.model.enums' # you need to tell python where to find the clone of Megatron-LM, e.g.: # # cd /tmp # git clone https://github.com/NVIDIA/Megatron-LM # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ... # # if you already have it cloned elsewhere, simply adjust the path to the existing path # # If the training was done using a Megatron-LM fork, e.g., # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one # in your path, i.e., /path/to/Megatron-DeepSpeed/ # import argparse import os import re import zipfile import torch from transformers import AutoTokenizer, GPTaConfig def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=0 ): # Format the message. if name is None: __magic_name__ : List[str] =None else: __magic_name__ : Optional[Any] =""".""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}""" __magic_name__ : Optional[int] =fmt.format(lowerCamelCase ) # Print and recurse (if needed). if isinstance(lowerCamelCase , lowerCamelCase ): if msg is not None: print(lowerCamelCase ) for k in val.keys(): recursive_print(lowerCamelCase , val[k] , spaces + 2 ) elif isinstance(lowerCamelCase , torch.Tensor ): print(lowerCamelCase , """:""" , val.size() ) else: print(lowerCamelCase , """:""" , lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] # for compatibility with later versions of NVIDIA Megatron-LM. # The inverse operation is performed inside Megatron-LM to read checkpoints: # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 # If param is the weight tensor of the self-attention block, the returned tensor # will have to be transposed one more time to be read by HuggingFace GPT2. __magic_name__ : Union[str, Any] =param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] __magic_name__ : int =(num_heads, hidden_size, num_splits) + input_shape[1:] __magic_name__ : List[str] =param.view(*lowerCamelCase ) __magic_name__ : List[Any] =param.transpose(0 , 2 ) __magic_name__ : int =param.transpose(1 , 2 ).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] __magic_name__ : Any =(num_heads, num_splits, hidden_size) + input_shape[1:] __magic_name__ : int =param.view(*lowerCamelCase ) __magic_name__ : str =param.transpose(0 , 1 ).contiguous() __magic_name__ : List[Any] =param.view(*lowerCamelCase ) return param def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): # The converted output model. __magic_name__ : Optional[int] ={} # old versions did not store training args __magic_name__ : Any =input_state_dict.get("""args""" , lowerCamelCase ) if ds_args is not None: # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint # from pprint import pprint # pprint(vars(ds_args)) __magic_name__ : List[str] =ds_args.padded_vocab_size __magic_name__ : List[str] =ds_args.max_position_embeddings __magic_name__ : List[Any] =ds_args.hidden_size __magic_name__ : Union[str, Any] =ds_args.num_layers __magic_name__ : Tuple =ds_args.num_attention_heads __magic_name__ : List[str] =ds_args.ffn_hidden_size # pprint(config) # The number of heads. __magic_name__ : Dict =config.n_head # The hidden_size per head. __magic_name__ : int =config.n_embd // config.n_head # Megatron-LM checkpoint version if "checkpoint_version" in input_state_dict.keys(): __magic_name__ : Union[str, Any] =input_state_dict["""checkpoint_version"""] else: __magic_name__ : Any =0.0 # The model. __magic_name__ : Dict =input_state_dict["""model"""] # The language model. __magic_name__ : Tuple =model["""language_model"""] # The embeddings. __magic_name__ : List[str] =lm["""embedding"""] # The word embeddings. __magic_name__ : str =embeddings["""word_embeddings"""]["""weight"""] # Truncate the embedding table to vocab_size rows. __magic_name__ : Dict =word_embeddings[: config.vocab_size, :] __magic_name__ : Optional[int] =word_embeddings # The position embeddings. __magic_name__ : str =embeddings["""position_embeddings"""]["""weight"""] # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size] __magic_name__ : Union[str, Any] =pos_embeddings.size(0 ) if n_positions != config.n_positions: raise ValueError( F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match" ) # Store the position embeddings. __magic_name__ : Optional[int] =pos_embeddings # The transformer. __magic_name__ : Dict =lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""] # The regex to extract layer names. __magic_name__ : List[str] =re.compile(R"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" ) # The simple map of names for "automated" rules. __magic_name__ : List[Any] ={ """attention.dense""": """.attn.c_proj.""", """self_attention.dense""": """.attn.c_proj.""", """mlp.dense_h_to_4h""": """.mlp.c_fc.""", """mlp.dense_4h_to_h""": """.mlp.c_proj.""", } # Extract the layers. for key, val in transformer.items(): # Match the name. __magic_name__ : List[Any] =layer_re.match(lowerCamelCase ) # Stop if that's not a layer if m is None: break # The index of the layer. __magic_name__ : str =int(m.group(1 ) ) # The name of the operation. __magic_name__ : Dict =m.group(2 ) # Is it a weight or a bias? __magic_name__ : Dict =m.group(3 ) # The name of the layer. __magic_name__ : List[str] =F"transformer.h.{layer_idx}" # For layernorm(s), simply store the layer norm. if op_name.endswith("""layernorm""" ): __magic_name__ : List[Any] ="""ln_1""" if op_name.startswith("""input""" ) else """ln_2""" __magic_name__ : Optional[int] =val # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. __magic_name__ : Optional[Any] =torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view( 1 , 1 , lowerCamelCase , lowerCamelCase ) __magic_name__ : Dict =causal_mask # Insert a "dummy" tensor for masked_bias. __magic_name__ : Any =torch.tensor(-1E4 , dtype=torch.floataa ) __magic_name__ : Dict =masked_bias __magic_name__ : List[str] =fix_query_key_value_ordering(lowerCamelCase , lowerCamelCase , 3 , lowerCamelCase , lowerCamelCase ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. __magic_name__ : Optional[Any] =out_val.transpose(0 , 1 ).contiguous() # Store. __magic_name__ : Any =out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": __magic_name__ : Dict =fix_query_key_value_ordering(lowerCamelCase , lowerCamelCase , 3 , lowerCamelCase , lowerCamelCase ) # Store. No change of shape. __magic_name__ : Union[str, Any] =out_val # Transpose the weights. elif weight_or_bias == "weight": __magic_name__ : Tuple =megatron_to_transformers[op_name] __magic_name__ : List[str] =val.transpose(0 , 1 ) # Copy the bias. elif weight_or_bias == "bias": __magic_name__ : Optional[int] =megatron_to_transformers[op_name] __magic_name__ : Dict =val # DEBUG. assert config.n_layer == layer_idx + 1 # The final layernorm. __magic_name__ : Any =transformer["""final_layernorm.weight"""] __magic_name__ : Tuple =transformer["""final_layernorm.bias"""] # For LM head, transformers' wants the matrix to weight embeddings. __magic_name__ : int =word_embeddings # It should be done! return output_state_dict def lowerCAmelCase_ ( ): # Create the argument parser. __magic_name__ : Union[str, Any] =argparse.ArgumentParser() parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" ) parser.add_argument( """path_to_checkpoint""" , type=lowerCamelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , ) parser.add_argument( """--config_file""" , default="""""" , type=lowerCamelCase , help="""An optional config json file describing the pre-trained model.""" , ) __magic_name__ : Any =parser.parse_args() # Extract the basename. __magic_name__ : Tuple =os.path.dirname(args.path_to_checkpoint ) # Load the model. # the .zip is very optional, let's keep it for backward compatibility print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}" ) if args.path_to_checkpoint.endswith(""".zip""" ): with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint: with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict: __magic_name__ : Optional[Any] =torch.load(lowerCamelCase , map_location="""cpu""" ) else: __magic_name__ : Dict =torch.load(args.path_to_checkpoint , map_location="""cpu""" ) __magic_name__ : Optional[Any] =input_state_dict.get("""args""" , lowerCamelCase ) # Read the config, or default to the model released by NVIDIA. if args.config_file == "": if ds_args is not None: if ds_args.bias_gelu_fusion: __magic_name__ : Optional[Any] ="""gelu_fast""" elif ds_args.openai_gelu: __magic_name__ : Optional[Any] ="""gelu_new""" else: __magic_name__ : List[Any] ="""gelu""" else: # in the very early days this used to be "gelu_new" __magic_name__ : Dict ="""gelu_new""" # Spell out all parameters in case the defaults change. __magic_name__ : Any =GPTaConfig( vocab_size=50257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=lowerCamelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.0_2 , summary_type="""cls_index""" , summary_use_proj=lowerCamelCase , summary_activation=lowerCamelCase , summary_proj_to_labels=lowerCamelCase , summary_first_dropout=0.1 , scale_attn_weights=lowerCamelCase , use_cache=lowerCamelCase , bos_token_id=50256 , eos_token_id=50256 , ) else: __magic_name__ : Optional[int] =GPTaConfig.from_json_file(args.config_file ) __magic_name__ : Tuple =["""GPT2LMHeadModel"""] # Convert. print("""Converting""" ) __magic_name__ : int =convert_megatron_checkpoint(lowerCamelCase , lowerCamelCase , lowerCamelCase ) # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(lowerCamelCase , lowerCamelCase ) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if ds_args is not None: __magic_name__ : Union[str, Any] =ds_args.tokenizer_type if tokenizer_type == "GPT2BPETokenizer": __magic_name__ : List[Any] ="""gpt2""" elif tokenizer_type == "PretrainedFromHF": __magic_name__ : Optional[Any] =ds_args.tokenizer_name_or_path else: raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}" ) else: __magic_name__ : List[Any] ="""gpt2""" __magic_name__ : Dict =AutoTokenizer.from_pretrained(lowerCamelCase ) __magic_name__ : Tuple =type(lowerCamelCase ).__name__ __magic_name__ : List[Any] =tokenizer_class # Store the config to file. print("""Saving config""" ) config.save_pretrained(lowerCamelCase ) # Save tokenizer based on args print(F"Adding {tokenizer_class} tokenizer files" ) tokenizer.save_pretrained(lowerCamelCase ) # Store the state_dict to file. __magic_name__ : Optional[int] =os.path.join(lowerCamelCase , """pytorch_model.bin""" ) print(F"Saving checkpoint to \"{output_checkpoint_file}\"" ) torch.save(lowerCamelCase , lowerCamelCase ) #################################################################################################### if __name__ == "__main__": main() ####################################################################################################
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from typing import Dict, List, Optional, Union import numpy as np from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy UpperCAmelCase_ : Dict = logging.get_logger(__name__) class __A ( UpperCamelCase__ ): def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =feature_size __magic_name__ : Union[str, Any] =sampling_rate __magic_name__ : List[Any] =padding_value __magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" ) __magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case ) super().__init__(**__snake_case ) def A__ ( self :Any , __snake_case :Union[ BatchFeature, List[BatchFeature], Dict[str, BatchFeature], Dict[str, List[BatchFeature]], List[Dict[str, BatchFeature]], ] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ): '''simple docstring''' if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ): __magic_name__ : Union[str, Any] ={ key: [example[key] for example in processed_features] for key in processed_features[0].keys() } # The model's main input name, usually `input_values`, has be passed for padding if self.model_input_names[0] not in processed_features: raise ValueError( """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`""" f" to this method that includes {self.model_input_names[0]}, but you provided" f" {list(processed_features.keys() )}" ) __magic_name__ : int =processed_features[self.model_input_names[0]] __magic_name__ : Union[str, Any] =( return_attention_mask if return_attention_mask is not None else self.return_attention_mask ) if len(__snake_case ) == 0: if return_attention_mask: __magic_name__ : List[str] =[] return processed_features # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch __magic_name__ : Optional[int] =required_input[0] if isinstance(__snake_case , (list, tuple) ): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. __magic_name__ : Optional[Any] =0 while len(required_input[index] ) == 0: index += 1 if index < len(__snake_case ): __magic_name__ : List[str] =required_input[index][0] if return_tensors is None: if is_tf_tensor(__snake_case ): __magic_name__ : int ="""tf""" elif is_torch_tensor(__snake_case ): __magic_name__ : str ="""pt""" elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ): __magic_name__ : List[Any] ="""np""" else: raise ValueError( f"type of {first_element} unknown: {type(__snake_case )}. " """Should be one of a python, numpy, pytorch or tensorflow object.""" ) for key, value in processed_features.items(): if isinstance(value[0] , (int, float) ): __magic_name__ : List[str] =to_numpy(__snake_case ) else: __magic_name__ : str =[to_numpy(__snake_case ) for v in value] # Convert padding_strategy in PaddingStrategy __magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case ) __magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]] __magic_name__ : Dict =len(__snake_case ) if not all(len(__snake_case ) == batch_size for v in processed_features.values() ): raise ValueError("""Some items in the output dictionary have a different batch size than others.""" ) __magic_name__ : Optional[int] =[] for i in range(__snake_case ): __magic_name__ : Any ={k: v[i] for k, v in processed_features.items()} # truncation __magic_name__ : List[str] =self._truncate( __snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , ) truncated_inputs.append(__snake_case ) if padding_strategy == PaddingStrategy.LONGEST: # make sure that `max_length` cannot be longer than the longest truncated length __magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs ) __magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH __magic_name__ : str ={} for i in range(__snake_case ): # padding __magic_name__ : List[str] =self._pad( truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , ) for key, value in outputs.items(): if key not in batch_outputs: __magic_name__ : Dict =[] if value.dtype is np.dtype(np.floataa ): __magic_name__ : Optional[int] =value.astype(np.floataa ) batch_outputs[key].append(__snake_case ) return BatchFeature(__snake_case , tensor_type=__snake_case ) def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' __magic_name__ : Dict =processed_features[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: __magic_name__ : Any =len(__snake_case ) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length if return_attention_mask and "attention_mask" not in processed_features: __magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa ) if needs_to_be_padded: __magic_name__ : List[Any] =max_length - len(__snake_case ) if self.padding_side == "right": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (0, difference) ) __magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference) __magic_name__ : str =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) elif self.padding_side == "left": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (difference, 0) ) __magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0) __magic_name__ : List[Any] =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) else: raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) ) return processed_features def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' if not truncation: return processed_features elif truncation and max_length is None: raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" ) __magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]] # find `max_length` that fits `pad_to_multiple_of` if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : Any =len(__snake_case ) > max_length if needs_to_be_truncated: __magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length] if "attention_mask" in processed_features: __magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length] return processed_features def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ): '''simple docstring''' if padding is not False: if padding is True: __magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch elif not isinstance(__snake_case , __snake_case ): __magic_name__ : Optional[int] =PaddingStrategy(__snake_case ) elif isinstance(__snake_case , __snake_case ): __magic_name__ : Any =padding else: __magic_name__ : Any =PaddingStrategy.DO_NOT_PAD # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: raise ValueError( f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" ) # Test if we have a padding value if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None): raise ValueError( """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use""" """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" ) return padding_strategy
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1
from random import shuffle import tensorflow as tf from numpy import array def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ : List[str] =int(lowerCamelCase ) assert noofclusters < len(lowerCamelCase ) # Find out the dimensionality __magic_name__ : Union[str, Any] =len(vectors[0] ) # Will help select random centroids from among the available vectors __magic_name__ : List[Any] =list(range(len(lowerCamelCase ) ) ) shuffle(lowerCamelCase ) # GRAPH OF COMPUTATION # We initialize a new graph and set it as the default during each run # of this algorithm. This ensures that as this function is called # multiple times, the default graph doesn't keep getting crowded with # unused ops and Variables from previous function calls. __magic_name__ : Any =tf.Graph() with graph.as_default(): # SESSION OF COMPUTATION __magic_name__ : Any =tf.Session() ##CONSTRUCTING THE ELEMENTS OF COMPUTATION ##First lets ensure we have a Variable vector for each centroid, ##initialized to one of the vectors from the available data points __magic_name__ : Dict =[ tf.Variable(vectors[vector_indices[i]] ) for i in range(lowerCamelCase ) ] ##These nodes will assign the centroid Variables the appropriate ##values __magic_name__ : List[Any] =tf.placeholder("""float64""" , [dim] ) __magic_name__ : Optional[Any] =[] for centroid in centroids: cent_assigns.append(tf.assign(lowerCamelCase , lowerCamelCase ) ) ##Variables for cluster assignments of individual vectors(initialized ##to 0 at first) __magic_name__ : Union[str, Any] =[tf.Variable(0 ) for i in range(len(lowerCamelCase ) )] ##These nodes will assign an assignment Variable the appropriate ##value __magic_name__ : Optional[Any] =tf.placeholder("""int32""" ) __magic_name__ : List[str] =[] for assignment in assignments: cluster_assigns.append(tf.assign(lowerCamelCase , lowerCamelCase ) ) ##Now lets construct the node that will compute the mean # The placeholder for the input __magic_name__ : List[str] =tf.placeholder("""float""" , [None, dim] ) # The Node/op takes the input and computes a mean along the 0th # dimension, i.e. the list of input vectors __magic_name__ : List[str] =tf.reduce_mean(lowerCamelCase , 0 ) ##Node for computing Euclidean distances # Placeholders for input __magic_name__ : Union[str, Any] =tf.placeholder("""float""" , [dim] ) __magic_name__ : Any =tf.placeholder("""float""" , [dim] ) __magic_name__ : Any =tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCamelCase , lowerCamelCase ) , 2 ) ) ) ##This node will figure out which cluster to assign a vector to, ##based on Euclidean distances of the vector from the centroids. # Placeholder for input __magic_name__ : Any =tf.placeholder("""float""" , [noofclusters] ) __magic_name__ : Tuple =tf.argmin(lowerCamelCase , 0 ) ##INITIALIZING STATE VARIABLES ##This will help initialization of all Variables defined with respect ##to the graph. The Variable-initializer should be defined after ##all the Variables have been constructed, so that each of them ##will be included in the initialization. __magic_name__ : str =tf.initialize_all_variables() # Initialize all variables sess.run(lowerCamelCase ) ##CLUSTERING ITERATIONS # Now perform the Expectation-Maximization steps of K-Means clustering # iterations. To keep things simple, we will only do a set number of # iterations, instead of using a Stopping Criterion. __magic_name__ : List[str] =100 for _ in range(lowerCamelCase ): ##EXPECTATION STEP ##Based on the centroid locations till last iteration, compute ##the _expected_ centroid assignments. # Iterate over each vector for vector_n in range(len(lowerCamelCase ) ): __magic_name__ : List[str] =vectors[vector_n] # Compute Euclidean distance between this vector and each # centroid. Remember that this list cannot be named #'centroid_distances', since that is the input to the # cluster assignment node. __magic_name__ : Optional[Any] =[ sess.run(lowerCamelCase , feed_dict={va: vect, va: sess.run(lowerCamelCase )} ) for centroid in centroids ] # Now use the cluster assignment node, with the distances # as the input __magic_name__ : Tuple =sess.run( lowerCamelCase , feed_dict={centroid_distances: distances} ) # Now assign the value to the appropriate state variable sess.run( cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} ) ##MAXIMIZATION STEP # Based on the expected state computed from the Expectation Step, # compute the locations of the centroids so as to maximize the # overall objective of minimizing within-cluster Sum-of-Squares for cluster_n in range(lowerCamelCase ): # Collect all the vectors assigned to this cluster __magic_name__ : List[str] =[ vectors[i] for i in range(len(lowerCamelCase ) ) if sess.run(assignments[i] ) == cluster_n ] # Compute new centroid location __magic_name__ : Tuple =sess.run( lowerCamelCase , feed_dict={mean_input: array(lowerCamelCase )} ) # Assign value to appropriate variable sess.run( cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} ) # Return centroids and assignments __magic_name__ : List[Any] =sess.run(lowerCamelCase ) __magic_name__ : int =sess.run(lowerCamelCase ) return centroids, assignments
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import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class __A ( nn.Module ): def __init__( self :List[Any] ): '''simple docstring''' super().__init__() __magic_name__ : Tuple =nn.Linear(3 , 4 ) __magic_name__ : Union[str, Any] =nn.BatchNormad(4 ) __magic_name__ : List[str] =nn.Linear(4 , 5 ) def A__ ( self :Dict , __snake_case :Tuple ): '''simple docstring''' return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) ) class __A ( UpperCamelCase__ ): def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ): '''simple docstring''' return (args[0] + 1,) + args[1:], kwargs class __A ( UpperCamelCase__ ): def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ): '''simple docstring''' return output + 1 class __A ( unittest.TestCase ): def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : Tuple =ModelHook() add_hook_to_module(__snake_case , __snake_case ) self.assertEqual(test_model._hf_hook , __snake_case ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : List[str] =ModelHook() add_hook_to_module(__snake_case , __snake_case ) add_hook_to_module(__snake_case , __snake_case , append=__snake_case ) self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case ) self.assertEqual(len(test_model._hf_hook.hooks ) , 2 ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() __magic_name__ : Any =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(x + 1 ) __magic_name__ : Optional[Any] =test_model(x + 2 ) __magic_name__ : int =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : int =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : str =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : List[str] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) assert torch.allclose(__snake_case , __snake_case , atol=1E-5 ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() __magic_name__ : Dict =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(__snake_case ) __magic_name__ : Dict =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Optional[int] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) assert torch.allclose(__snake_case , output + 2 , atol=1E-5 ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : Tuple =ModelForTest() __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Dict =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 ) ) self.assertTrue(outputa.requires_grad ) __magic_name__ : Any =True __magic_name__ : Any =test_model(__snake_case ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) ) self.assertEqual(model.lineara.weight.device , torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[Any] =model(__snake_case ) self.assertEqual(output.device , torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) ) __magic_name__ : int =torch.randn(2 , 3 ).to(0 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , torch.device(0 ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : int =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True} add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : Union[str, Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload __magic_name__ : Tuple ={ """execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True, """offload_buffers""": True, } add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Tuple =torch.randn(2 , 3 ) __magic_name__ : int =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : str =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Optional[int] =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Dict =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Any =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
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from __future__ import annotations def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : int =0.0_0 __magic_name__ : Tuple =0 for resistor in resistors: if resistor <= 0: __magic_name__ : Optional[int] =F"Resistor at index {index} has a negative or zero value!" raise ValueError(lowerCamelCase ) first_sum += 1 / float(lowerCamelCase ) index += 1 return 1 / first_sum def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Optional[int] =0.0_0 __magic_name__ : Optional[Any] =0 for resistor in resistors: sum_r += resistor if resistor < 0: __magic_name__ : Optional[int] =F"Resistor at index {index} has a negative value!" raise ValueError(lowerCamelCase ) index += 1 return sum_r if __name__ == "__main__": import doctest doctest.testmod()
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import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import XLMRobertaTokenizerFast from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = KandinskyInpaintPipeline UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""] UpperCamelCase = [ """prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image""", ] UpperCamelCase = [ """generator""", """height""", """width""", """latents""", """guidance_scale""", """negative_prompt""", """num_inference_steps""", """return_dict""", """guidance_scale""", """num_images_per_prompt""", """output_type""", """return_dict""", ] UpperCamelCase = False @property def A__ ( self :Union[str, Any] ): '''simple docstring''' return 32 @property def A__ ( self :Optional[Any] ): '''simple docstring''' return 32 @property def A__ ( self :List[Any] ): '''simple docstring''' return self.time_input_dim @property def A__ ( self :Dict ): '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self :List[Any] ): '''simple docstring''' return 1_00 @property def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" ) return tokenizer @property def A__ ( self :str ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : str =MCLIPConfig( numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , ) __magic_name__ : Tuple =MultilingualCLIP(__snake_case ) __magic_name__ : Optional[int] =text_encoder.eval() return text_encoder @property def A__ ( self :Dict ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Optional[Any] ={ """in_channels""": 9, # Out channels is double in channels because predicts mean and variance """out_channels""": 8, """addition_embed_type""": """text_image""", """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""), """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""), """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""", """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2), """layers_per_block""": 1, """encoder_hid_dim""": self.text_embedder_hidden_size, """encoder_hid_dim_type""": """text_image_proj""", """cross_attention_dim""": self.cross_attention_dim, """attention_head_dim""": 4, """resnet_time_scale_shift""": """scale_shift""", """class_embed_type""": None, } __magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case ) return model @property def A__ ( self :List[str] ): '''simple docstring''' return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def A__ ( self :Tuple ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs ) return model def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[str] =self.dummy_text_encoder __magic_name__ : Optional[Any] =self.dummy_tokenizer __magic_name__ : Optional[Any] =self.dummy_unet __magic_name__ : Tuple =self.dummy_movq __magic_name__ : List[str] =DDIMScheduler( num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , ) __magic_name__ : str ={ """text_encoder""": text_encoder, """tokenizer""": tokenizer, """unet""": unet, """scheduler""": scheduler, """movq""": movq, } return components def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ): '''simple docstring''' __magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case ) # create init_image __magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0] __magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) ) # create mask __magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa ) __magic_name__ : Any =0 if str(__snake_case ).startswith("""mps""" ): __magic_name__ : Dict =torch.manual_seed(__snake_case ) else: __magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case ) __magic_name__ : List[Any] ={ """prompt""": """horse""", """image""": init_image, """mask_image""": mask, """image_embeds""": image_embeds, """negative_image_embeds""": negative_image_embeds, """generator""": generator, """height""": 64, """width""": 64, """num_inference_steps""": 2, """guidance_scale""": 4.0, """output_type""": """np""", } return inputs def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Tuple ="""cpu""" __magic_name__ : List[Any] =self.get_dummy_components() __magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case ) __magic_name__ : Tuple =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) ) __magic_name__ : List[Any] =output.images __magic_name__ : Any =pipe( **self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0] __magic_name__ : int =image[0, -3:, -3:, -1] __magic_name__ : str =image_from_tuple[0, -3:, -3:, -1] print(f"image.shape {image.shape}" ) assert image.shape == (1, 64, 64, 3) __magic_name__ : Optional[Any] =np.array( [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" def A__ ( self :Dict ): '''simple docstring''' super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) @slow @require_torch_gpu class __A ( unittest.TestCase ): def A__ ( self :List[Any] ): '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : List[str] =load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" ) __magic_name__ : int =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" ) __magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa ) __magic_name__ : Any =0 __magic_name__ : int ="""a hat""" __magic_name__ : int =KandinskyPriorPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa ) pipe_prior.to(__snake_case ) __magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa ) __magic_name__ : int =pipeline.to(__snake_case ) pipeline.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 ) __magic_name__ , __magic_name__ : Dict =pipe_prior( __snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple() __magic_name__ : Optional[Any] =pipeline( __snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , ) __magic_name__ : Optional[int] =output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(__snake_case , __snake_case )
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1
def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =(1 + 24 * n) ** 0.5 return ((1 + root) / 6) % 1 == 0 def lowerCAmelCase_ ( lowerCamelCase = 5000 ): __magic_name__ : List[str] =[(i * (3 * i - 1)) // 2 for i in range(1 , lowerCamelCase )] for i, pentagonal_i in enumerate(lowerCamelCase ): for j in range(lowerCamelCase , len(lowerCamelCase ) ): __magic_name__ : Any =pentagonal_nums[j] __magic_name__ : Tuple =pentagonal_i + pentagonal_j __magic_name__ : Tuple =pentagonal_j - pentagonal_i if is_pentagonal(lowerCamelCase ) and is_pentagonal(lowerCamelCase ): return b return -1 if __name__ == "__main__": print(F"""{solution() = }""")
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import inspect import unittest import numpy as np from tests.test_modeling_common import floats_tensor from transformers import MaskaFormerConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel if is_vision_available(): from transformers import MaskaFormerImageProcessor if is_vision_available(): from PIL import Image class __A : def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ): '''simple docstring''' __magic_name__ : Optional[int] =parent __magic_name__ : List[Any] =batch_size __magic_name__ : List[str] =is_training __magic_name__ : List[str] =use_auxiliary_loss __magic_name__ : Union[str, Any] =num_queries __magic_name__ : str =num_channels __magic_name__ : Union[str, Any] =min_size __magic_name__ : Union[str, Any] =max_size __magic_name__ : Optional[int] =num_labels __magic_name__ : Tuple =hidden_dim __magic_name__ : Any =hidden_dim def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to( __snake_case ) __magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case ) __magic_name__ : List[str] =( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5 ).float() __magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long() __magic_name__ : str =self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Dict =MaskaFormerConfig( hidden_size=self.hidden_dim , ) __magic_name__ : str =self.num_queries __magic_name__ : Dict =self.num_labels __magic_name__ : int =[1, 1, 1, 1] __magic_name__ : List[str] =self.num_channels __magic_name__ : str =64 __magic_name__ : List[str] =1_28 __magic_name__ : Optional[Any] =self.hidden_dim __magic_name__ : Tuple =self.hidden_dim __magic_name__ : Optional[int] =self.hidden_dim return config def A__ ( self :Any ): '''simple docstring''' __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs() __magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask} return config, inputs_dict def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ): '''simple docstring''' __magic_name__ : int =output.encoder_hidden_states __magic_name__ : List[str] =output.pixel_decoder_hidden_states __magic_name__ : int =output.transformer_decoder_hidden_states self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , config.decoder_layers ) def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ): '''simple docstring''' with torch.no_grad(): __magic_name__ : List[str] =MaskaFormerModel(config=__snake_case ) model.to(__snake_case ) model.eval() __magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case ) self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(output.encoder_last_hidden_state is not None ) if output_hidden_states: self.check_output_hidden_state(__snake_case , __snake_case ) def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ): '''simple docstring''' __magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case ) model.to(__snake_case ) model.eval() def comm_check_on_output(__snake_case :List[str] ): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.encoder_last_hidden_state is not None ) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): __magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : List[str] =model(__snake_case ) comm_check_on_output(__snake_case ) __magic_name__ : Any =model( pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) comm_check_on_output(__snake_case ) self.parent.assertTrue(result.loss is not None ) self.parent.assertEqual(result.loss.shape , torch.Size([1] ) ) @require_torch class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else () UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {} UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False def A__ ( self :str ): '''simple docstring''' __magic_name__ : Any =MaskaFormerModelTester(self ) __magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case ) def A__ ( self :Dict ): '''simple docstring''' self.config_tester.run_common_tests() def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case ) @unittest.skip(reason="""Mask2Former does not use inputs_embeds""" ) def A__ ( self :List[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" ) def A__ ( self :Dict ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former is not a generative model""" ) def A__ ( self :Optional[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not use token embeddings""" ) def A__ ( self :int ): '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip( reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" ) def A__ ( self :Tuple ): '''simple docstring''' pass @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" ) def A__ ( self :Union[str, Any] ): '''simple docstring''' pass def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : Tuple =model_class(__snake_case ) __magic_name__ : Optional[Any] =inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic __magic_name__ : Tuple =[*signature.parameters.keys()] __magic_name__ : Optional[Any] =["""pixel_values"""] self.assertListEqual(arg_names[:1] , __snake_case ) @slow def A__ ( self :Tuple ): '''simple docstring''' for model_name in ["facebook/mask2former-swin-small-coco-instance"]: __magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =(self.model_tester.min_size,) * 2 __magic_name__ : Union[str, Any] ={ """pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ), """mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ), """class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(), } __magic_name__ : Optional[Any] =self.model_tester.get_config() __magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case ) __magic_name__ : Any =model(**__snake_case ) self.assertTrue(outputs.loss is not None ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case ) __magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case ) self.assertTrue(outputs.attentions is not None ) def A__ ( self :int ): '''simple docstring''' if not self.model_tester.is_training: return __magic_name__ : List[Any] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Dict =model_class(__snake_case ) model.to(__snake_case ) model.train() __magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss loss.backward() def A__ ( self :int ): '''simple docstring''' __magic_name__ : List[str] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Tuple =True __magic_name__ : Optional[int] =True __magic_name__ : int =model_class(__snake_case ).to(__snake_case ) model.train() __magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) __magic_name__ : Optional[int] =outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() __magic_name__ : Optional[int] =outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=__snake_case ) self.assertIsNotNone(encoder_hidden_states.grad ) self.assertIsNotNone(pixel_decoder_hidden_states.grad ) self.assertIsNotNone(transformer_decoder_hidden_states.grad ) self.assertIsNotNone(attentions.grad ) UpperCAmelCase_ : Dict = 1e-4 def lowerCAmelCase_ ( ): __magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_vision @slow class __A ( unittest.TestCase ): @cached_property def A__ ( self :int ): '''simple docstring''' return "facebook/mask2former-swin-small-coco-instance" @cached_property def A__ ( self :int ): '''simple docstring''' return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case ) __magic_name__ : int =self.default_image_processor __magic_name__ : List[Any] =prepare_img() __magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Dict =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : List[str] =model(**__snake_case ) __magic_name__ : Any =torch.tensor( [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Dict =torch.tensor( [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Any =torch.tensor( [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Optional[int] =self.default_image_processor __magic_name__ : Tuple =prepare_img() __magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : str =model(**__snake_case ) # masks_queries_logits __magic_name__ : List[Any] =outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ) __magic_name__ : List[Any] =[ [-8.7839, -9.0056, -8.8121], [-7.4104, -7.0313, -6.5401], [-6.6105, -6.3427, -6.4675], ] __magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) # class_queries_logits __magic_name__ : Any =outputs.class_queries_logits self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) ) __magic_name__ : int =torch.tensor( [ [1.8324, -8.0835, -4.1922], [0.8450, -9.0050, -3.6053], [0.3045, -7.7293, -3.0275], ] ).to(__snake_case ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Any =self.default_image_processor __magic_name__ : Union[str, Any] =image_processor( [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , ) __magic_name__ : str =inputs["""pixel_values"""].to(__snake_case ) __magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]] __magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]] with torch.no_grad(): __magic_name__ : Dict =model(**__snake_case ) self.assertTrue(outputs.loss is not None )
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class __A ( unittest.TestCase ): def A__ ( self :Tuple ): '''simple docstring''' debug_launcher(test_script.main ) def A__ ( self :Dict ): '''simple docstring''' debug_launcher(test_ops.main )
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import warnings from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "nvidia/segformer-b0-finetuned-ade-512-512": ( "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json" ), # See all SegFormer models at https://huggingface.co/models?filter=segformer } class __A ( UpperCamelCase__ ): UpperCamelCase = """segformer""" def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ): '''simple docstring''' super().__init__(**__snake_case ) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be""" """ removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , ) __magic_name__ : Dict =num_channels __magic_name__ : str =num_encoder_blocks __magic_name__ : List[Any] =depths __magic_name__ : Optional[Any] =sr_ratios __magic_name__ : List[str] =hidden_sizes __magic_name__ : List[str] =patch_sizes __magic_name__ : Any =strides __magic_name__ : Optional[Any] =mlp_ratios __magic_name__ : str =num_attention_heads __magic_name__ : int =hidden_act __magic_name__ : List[Any] =hidden_dropout_prob __magic_name__ : Optional[Any] =attention_probs_dropout_prob __magic_name__ : Optional[Any] =classifier_dropout_prob __magic_name__ : List[str] =initializer_range __magic_name__ : List[str] =drop_path_rate __magic_name__ : List[Any] =layer_norm_eps __magic_name__ : List[str] =decoder_hidden_size __magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case ) __magic_name__ : Dict =semantic_loss_ignore_index class __A ( UpperCamelCase__ ): UpperCamelCase = version.parse("""1.11""" ) @property def A__ ( self :List[str] ): '''simple docstring''' return OrderedDict( [ ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}), ] ) @property def A__ ( self :Any ): '''simple docstring''' return 1E-4 @property def A__ ( self :int ): '''simple docstring''' return 12
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import fire from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , **lowerCamelCase ): __magic_name__ : int =AutoConfig.from_pretrained(lowerCamelCase , **lowerCamelCase ) __magic_name__ : List[str] =AutoModelForSeqaSeqLM.from_config(lowerCamelCase ) model.save_pretrained(lowerCamelCase ) AutoTokenizer.from_pretrained(lowerCamelCase ).save_pretrained(lowerCamelCase ) return model if __name__ == "__main__": fire.Fire(save_randomly_initialized_version)
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import heapq def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : list[list] =[] # 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(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] ) # chosen_vertices = set of chosen vertices __magic_name__ : Tuple =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 __magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0] chosen_vertices.add(lowerCamelCase ) # 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]: __magic_name__ : Tuple =elem[1][1].index(lowerCamelCase ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(lowerCamelCase ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : Optional[int] = {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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def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): if a < 0 or b < 0: raise ValueError("""the value of both inputs must be positive""" ) __magic_name__ : str =str(bin(lowerCamelCase ) )[2:] # remove the leading "0b" __magic_name__ : str =str(bin(lowerCamelCase ) )[2:] # remove the leading "0b" __magic_name__ : Optional[int] =max(len(lowerCamelCase ) , len(lowerCamelCase ) ) return "0b" + "".join( str(int(char_a != char_b ) ) for char_a, char_b in zip(a_binary.zfill(lowerCamelCase ) , b_binary.zfill(lowerCamelCase ) ) ) if __name__ == "__main__": import doctest doctest.testmod()
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UpperCAmelCase_ : int = range(2, 20 + 1) UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)] UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) ) __magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) ) __magic_name__ , __magic_name__ : Tuple =0, 0 __magic_name__ : Optional[Any] =n - i __magic_name__ : Union[str, Any] =memo.get(lowerCamelCase ) if sub_memo is not None: __magic_name__ : int =sub_memo.get(lowerCamelCase ) if jumps is not None and len(lowerCamelCase ) > 0: # find and make the largest jump without going over __magic_name__ : Dict =-1 for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ): if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: __magic_name__ : Optional[Any] =_k break if max_jump >= 0: __magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump] # since the difference between jumps is cached, add c __magic_name__ : Tuple =diff + c for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ): __magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 ) if new_c > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) else: __magic_name__ : str =[] else: __magic_name__ : List[str] ={c: []} __magic_name__ : List[str] =sub_memo if dn >= max_dn or c + diff >= base[k]: return diff, dn if k > ks[0]: while True: # keep doing smaller jumps __magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped if dn >= max_dn or c + diff >= base[k]: break else: # would be too small a jump, just compute sequential terms instead __magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped __magic_name__ : Tuple =sub_memo[c] # keep jumps sorted by # of terms skipped __magic_name__ : List[Any] =0 while j < len(lowerCamelCase ): if jumps[j][1] > dn: break j += 1 # cache the jump for this value digitsum(b) and c sub_memo[c].insert(lowerCamelCase , (diff, dn, k) ) return (diff, dn) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): if i >= n: return 0, i if k > len(lowerCamelCase ): a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] ) # note: a_i -> b * 10^k + c # ds_b -> digitsum(b) # ds_c -> digitsum(c) __magic_name__ : Tuple =i __magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0 for j in range(len(lowerCamelCase ) ): if j >= k: ds_b += a_i[j] else: ds_c += a_i[j] while i < n: i += 1 __magic_name__ : Optional[Any] =ds_c + ds_b diff += addend __magic_name__ : str =0 for j in range(lowerCamelCase ): __magic_name__ : int =a_i[j] + addend __magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 ) ds_c += a_i[j] if addend > 0: break if addend > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) return diff, i - start_i def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): for j in range(lowerCamelCase , len(lowerCamelCase ) ): __magic_name__ : Tuple =digits[j] + addend if s >= 10: __magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 ) __magic_name__ : int =addend // 10 + quotient else: __magic_name__ : Dict =s __magic_name__ : Any =addend // 10 if addend == 0: break while addend > 0: __magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 ) digits.append(lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase = 10**15 ): __magic_name__ : List[str] =[1] __magic_name__ : str =1 __magic_name__ : str =0 while True: __magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase ) dn += terms_jumped if dn == n - i: break __magic_name__ : int =0 for j in range(len(lowerCamelCase ) ): a_n += digits[j] * 10**j return a_n if __name__ == "__main__": print(F"""{solution() = }""")
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import argparse import intel_extension_for_pytorch as ipex import torch from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline UpperCAmelCase_ : Tuple = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False) parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not") parser.add_argument("--steps", default=None, type=int, help="Num inference steps") UpperCAmelCase_ : int = parser.parse_args() UpperCAmelCase_ : Union[str, Any] = "cpu" UpperCAmelCase_ : int = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings" UpperCAmelCase_ : Optional[int] = "path-to-your-trained-model" UpperCAmelCase_ : Union[str, Any] = StableDiffusionPipeline.from_pretrained(model_id) if args.dpm: UpperCAmelCase_ : Any = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) UpperCAmelCase_ : Union[str, Any] = pipe.to(device) # to channels last UpperCAmelCase_ : List[str] = pipe.unet.to(memory_format=torch.channels_last) UpperCAmelCase_ : List[Any] = pipe.vae.to(memory_format=torch.channels_last) UpperCAmelCase_ : List[Any] = pipe.text_encoder.to(memory_format=torch.channels_last) if pipe.requires_safety_checker: UpperCAmelCase_ : str = pipe.safety_checker.to(memory_format=torch.channels_last) # optimize with ipex UpperCAmelCase_ : int = torch.randn(2, 4, 64, 64) UpperCAmelCase_ : str = torch.rand(1) * 999 UpperCAmelCase_ : Optional[Any] = torch.randn(2, 77, 768) UpperCAmelCase_ : Union[str, Any] = (sample, timestep, encoder_hidden_status) try: UpperCAmelCase_ : Dict = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example) except Exception: UpperCAmelCase_ : Any = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True) UpperCAmelCase_ : Optional[int] = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True) UpperCAmelCase_ : Tuple = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True) if pipe.requires_safety_checker: UpperCAmelCase_ : Tuple = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True) # compute UpperCAmelCase_ : List[str] = 666 UpperCAmelCase_ : List[str] = torch.Generator(device).manual_seed(seed) UpperCAmelCase_ : List[str] = {"generator": generator} if args.steps is not None: UpperCAmelCase_ : Any = args.steps with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa): UpperCAmelCase_ : List[Any] = pipe(prompt, **generate_kwargs).images[0] # save image image.save("generated.png")
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from typing import List from .keymap import KEYMAP, get_character def lowerCAmelCase_ ( lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] ) handle += [key] setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator def lowerCAmelCase_ ( *lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] ) handle += keys setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator class __A ( UpperCamelCase__ ): def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ): '''simple docstring''' __magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case ) if not hasattr(__snake_case , """key_handler""" ): setattr(__snake_case , """key_handler""" , {} ) setattr(__snake_case , """handle_input""" , KeyHandler.handle_input ) for value in attrs.values(): __magic_name__ : int =getattr(__snake_case , """handle_key""" , [] ) for key in handled_keys: __magic_name__ : List[str] =value return new_cls @staticmethod def A__ ( cls :Optional[int] ): '''simple docstring''' __magic_name__ : Union[str, Any] =get_character() if char != KEYMAP["undefined"]: __magic_name__ : Optional[int] =ord(__snake_case ) __magic_name__ : int =cls.key_handler.get(__snake_case ) if handler: __magic_name__ : Dict =char return handler(cls ) else: return None def lowerCAmelCase_ ( cls ): return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
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import importlib import torch import yaml from omegaconf import OmegaConf from taming.models.vqgan import VQModel def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase ) if display: print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) ) return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ): if conf_path is None: __magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml""" __magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase ) __magic_name__ : Tuple =VQModel(**config.model.params ) if ckpt_path is None: __magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt""" __magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase ) if ".ckpt" in ckpt_path: __magic_name__ : Any =sd["""state_dict"""] model.load_state_dict(lowerCamelCase , strict=lowerCamelCase ) model.to(lowerCamelCase ) del sd return model def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase ) print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" ) __magic_name__ : List[Any] =model.decode(lowerCamelCase ) return xrec def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 ) if reload: __magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase ) importlib.reload(lowerCamelCase ) return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls ) def lowerCAmelCase_ ( lowerCamelCase ): if "target" not in config: raise KeyError("""Expected key `target` to instantiate.""" ) return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ): __magic_name__ : str =instantiate_from_config(lowerCamelCase ) if sd is not None: model.load_state_dict(lowerCamelCase ) if gpu: model.cuda() if eval_mode: model.eval() return {"model": model} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): # load the specified checkpoint if ckpt: __magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" ) __magic_name__ : Any =pl_sd["""global_step"""] print(F"loaded model from global step {global_step}." ) else: __magic_name__ : List[Any] ={"""state_dict""": None} __magic_name__ : Optional[Any] =None __magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""] return model, global_step
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import os import jsonlines import numpy as np from tqdm import tqdm UpperCAmelCase_ : Dict = 2048 UpperCAmelCase_ : int = 4096 UpperCAmelCase_ : Any = 42 UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false") UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4} def lowerCAmelCase_ ( lowerCamelCase ): def choose_first(lowerCamelCase , lowerCamelCase=False ): assert isinstance(lowerCamelCase , lowerCamelCase ) if len(lowerCamelCase ) == 1: __magic_name__ : List[str] =answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: __magic_name__ : Tuple ={k: [a[k]] for k in a} if len(a["""start_token"""] ) > 0: break return a __magic_name__ : str ={"""id""": example["""id"""]} __magic_name__ : List[Any] =example["""annotations"""] __magic_name__ : List[str] =annotation["""yes_no_answer"""] if 0 in yes_no_answer or 1 in yes_no_answer: __magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""] __magic_name__ : List[str] =[] __magic_name__ : Dict =[] __magic_name__ : str =["""<cls>"""] else: __magic_name__ : Tuple =["""short"""] __magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] ) if len(out["""start_token"""] ) == 0: # answer will be long if short is not available __magic_name__ : Tuple =["""long"""] __magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase ) __magic_name__ : List[Any] =[] answer.update(lowerCamelCase ) # disregard some samples if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]: __magic_name__ : Any =True else: __magic_name__ : List[str] =False __magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""] if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ): raise ValueError("""Issue in ID""" , example["""id"""] ) return answer def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase ) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : Any =example["""document"""]["""tokens"""] __magic_name__ : str =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": -100, # ignore index in cross-entropy "end_token": -100, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples __magic_name__ : Dict =["""start_token""", """end_token"""] answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10 __magic_name__ : Tuple =example["""document"""]["""tokens"""] __magic_name__ : Optional[int] =answer["""start_token"""] __magic_name__ : List[Any] =answer["""end_token"""] __magic_name__ : Optional[Any] =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 __magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] ) # checking above code if assertion: __magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] ) if new != old: print("""ID:""" , example["""id"""] ) print("""New:""" , lowerCamelCase , end="""\n""" ) print("""Old:""" , lowerCamelCase , end="""\n\n""" ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ): # overlap will be of doc_stride - q_len __magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase ) __magic_name__ : Union[str, Any] =out["""answer"""] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } __magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids __magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : List[str] =[] __magic_name__ : int =[] __magic_name__ : List[str] =input_ids[:q_len] __magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Tuple =input_ids[i:end_index] inputs.append(q_indices + slice ) category.append(answer["""category"""][0] ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-100] * len(lowerCamelCase ), "end_token": [-100] * len(lowerCamelCase ), "category": category, }, } __magic_name__ : int =out["""context"""].split() __magic_name__ : Any =splitted_context[answer["""end_token"""]] __magic_name__ : str =len( tokenizer( """ """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids ) __magic_name__ : Optional[int] =len( tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids ) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token __magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids ) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 __magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive __magic_name__ : Dict =answer["""start_token"""] __magic_name__ : int =answer["""end_token"""] if assertion: __magic_name__ : Any =tokenizer.decode(lowerCamelCase ) if answer["span"] != new: print("""ISSUE IN TOKENIZATION""" ) print("""OLD:""" , answer["""span"""] ) print("""NEW:""" , lowerCamelCase , end="""\n\n""" ) if len(lowerCamelCase ) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } __magic_name__ : Any =input_ids[:q_len] __magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) __magic_name__ : Any =[] __magic_name__ : List[str] =[] __magic_name__ : List[str] =[] __magic_name__ : str =[] # null, yes, no, long, short for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Dict =input_ids[i:end_index] inputs.append(q_indices + slice ) assert len(inputs[-1] ) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: __magic_name__ : List[Any] =start_token - i + q_len __magic_name__ : Optional[Any] =end_token - i + q_len answers_category.append(answer["""category"""][0] ) # ["short"] -> "short" else: __magic_name__ : Optional[Any] =-100 __magic_name__ : Optional[Any] =-100 answers_category.append("""null""" ) __magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1] answers_start_token.append(lowerCamelCase ) answers_end_token.append(lowerCamelCase ) if assertion: if new != old and new != [tokenizer.cls_token_id]: print("""ISSUE in strided for ID:""" , example["""id"""] ) print("""New:""" , tokenizer.decode(lowerCamelCase ) ) print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ): __magic_name__ : List[Any] =get_strided_contexts_and_ans( lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , ) return example def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): with jsonlines.open(lowerCamelCase , """a""" ) as writer: for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ): __magic_name__ : int =example["""labels"""] for ids, start, end, cat in zip( example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { """input_ids""": ids, """start_token""": start, """end_token""": end, """category""": CATEGORY_MAPPING[cat], } ) if __name__ == "__main__": from datasets import load_dataset from transformers import BigBirdTokenizer UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions") UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"] UpperCAmelCase_ : Optional[int] = { "tokenizer": tokenizer, "doc_stride": DOC_STRIDE, "max_length": MAX_LENGTH, "assertion": False, } UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs) UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"]) print(data) np.random.seed(SEED) UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl" save_to_disk(data, file_name=cache_file_name)
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available UpperCAmelCase_ : Optional[Any] = { "configuration_table_transformer": [ "TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TableTransformerConfig", "TableTransformerOnnxConfig", ] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase_ : Optional[int] = [ "TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TableTransformerForObjectDetection", "TableTransformerModel", "TableTransformerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_table_transformer import ( TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TableTransformerConfig, TableTransformerOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_table_transformer import ( TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TableTransformerForObjectDetection, TableTransformerModel, TableTransformerPreTrainedModel, ) else: import sys UpperCAmelCase_ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Tuple = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json", "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json", # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl } class __A ( UpperCamelCase__ ): UpperCamelCase = """xlm-roberta-xl""" def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ): '''simple docstring''' super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case ) __magic_name__ : List[str] =vocab_size __magic_name__ : List[str] =hidden_size __magic_name__ : Union[str, Any] =num_hidden_layers __magic_name__ : Any =num_attention_heads __magic_name__ : Any =hidden_act __magic_name__ : List[str] =intermediate_size __magic_name__ : Any =hidden_dropout_prob __magic_name__ : Union[str, Any] =attention_probs_dropout_prob __magic_name__ : Any =max_position_embeddings __magic_name__ : Any =type_vocab_size __magic_name__ : List[str] =initializer_range __magic_name__ : Optional[int] =layer_norm_eps __magic_name__ : Dict =position_embedding_type __magic_name__ : Any =use_cache __magic_name__ : Dict =classifier_dropout class __A ( UpperCamelCase__ ): @property def A__ ( self :Dict ): '''simple docstring''' if self.task == "multiple-choice": __magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""} else: __magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ] )
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1
import torch from diffusers import DDPMParallelScheduler from .test_schedulers import SchedulerCommonTest class __A ( UpperCamelCase__ ): UpperCamelCase = (DDPMParallelScheduler,) def A__ ( self :Any , **__snake_case :Tuple ): '''simple docstring''' __magic_name__ : Optional[int] ={ """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(**__snake_case ) return config def A__ ( self :Optional[int] ): '''simple docstring''' for timesteps in [1, 5, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=__snake_case ) def A__ ( self :Optional[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=__snake_case , beta_end=__snake_case ) def A__ ( self :Optional[int] ): '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=__snake_case ) def A__ ( self :Union[str, Any] ): '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=__snake_case ) def A__ ( self :Optional[int] ): '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=__snake_case ) def A__ ( self :Dict ): '''simple docstring''' self.check_over_configs(thresholding=__snake_case ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=__snake_case , prediction_type=__snake_case , sample_max_value=__snake_case , ) def A__ ( self :List[str] ): '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' for t in [0, 5_00, 9_99]: self.check_over_forward(time_step=__snake_case ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : List[Any] =self.scheduler_classes[0] __magic_name__ : Any =self.get_scheduler_config() __magic_name__ : str =scheduler_class(**__snake_case ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.00979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1E-5 def A__ ( self :str ): '''simple docstring''' __magic_name__ : str =self.scheduler_classes[0] __magic_name__ : Optional[int] =self.get_scheduler_config() __magic_name__ : int =scheduler_class(**__snake_case ) __magic_name__ : Union[str, Any] =len(__snake_case ) __magic_name__ : str =self.dummy_model() __magic_name__ : Optional[Any] =self.dummy_sample_deter __magic_name__ : Any =self.dummy_sample_deter + 0.1 __magic_name__ : Union[str, Any] =self.dummy_sample_deter - 0.1 __magic_name__ : str =samplea.shape[0] __magic_name__ : Any =torch.stack([samplea, samplea, samplea] , dim=0 ) __magic_name__ : Optional[int] =torch.arange(__snake_case )[0:3, None].repeat(1 , __snake_case ) __magic_name__ : int =model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) ) __magic_name__ : Optional[int] =scheduler.batch_step_no_noise(__snake_case , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) ) __magic_name__ : Tuple =torch.sum(torch.abs(__snake_case ) ) __magic_name__ : Union[str, Any] =torch.mean(torch.abs(__snake_case ) ) assert abs(result_sum.item() - 1153.1833 ) < 1E-2 assert abs(result_mean.item() - 0.5005 ) < 1E-3 def A__ ( self :int ): '''simple docstring''' __magic_name__ : Any =self.scheduler_classes[0] __magic_name__ : List[Any] =self.get_scheduler_config() __magic_name__ : Union[str, Any] =scheduler_class(**__snake_case ) __magic_name__ : List[Any] =len(__snake_case ) __magic_name__ : int =self.dummy_model() __magic_name__ : Tuple =self.dummy_sample_deter __magic_name__ : Optional[Any] =torch.manual_seed(0 ) for t in reversed(range(__snake_case ) ): # 1. predict noise residual __magic_name__ : Optional[Any] =model(__snake_case , __snake_case ) # 2. predict previous mean of sample x_t-1 __magic_name__ : Dict =scheduler.step(__snake_case , __snake_case , __snake_case , generator=__snake_case ).prev_sample __magic_name__ : Optional[Any] =pred_prev_sample __magic_name__ : str =torch.sum(torch.abs(__snake_case ) ) __magic_name__ : List[Any] =torch.mean(torch.abs(__snake_case ) ) assert abs(result_sum.item() - 258.9606 ) < 1E-2 assert abs(result_mean.item() - 0.3372 ) < 1E-3 def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ : Optional[Any] =self.scheduler_classes[0] __magic_name__ : Any =self.get_scheduler_config(prediction_type="""v_prediction""" ) __magic_name__ : int =scheduler_class(**__snake_case ) __magic_name__ : Optional[Any] =len(__snake_case ) __magic_name__ : str =self.dummy_model() __magic_name__ : str =self.dummy_sample_deter __magic_name__ : List[str] =torch.manual_seed(0 ) for t in reversed(range(__snake_case ) ): # 1. predict noise residual __magic_name__ : Optional[int] =model(__snake_case , __snake_case ) # 2. predict previous mean of sample x_t-1 __magic_name__ : str =scheduler.step(__snake_case , __snake_case , __snake_case , generator=__snake_case ).prev_sample __magic_name__ : List[Any] =pred_prev_sample __magic_name__ : Optional[int] =torch.sum(torch.abs(__snake_case ) ) __magic_name__ : List[Any] =torch.mean(torch.abs(__snake_case ) ) assert abs(result_sum.item() - 202.0296 ) < 1E-2 assert abs(result_mean.item() - 0.2631 ) < 1E-3 def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Tuple =self.scheduler_classes[0] __magic_name__ : str =self.get_scheduler_config() __magic_name__ : Optional[int] =scheduler_class(**__snake_case ) __magic_name__ : Dict =[1_00, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=__snake_case ) __magic_name__ : int =scheduler.timesteps for i, timestep in enumerate(__snake_case ): if i == len(__snake_case ) - 1: __magic_name__ : Any =-1 else: __magic_name__ : List[str] =timesteps[i + 1] __magic_name__ : Tuple =scheduler.previous_timestep(__snake_case ) __magic_name__ : Any =prev_t.item() self.assertEqual(__snake_case , __snake_case ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : List[Any] =self.scheduler_classes[0] __magic_name__ : List[Any] =self.get_scheduler_config() __magic_name__ : Union[str, Any] =scheduler_class(**__snake_case ) __magic_name__ : Tuple =[1_00, 87, 50, 51, 0] with self.assertRaises(__snake_case , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=__snake_case ) def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Union[str, Any] =self.scheduler_classes[0] __magic_name__ : Dict =self.get_scheduler_config() __magic_name__ : Optional[int] =scheduler_class(**__snake_case ) __magic_name__ : int =[1_00, 87, 50, 1, 0] __magic_name__ : Optional[Any] =len(__snake_case ) with self.assertRaises(__snake_case , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=__snake_case , timesteps=__snake_case ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : Union[str, Any] =self.scheduler_classes[0] __magic_name__ : Dict =self.get_scheduler_config() __magic_name__ : Union[str, Any] =scheduler_class(**__snake_case ) __magic_name__ : Union[str, Any] =[scheduler.config.num_train_timesteps] with self.assertRaises( __snake_case , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=__snake_case )
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from pathlib import Path import fire from tqdm import tqdm def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ): try: import datasets except (ModuleNotFoundError, ImportError): raise ImportError("""run pip install datasets""" ) __magic_name__ : Dict =F"{src_lang}-{tgt_lang}" print(F"Converting {dataset}-{pair}" ) __magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase ) if save_dir is None: __magic_name__ : Optional[int] =F"{dataset}-{pair}" __magic_name__ : int =Path(lowerCamelCase ) save_dir.mkdir(exist_ok=lowerCamelCase ) for split in ds.keys(): print(F"Splitting {split} with {ds[split].num_rows} records" ) # to save to val.source, val.target like summary datasets __magic_name__ : Dict ="""val""" if split == """validation""" else split __magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" ) __magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" ) __magic_name__ : Optional[Any] =src_path.open("""w+""" ) __magic_name__ : List[Any] =tgt_path.open("""w+""" ) # reader is the bottleneck so writing one record at a time doesn't slow things down for x in tqdm(ds[split] ): __magic_name__ : str =x["""translation"""] src_fp.write(ex[src_lang] + """\n""" ) tgt_fp.write(ex[tgt_lang] + """\n""" ) print(F"Saved {dataset} dataset to {save_dir}" ) if __name__ == "__main__": fire.Fire(download_wmt_dataset)
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1
import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import XLMRobertaTokenizerFast from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = KandinskyInpaintPipeline UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""] UpperCamelCase = [ """prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image""", ] UpperCamelCase = [ """generator""", """height""", """width""", """latents""", """guidance_scale""", """negative_prompt""", """num_inference_steps""", """return_dict""", """guidance_scale""", """num_images_per_prompt""", """output_type""", """return_dict""", ] UpperCamelCase = False @property def A__ ( self :Union[str, Any] ): '''simple docstring''' return 32 @property def A__ ( self :Optional[Any] ): '''simple docstring''' return 32 @property def A__ ( self :List[Any] ): '''simple docstring''' return self.time_input_dim @property def A__ ( self :Dict ): '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self :List[Any] ): '''simple docstring''' return 1_00 @property def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" ) return tokenizer @property def A__ ( self :str ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : str =MCLIPConfig( numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , ) __magic_name__ : Tuple =MultilingualCLIP(__snake_case ) __magic_name__ : Optional[int] =text_encoder.eval() return text_encoder @property def A__ ( self :Dict ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Optional[Any] ={ """in_channels""": 9, # Out channels is double in channels because predicts mean and variance """out_channels""": 8, """addition_embed_type""": """text_image""", """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""), """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""), """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""", """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2), """layers_per_block""": 1, """encoder_hid_dim""": self.text_embedder_hidden_size, """encoder_hid_dim_type""": """text_image_proj""", """cross_attention_dim""": self.cross_attention_dim, """attention_head_dim""": 4, """resnet_time_scale_shift""": """scale_shift""", """class_embed_type""": None, } __magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case ) return model @property def A__ ( self :List[str] ): '''simple docstring''' return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def A__ ( self :Tuple ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs ) return model def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[str] =self.dummy_text_encoder __magic_name__ : Optional[Any] =self.dummy_tokenizer __magic_name__ : Optional[Any] =self.dummy_unet __magic_name__ : Tuple =self.dummy_movq __magic_name__ : List[str] =DDIMScheduler( num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , ) __magic_name__ : str ={ """text_encoder""": text_encoder, """tokenizer""": tokenizer, """unet""": unet, """scheduler""": scheduler, """movq""": movq, } return components def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ): '''simple docstring''' __magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case ) # create init_image __magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0] __magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) ) # create mask __magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa ) __magic_name__ : Any =0 if str(__snake_case ).startswith("""mps""" ): __magic_name__ : Dict =torch.manual_seed(__snake_case ) else: __magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case ) __magic_name__ : List[Any] ={ """prompt""": """horse""", """image""": init_image, """mask_image""": mask, """image_embeds""": image_embeds, """negative_image_embeds""": negative_image_embeds, """generator""": generator, """height""": 64, """width""": 64, """num_inference_steps""": 2, """guidance_scale""": 4.0, """output_type""": """np""", } return inputs def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Tuple ="""cpu""" __magic_name__ : List[Any] =self.get_dummy_components() __magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case ) __magic_name__ : Tuple =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) ) __magic_name__ : List[Any] =output.images __magic_name__ : Any =pipe( **self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0] __magic_name__ : int =image[0, -3:, -3:, -1] __magic_name__ : str =image_from_tuple[0, -3:, -3:, -1] print(f"image.shape {image.shape}" ) assert image.shape == (1, 64, 64, 3) __magic_name__ : Optional[Any] =np.array( [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" def A__ ( self :Dict ): '''simple docstring''' super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) @slow @require_torch_gpu class __A ( unittest.TestCase ): def A__ ( self :List[Any] ): '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : List[str] =load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" ) __magic_name__ : int =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" ) __magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa ) __magic_name__ : Any =0 __magic_name__ : int ="""a hat""" __magic_name__ : int =KandinskyPriorPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa ) pipe_prior.to(__snake_case ) __magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa ) __magic_name__ : int =pipeline.to(__snake_case ) pipeline.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 ) __magic_name__ , __magic_name__ : Dict =pipe_prior( __snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple() __magic_name__ : Optional[Any] =pipeline( __snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , ) __magic_name__ : Optional[int] =output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(__snake_case , __snake_case )
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from __future__ import annotations from fractions import Fraction def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): return ( num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =[] __magic_name__ : List[Any] =11 __magic_name__ : Tuple =int("""1""" + """0""" * digit_len ) for num in range(lowerCamelCase , lowerCamelCase ): while den <= 99: if (num != den) and (num % 10 == den // 10) and (den % 10 != 0): if is_digit_cancelling(lowerCamelCase , lowerCamelCase ): solutions.append(F"{num}/{den}" ) den += 1 num += 1 __magic_name__ : List[str] =10 return solutions def lowerCAmelCase_ ( lowerCamelCase = 2 ): __magic_name__ : str =1.0 for fraction in fraction_list(lowerCamelCase ): __magic_name__ : int =Fraction(lowerCamelCase ) result *= frac.denominator / frac.numerator return int(lowerCamelCase ) if __name__ == "__main__": print(solution())
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1
def lowerCAmelCase_ ( lowerCamelCase = 1000 ): __magic_name__ : Optional[int] =-1 __magic_name__ : Tuple =0 for a in range(1 , n // 3 ): # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c __magic_name__ : str =(n * n - 2 * a * n) // (2 * n - 2 * a) __magic_name__ : Tuple =n - a - b if c * c == (a * a + b * b): __magic_name__ : Any =a * b * c if candidate >= product: __magic_name__ : Optional[int] =candidate return product if __name__ == "__main__": print(F"""{solution() = }""")
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from collections.abc import Callable from math import pi, sqrt from random import uniform from statistics import mean def lowerCAmelCase_ ( lowerCamelCase ): # A local function to see if a dot lands in the circle. def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool: __magic_name__ : Dict =sqrt((x**2) + (y**2) ) # Our circle has a radius of 1, so a distance # greater than 1 would land outside the circle. return distance_from_centre <= 1 # The proportion of guesses that landed in the circle __magic_name__ : Union[str, Any] =mean( int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) ) for _ in range(lowerCamelCase ) ) # The ratio of the area for circle to square is pi/4. __magic_name__ : List[Any] =proportion * 4 print(F"The estimated value of pi is {pi_estimate}" ) print(F"The numpy value of pi is {pi}" ) print(F"The total error is {abs(pi - pi_estimate )}" ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ): return mean( function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ): def identity_function(lowerCamelCase ) -> float: return x __magic_name__ : Optional[int] =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) __magic_name__ : str =(max_value * max_value - min_value * min_value) / 2 print("""******************""" ) print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {expected_value}" ) print(F"Total error is {abs(estimated_value - expected_value )}" ) print("""******************""" ) def lowerCAmelCase_ ( lowerCamelCase ): def function_to_integrate(lowerCamelCase ) -> float: return sqrt(4.0 - x * x ) __magic_name__ : Dict =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , 0.0 , 2.0 ) print("""******************""" ) print("""Estimating pi using area_under_curve_estimator""" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {pi}" ) print(F"Total error is {abs(estimated_value - pi )}" ) print("""******************""" ) if __name__ == "__main__": import doctest doctest.testmod()
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1
from __future__ import annotations import numpy as np def __lowercase ( snake_case ): """simple docstring""" __magic_name__ , __magic_name__ :Optional[int] = np.shape(snake_case ) if rows != columns: __magic_name__ :Dict = ( '''\'table\' has to be of square shaped array but got a ''' f'''{rows}x{columns} array:\n{table}''' ) raise ValueError(snake_case ) __magic_name__ :List[str] = np.zeros((rows, columns) ) __magic_name__ :Union[str, Any] = np.zeros((rows, columns) ) for i in range(snake_case ): for j in range(snake_case ): __magic_name__ :List[str] = sum(lower[i][k] * upper[k][j] for k in range(snake_case ) ) if upper[j][j] == 0: raise ArithmeticError('''No LU decomposition exists''' ) __magic_name__ :str = (table[i][j] - total) / upper[j][j] __magic_name__ :int = 1 for j in range(snake_case, snake_case ): __magic_name__ :Any = sum(lower[i][k] * upper[k][j] for k in range(snake_case ) ) __magic_name__ :Dict = table[i][j] - total return lower, upper if __name__ == "__main__": import doctest doctest.testmod()
0
import os from typing import Dict, List, Union import tensorflow as tf from keras_nlp.tokenizers import BytePairTokenizer from tensorflow_text import pad_model_inputs from .tokenization_gpta import GPTaTokenizer class __A ( tf.keras.layers.Layer ): def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ): '''simple docstring''' super().__init__() __magic_name__ : Optional[int] =pad_token_id __magic_name__ : List[Any] =max_length __magic_name__ : Dict =vocab __magic_name__ : int =merges __magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case ) @classmethod def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ): '''simple docstring''' __magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()] __magic_name__ : str =tokenizer.get_vocab() return cls(__snake_case , __snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ): '''simple docstring''' __magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case ) return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Optional[Any] , __snake_case :List[Any] ): '''simple docstring''' return cls(**__snake_case ) def A__ ( self :Union[str, Any] ): '''simple docstring''' return { "vocab": self.vocab, "merges": self.merges, "max_length": self.max_length, "pad_token_id": self.pad_token_id, } def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ): '''simple docstring''' __magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case ) __magic_name__ : Tuple =tf.ones_like(__snake_case ) if self.pad_token_id is not None: # pad the tokens up to max length __magic_name__ : Tuple =max_length if max_length is not None else self.max_length if max_length is not None: __magic_name__ , __magic_name__ : Tuple =pad_model_inputs( __snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id ) return {"attention_mask": attention_mask, "input_ids": input_ids}
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0
def _A ( ) -> list[list[int]]: """simple docstring""" return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )] __snake_case = generate_large_matrix() __snake_case = ( [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]], [[3, 2], [1, 0]], [[7, 7, 6]], [[7, 7, 6], [-1, -2, -3]], grid, ) def _A ( _lowercase ) -> None: """simple docstring""" assert all(row == sorted(_lowercase , reverse=_lowercase ) for row in grid ) assert all(list(_lowercase ) == sorted(_lowercase , reverse=_lowercase ) for col in zip(*_lowercase ) ) def _A ( _lowercase ) -> int: """simple docstring""" __UpperCamelCase = 0 __UpperCamelCase = len(_lowercase ) - 1 # Edge cases such as no values or all numbers are negative. if not array or array[0] < 0: return 0 while right + 1 > left: __UpperCamelCase = (left + right) // 2 __UpperCamelCase = array[mid] # Num must be negative and the index must be greater than or equal to 0. if num < 0 and array[mid - 1] >= 0: return mid if num >= 0: __UpperCamelCase = mid + 1 else: __UpperCamelCase = mid - 1 # No negative numbers so return the last index of the array + 1 which is the length. return len(_lowercase ) def _A ( _lowercase ) -> int: """simple docstring""" __UpperCamelCase = 0 __UpperCamelCase = len(grid[0] ) for i in range(len(_lowercase ) ): __UpperCamelCase = find_negative_index(grid[i][:bound] ) total += bound return (len(_lowercase ) * len(grid[0] )) - total def _A ( _lowercase ) -> int: """simple docstring""" return len([number for row in grid for number in row if number < 0] ) def _A ( _lowercase ) -> int: """simple docstring""" __UpperCamelCase = 0 for row in grid: for i, number in enumerate(_lowercase ): if number < 0: total += len(_lowercase ) - i break return total def _A ( ) -> None: """simple docstring""" from timeit import timeit print('Running benchmarks' ) __UpperCamelCase = ( 'from __main__ import count_negatives_binary_search, ' 'count_negatives_brute_force, count_negatives_brute_force_with_break, grid' ) for func in ( "count_negatives_binary_search", # took 0.7727 seconds "count_negatives_brute_force_with_break", # took 4.6505 seconds "count_negatives_brute_force", # took 12.8160 seconds ): __UpperCamelCase = timeit(f'''{func}(grid=grid)''' , setup=_lowercase , number=5_00 ) print(f'''{func}() took {time:0.4f} seconds''' ) if __name__ == "__main__": import doctest doctest.testmod() benchmark()
1
import math import tensorflow as tf from packaging import version def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype ) __magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase ) return x * tf.tanh(tf.math.softplus(lowerCamelCase ) ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype ) return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) )) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype ) return x * tf.math.sigmoid(coeff * x ) def lowerCAmelCase_ ( lowerCamelCase ): return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ): __magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase ) return a * tf.math.sigmoid(lowerCamelCase ) if version.parse(tf.version.VERSION) >= version.parse("2.4"): def lowerCAmelCase_ ( lowerCamelCase ): return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase ) UpperCAmelCase_ : List[str] = tf.keras.activations.gelu UpperCAmelCase_ : Dict = approximate_gelu_wrap else: UpperCAmelCase_ : Dict = _gelu UpperCAmelCase_ : str = _gelu_new UpperCAmelCase_ : Any = { "gelu": gelu, "gelu_10": gelu_aa, "gelu_fast": gelu_fast, "gelu_new": gelu_new, "glu": glu, "mish": mish, "quick_gelu": quick_gelu, "relu": tf.keras.activations.relu, "sigmoid": tf.keras.activations.sigmoid, "silu": tf.keras.activations.swish, "swish": tf.keras.activations.swish, "tanh": tf.keras.activations.tanh, } def lowerCAmelCase_ ( lowerCamelCase ): if activation_string in ACTaFN: return ACTaFN[activation_string] else: raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
21
0
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 SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> Any: _A = filter(lambda _snake_case : p.requires_grad , model.parameters() ) _A = sum([np.prod(p.size() ) for p in model_parameters] ) return params UpperCAmelCase_ = logging.getLogger(__name__) def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :Dict ) -> Any: if metric == "rouge2": _A = '''{val_avg_rouge2:.4f}-{step_count}''' elif metric == "bleu": _A = '''{val_avg_bleu:.4f}-{step_count}''' elif metric == "em": _A = '''{val_avg_em:.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.''' ) _A = ModelCheckpoint( dirpath=_snake_case , filename=_snake_case , monitor=F'''val_{metric}''' , mode='''max''' , save_top_k=3 , every_n_epochs=1 , ) return checkpoint_callback def SCREAMING_SNAKE_CASE_ ( _snake_case :int , _snake_case :Any ) -> Union[str, Any]: return EarlyStopping( monitor=F'''val_{metric}''' , mode='''min''' if '''loss''' in metric else '''max''' , patience=_snake_case , verbose=_snake_case , ) class lowerCamelCase__ ( pl.Callback): """simple docstring""" def snake_case_ ( self : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] ) -> Dict: _A = {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )} pl_module.logger.log_metrics(__lowerCAmelCase ) @rank_zero_only def snake_case_ ( self : Any , __lowerCAmelCase : pl.Trainer , __lowerCAmelCase : pl.LightningModule , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any]=True ) -> None: logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' ) _A = 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 _A = Path(pl_module.hparams.output_dir ) if type_path == "test": _A = od / '''test_results.txt''' _A = 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. _A = od / f'''{type_path}_results/{trainer.global_step:05d}.txt''' _A = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt''' results_file.parent.mkdir(exist_ok=__lowerCAmelCase ) generations_file.parent.mkdir(exist_ok=__lowerCAmelCase ) with open(__lowerCAmelCase , '''a+''' ) as writer: for key in sorted(__lowerCAmelCase ): if key in ["log", "progress_bar", "preds"]: continue _A = metrics[key] if isinstance(__lowerCAmelCase , torch.Tensor ): _A = val.item() _A = f'''{key}: {val:.6f}\n''' writer.write(__lowerCAmelCase ) if not save_generations: return if "preds" in metrics: _A = '''\n'''.join(metrics['''preds'''] ) generations_file.open('''w+''' ).write(__lowerCAmelCase ) @rank_zero_only def snake_case_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Any ) -> List[str]: try: _A = pl_module.model.model.num_parameters() except AttributeError: _A = pl_module.model.num_parameters() _A = count_trainable_parameters(__lowerCAmelCase ) # 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 snake_case_ ( self : Any , __lowerCAmelCase : pl.Trainer , __lowerCAmelCase : pl.LightningModule ) -> Optional[int]: save_json(pl_module.metrics , pl_module.metrics_save_path ) return self._write_logs(__lowerCAmelCase , __lowerCAmelCase , '''test''' ) @rank_zero_only def snake_case_ ( self : List[Any] , __lowerCAmelCase : pl.Trainer , __lowerCAmelCase : List[str] ) -> Dict: save_json(pl_module.metrics , pl_module.metrics_save_path ) # Uncommenting this will save val generations # return self._write_logs(trainer, pl_module, "valid")
2
from collections.abc import Sequence def lowerCAmelCase_ ( lowerCamelCase = None ): if nums is None or not nums: raise ValueError("""Input sequence should not be empty""" ) __magic_name__ : str =nums[0] for i in range(1 , len(lowerCamelCase ) ): __magic_name__ : Any =nums[i] __magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase ) return ans if __name__ == "__main__": import doctest doctest.testmod() # Try on a sample input from the user UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip()) UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n] print(max_subsequence_sum(array))
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0
'''simple docstring''' import argparse from tax import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM def A_( A : str , A : List[Any] , A : Optional[Any]): UpperCamelCase = AutoConfig.from_pretrained(A) UpperCamelCase = FlaxAutoModelForSeqaSeqLM.from_config(config=A) UpperCamelCase = checkpoints.load_tax_checkpoint(A) UpperCamelCase = 'wi_0' in tax_model['target']['encoder']['layers_0']['mlp'] if config.model_type == "t5": UpperCamelCase = 'SelfAttention' if config.model_type == "longt5" and config.encoder_attention_type == "local": UpperCamelCase = 'LocalSelfAttention' elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCamelCase = 'TransientGlobalSelfAttention' else: raise ValueError( 'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`' ' attribute with a value from [\'local\', \'transient-global].') # Encoder for layer_index in range(config.num_layers): UpperCamelCase = f'''layers_{str(A)}''' # Self-Attention UpperCamelCase = tax_model['target']['encoder'][layer_name]['attention']['key']['kernel'] UpperCamelCase = tax_model['target']['encoder'][layer_name]['attention']['out']['kernel'] UpperCamelCase = tax_model['target']['encoder'][layer_name]['attention']['query']['kernel'] UpperCamelCase = tax_model['target']['encoder'][layer_name]['attention']['value']['kernel'] # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCamelCase = tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale'] # Layer Normalization UpperCamelCase = tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale'] if split_mlp_wi: UpperCamelCase = tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel'] UpperCamelCase = tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel'] else: UpperCamelCase = tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel'] UpperCamelCase = tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel'] # Layer Normalization UpperCamelCase = tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale'] # Assigning UpperCamelCase = flax_model.params['encoder']['block'][str(A)]['layer'] UpperCamelCase = tax_attention_key UpperCamelCase = tax_attention_out UpperCamelCase = tax_attention_query UpperCamelCase = tax_attention_value UpperCamelCase = tax_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCamelCase = tax_global_layer_norm if split_mlp_wi: UpperCamelCase = tax_mlp_wi_a UpperCamelCase = tax_mlp_wi_a else: UpperCamelCase = tax_mlp_wi UpperCamelCase = tax_mlp_wo UpperCamelCase = tax_mlp_layer_norm UpperCamelCase = flax_model_encoder_layer_block # Only for layer 0: UpperCamelCase = tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T UpperCamelCase = tax_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCamelCase = tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T UpperCamelCase = tax_encoder_global_rel_embedding # Assigning UpperCamelCase = tax_model['target']['encoder']['encoder_norm']['scale'] UpperCamelCase = tax_encoder_norm # Decoder for layer_index in range(config.num_layers): UpperCamelCase = f'''layers_{str(A)}''' # Self-Attention UpperCamelCase = tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel'] UpperCamelCase = tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel'] UpperCamelCase = tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel'] UpperCamelCase = tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel'] # Layer Normalization UpperCamelCase = tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][ 'scale' ] # Encoder-Decoder-Attention UpperCamelCase = tax_model['target']['decoder'][layer_name]['encoder_decoder_attention'] UpperCamelCase = tax_enc_dec_attention_module['key']['kernel'] UpperCamelCase = tax_enc_dec_attention_module['out']['kernel'] UpperCamelCase = tax_enc_dec_attention_module['query']['kernel'] UpperCamelCase = tax_enc_dec_attention_module['value']['kernel'] # Layer Normalization UpperCamelCase = tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale'] # MLP if split_mlp_wi: UpperCamelCase = tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel'] UpperCamelCase = tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel'] else: UpperCamelCase = tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel'] UpperCamelCase = tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel'] # Layer Normalization UpperCamelCase = tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale'] # Assigning UpperCamelCase = flax_model.params['decoder']['block'][str(A)]['layer'] UpperCamelCase = tax_attention_key UpperCamelCase = tax_attention_out UpperCamelCase = tax_attention_query UpperCamelCase = tax_attention_value UpperCamelCase = tax_pre_attention_layer_norm UpperCamelCase = tax_enc_dec_attention_key UpperCamelCase = tax_enc_dec_attention_out UpperCamelCase = tax_enc_dec_attention_query UpperCamelCase = tax_enc_dec_attention_value UpperCamelCase = tax_cross_layer_norm if split_mlp_wi: UpperCamelCase = tax_mlp_wi_a UpperCamelCase = tax_mlp_wi_a else: UpperCamelCase = tax_mlp_wi UpperCamelCase = tax_mlp_wo UpperCamelCase = txa_mlp_layer_norm UpperCamelCase = flax_model_decoder_layer_block # Decoder Normalization UpperCamelCase = tax_model['target']['decoder']['decoder_norm']['scale'] UpperCamelCase = txa_decoder_norm # Only for layer 0: UpperCamelCase = tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T UpperCamelCase = tax_decoder_rel_embedding # Token Embeddings UpperCamelCase = tax_model['target']['token_embedder']['embedding'] UpperCamelCase = txa_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in tax_model["target"]["decoder"]: UpperCamelCase = tax_model['target']['decoder']['logits_dense']['kernel'] flax_model.save_pretrained(A) print('T5X Model was sucessfully converted!') if __name__ == "__main__": lowerCAmelCase : Tuple = argparse.ArgumentParser() # Required parameters parser.add_argument( '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.' ) parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.') parser.add_argument( '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.' ) lowerCAmelCase : int = parser.parse_args() convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
3
from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union import pyarrow as pa if TYPE_CHECKING: from .features import FeatureType @dataclass class __A : UpperCamelCase = 42 UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def __call__( self :Union[str, Any] ): '''simple docstring''' return pa.struct({lang: pa.string() for lang in sorted(self.languages )} ) def A__ ( self :List[Any] ): '''simple docstring''' from .features import Value return {k: Value("""string""" ) for k in sorted(self.languages )} @dataclass class __A : UpperCamelCase = None UpperCamelCase = None UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None __magic_name__ : Optional[int] =len(self.languages ) if self.languages else None def __call__( self :List[str] ): '''simple docstring''' return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} ) def A__ ( self :str , __snake_case :str ): '''simple docstring''' __magic_name__ : Optional[int] =set(self.languages ) if self.languages and set(__snake_case ) - lang_set: raise ValueError( f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." ) # Convert dictionary into tuples, splitting out cases where there are # multiple translations for a single language. __magic_name__ : Any =[] for lang, text in translation_dict.items(): if isinstance(__snake_case , __snake_case ): translation_tuples.append((lang, text) ) else: translation_tuples.extend([(lang, el) for el in text] ) # Ensure translations are in ascending order by language code. __magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) ) return {"language": languages, "translation": translations} def A__ ( self :List[Any] ): '''simple docstring''' from .features import Sequence, Value return { "language": Sequence(Value("""string""" ) ), "translation": Sequence(Value("""string""" ) ), }
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0
"""simple docstring""" import importlib import json import os from collections import OrderedDict from typing import Dict, Optional, Union # Build the list of all image processors from ...configuration_utils import PretrainedConfig from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code from ...image_processing_utils import ImageProcessingMixin from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging from .auto_factory import _LazyAutoMapping from .configuration_auto import ( CONFIG_MAPPING_NAMES, AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings, ) __UpperCamelCase : Any = logging.get_logger(__name__) __UpperCamelCase : List[str] = OrderedDict( [ ('''align''', '''EfficientNetImageProcessor'''), ('''beit''', '''BeitImageProcessor'''), ('''bit''', '''BitImageProcessor'''), ('''blip''', '''BlipImageProcessor'''), ('''blip-2''', '''BlipImageProcessor'''), ('''bridgetower''', '''BridgeTowerImageProcessor'''), ('''chinese_clip''', '''ChineseCLIPImageProcessor'''), ('''clip''', '''CLIPImageProcessor'''), ('''clipseg''', '''ViTImageProcessor'''), ('''conditional_detr''', '''ConditionalDetrImageProcessor'''), ('''convnext''', '''ConvNextImageProcessor'''), ('''convnextv2''', '''ConvNextImageProcessor'''), ('''cvt''', '''ConvNextImageProcessor'''), ('''data2vec-vision''', '''BeitImageProcessor'''), ('''deformable_detr''', '''DeformableDetrImageProcessor'''), ('''deit''', '''DeiTImageProcessor'''), ('''deta''', '''DetaImageProcessor'''), ('''detr''', '''DetrImageProcessor'''), ('''dinat''', '''ViTImageProcessor'''), ('''donut-swin''', '''DonutImageProcessor'''), ('''dpt''', '''DPTImageProcessor'''), ('''efficientformer''', '''EfficientFormerImageProcessor'''), ('''efficientnet''', '''EfficientNetImageProcessor'''), ('''flava''', '''FlavaImageProcessor'''), ('''focalnet''', '''BitImageProcessor'''), ('''git''', '''CLIPImageProcessor'''), ('''glpn''', '''GLPNImageProcessor'''), ('''groupvit''', '''CLIPImageProcessor'''), ('''imagegpt''', '''ImageGPTImageProcessor'''), ('''instructblip''', '''BlipImageProcessor'''), ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''), ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''), ('''levit''', '''LevitImageProcessor'''), ('''mask2former''', '''Mask2FormerImageProcessor'''), ('''maskformer''', '''MaskFormerImageProcessor'''), ('''mgp-str''', '''ViTImageProcessor'''), ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''), ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''), ('''mobilevit''', '''MobileViTImageProcessor'''), ('''mobilevit''', '''MobileViTImageProcessor'''), ('''mobilevitv2''', '''MobileViTImageProcessor'''), ('''nat''', '''ViTImageProcessor'''), ('''oneformer''', '''OneFormerImageProcessor'''), ('''owlvit''', '''OwlViTImageProcessor'''), ('''perceiver''', '''PerceiverImageProcessor'''), ('''pix2struct''', '''Pix2StructImageProcessor'''), ('''poolformer''', '''PoolFormerImageProcessor'''), ('''regnet''', '''ConvNextImageProcessor'''), ('''resnet''', '''ConvNextImageProcessor'''), ('''sam''', '''SamImageProcessor'''), ('''segformer''', '''SegformerImageProcessor'''), ('''swiftformer''', '''ViTImageProcessor'''), ('''swin''', '''ViTImageProcessor'''), ('''swin2sr''', '''Swin2SRImageProcessor'''), ('''swinv2''', '''ViTImageProcessor'''), ('''table-transformer''', '''DetrImageProcessor'''), ('''timesformer''', '''VideoMAEImageProcessor'''), ('''tvlt''', '''TvltImageProcessor'''), ('''upernet''', '''SegformerImageProcessor'''), ('''van''', '''ConvNextImageProcessor'''), ('''videomae''', '''VideoMAEImageProcessor'''), ('''vilt''', '''ViltImageProcessor'''), ('''vit''', '''ViTImageProcessor'''), ('''vit_hybrid''', '''ViTHybridImageProcessor'''), ('''vit_mae''', '''ViTImageProcessor'''), ('''vit_msn''', '''ViTImageProcessor'''), ('''xclip''', '''CLIPImageProcessor'''), ('''yolos''', '''YolosImageProcessor'''), ] ) __UpperCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES) def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ): for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items(): if class_name in extractors: lowerCAmelCase = model_type_to_module_name(_UpperCAmelCase ) lowerCAmelCase = importlib.import_module(F'.{module_name}' , 'transformers.models' ) try: return getattr(_UpperCAmelCase , _UpperCAmelCase ) except AttributeError: continue for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items(): if getattr(_UpperCAmelCase , '__name__' , _UpperCAmelCase ) == class_name: return extractor # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main # init and we return the proper dummy to get an appropriate error message. lowerCAmelCase = importlib.import_module('transformers' ) if hasattr(_UpperCAmelCase , _UpperCAmelCase ): return getattr(_UpperCAmelCase , _UpperCAmelCase ) return None def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, os.PathLike] , _UpperCAmelCase : Optional[Union[str, os.PathLike]] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[Dict[str, str]] = None , _UpperCAmelCase : Optional[Union[bool, str]] = None , _UpperCAmelCase : Optional[str] = None , _UpperCAmelCase : bool = False , **_UpperCAmelCase : List[Any] , ): lowerCAmelCase = get_file_from_repo( _UpperCAmelCase , _UpperCAmelCase , cache_dir=_UpperCAmelCase , force_download=_UpperCAmelCase , resume_download=_UpperCAmelCase , proxies=_UpperCAmelCase , use_auth_token=_UpperCAmelCase , revision=_UpperCAmelCase , local_files_only=_UpperCAmelCase , ) if resolved_config_file is None: logger.info( 'Could not locate the image processor configuration file, will try to use the model config instead.' ) return {} with open(_UpperCAmelCase , encoding='utf-8' ) as reader: return json.load(_UpperCAmelCase ) class a : def __init__( self ): """simple docstring""" raise EnvironmentError( 'AutoImageProcessor is designed to be instantiated ' 'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.' ) @classmethod @replace_list_option_in_docstrings(_snake_case ) def UpperCamelCase__ ( cls , _snake_case , **_snake_case ): """simple docstring""" lowerCAmelCase = kwargs.pop('config' , _snake_case ) lowerCAmelCase = kwargs.pop('trust_remote_code' , _snake_case ) lowerCAmelCase = True lowerCAmelCase ,lowerCAmelCase = ImageProcessingMixin.get_image_processor_dict(_snake_case , **_snake_case ) lowerCAmelCase = config_dict.get('image_processor_type' , _snake_case ) lowerCAmelCase = None if "AutoImageProcessor" in config_dict.get('auto_map' , {} ): lowerCAmelCase = config_dict['auto_map']['AutoImageProcessor'] # If we still don't have the image processor class, check if we're loading from a previous feature extractor config # and if so, infer the image processor class from there. if image_processor_class is None and image_processor_auto_map is None: lowerCAmelCase = config_dict.pop('feature_extractor_type' , _snake_case ) if feature_extractor_class is not None: logger.warning( 'Could not find image processor class in the image processor config or the model config. Loading' ' based on pattern matching with the model\'s feature extractor configuration.' ) lowerCAmelCase = feature_extractor_class.replace('FeatureExtractor' , 'ImageProcessor' ) if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ): lowerCAmelCase = config_dict['auto_map']['AutoFeatureExtractor'] lowerCAmelCase = feature_extractor_auto_map.replace('FeatureExtractor' , 'ImageProcessor' ) logger.warning( 'Could not find image processor auto map in the image processor config or the model config.' ' Loading based on pattern matching with the model\'s feature extractor configuration.' ) # If we don't find the image processor class in the image processor config, let's try the model config. if image_processor_class is None and image_processor_auto_map is None: if not isinstance(_snake_case , _snake_case ): lowerCAmelCase = AutoConfig.from_pretrained(_snake_case , **_snake_case ) # It could be in `config.image_processor_type`` lowerCAmelCase = getattr(_snake_case , 'image_processor_type' , _snake_case ) if hasattr(_snake_case , 'auto_map' ) and "AutoImageProcessor" in config.auto_map: lowerCAmelCase = config.auto_map['AutoImageProcessor'] if image_processor_class is not None: lowerCAmelCase = image_processor_class_from_name(_snake_case ) lowerCAmelCase = image_processor_auto_map is not None lowerCAmelCase = image_processor_class is not None or type(_snake_case ) in IMAGE_PROCESSOR_MAPPING lowerCAmelCase = resolve_trust_remote_code( _snake_case , _snake_case , _snake_case , _snake_case ) if has_remote_code and trust_remote_code: lowerCAmelCase = get_class_from_dynamic_module( _snake_case , _snake_case , **_snake_case ) lowerCAmelCase = kwargs.pop('code_revision' , _snake_case ) if os.path.isdir(_snake_case ): image_processor_class.register_for_auto_class() return image_processor_class.from_dict(_snake_case , **_snake_case ) elif image_processor_class is not None: return image_processor_class.from_dict(_snake_case , **_snake_case ) # Last try: we use the IMAGE_PROCESSOR_MAPPING. elif type(_snake_case ) in IMAGE_PROCESSOR_MAPPING: lowerCAmelCase = IMAGE_PROCESSOR_MAPPING[type(_snake_case )] return image_processor_class.from_dict(_snake_case , **_snake_case ) raise ValueError( F'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a ' F'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following ' F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}' ) @staticmethod def UpperCamelCase__ ( _snake_case , _snake_case ): """simple docstring""" IMAGE_PROCESSOR_MAPPING.register(_snake_case , _snake_case )
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from sklearn.metrics import matthews_corrcoef import datasets UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n" UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n" UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n" @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class __A ( datasets.Metric ): def A__ ( self :List[str] ): '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""int32""" ), """references""": datasets.Value("""int32""" ), } ) , reference_urls=[ """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html""" ] , ) def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ): '''simple docstring''' return { "matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ), }
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'''simple docstring''' import unittest from transformers import load_tool from transformers.utils import is_torch_available if is_torch_available(): import torch from transformers.testing_utils import require_torch from .test_tools_common import ToolTesterMixin @require_torch class UpperCAmelCase_ ( unittest.TestCase , _SCREAMING_SNAKE_CASE ): '''simple docstring''' def _lowercase ( self ): """simple docstring""" _lowerCAmelCase = load_tool("""text-to-speech""" ) self.tool.setup() def _lowercase ( self ): """simple docstring""" torch.manual_seed(0 ) _lowerCAmelCase = self.tool("""hey""" ) _lowerCAmelCase = result.to_raw() self.assertTrue( torch.allclose( resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) ) def _lowercase ( self ): """simple docstring""" torch.manual_seed(0 ) _lowerCAmelCase = self.tool("""hey""" ) _lowerCAmelCase = result.to_raw() self.assertTrue( torch.allclose( resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
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import importlib import torch import yaml from omegaconf import OmegaConf from taming.models.vqgan import VQModel def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase ) if display: print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) ) return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ): if conf_path is None: __magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml""" __magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase ) __magic_name__ : Tuple =VQModel(**config.model.params ) if ckpt_path is None: __magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt""" __magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase ) if ".ckpt" in ckpt_path: __magic_name__ : Any =sd["""state_dict"""] model.load_state_dict(lowerCamelCase , strict=lowerCamelCase ) model.to(lowerCamelCase ) del sd return model def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase ) print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" ) __magic_name__ : List[Any] =model.decode(lowerCamelCase ) return xrec def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 ) if reload: __magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase ) importlib.reload(lowerCamelCase ) return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls ) def lowerCAmelCase_ ( lowerCamelCase ): if "target" not in config: raise KeyError("""Expected key `target` to instantiate.""" ) return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ): __magic_name__ : str =instantiate_from_config(lowerCamelCase ) if sd is not None: model.load_state_dict(lowerCamelCase ) if gpu: model.cuda() if eval_mode: model.eval() return {"model": model} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): # load the specified checkpoint if ckpt: __magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" ) __magic_name__ : Any =pl_sd["""global_step"""] print(F"loaded model from global step {global_step}." ) else: __magic_name__ : List[Any] ={"""state_dict""": None} __magic_name__ : Optional[Any] =None __magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""] return model, global_step
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from __future__ import annotations def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: tuple[int, int] , UpperCamelCase__: int ): SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = position SCREAMING_SNAKE_CASE__ = [ (y + 1, x + 2), (y - 1, x + 2), (y + 1, x - 2), (y - 1, x - 2), (y + 2, x + 1), (y + 2, x - 1), (y - 2, x + 1), (y - 2, x - 1), ] SCREAMING_SNAKE_CASE__ = [] for position in positions: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = position if 0 <= y_test < n and 0 <= x_test < n: permissible_positions.append(UpperCamelCase__ ) return permissible_positions def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[list[int]] ): return not any(elem == 0 for row in board for elem in row ) def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[list[int]] , UpperCamelCase__: tuple[int, int] , UpperCamelCase__: int ): if is_complete(UpperCamelCase__ ): return True for position in get_valid_pos(UpperCamelCase__ , len(UpperCamelCase__ ) ): SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = position if board[y][x] == 0: SCREAMING_SNAKE_CASE__ = curr + 1 if open_knight_tour_helper(UpperCamelCase__ , UpperCamelCase__ , curr + 1 ): return True SCREAMING_SNAKE_CASE__ = 0 return False def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: int ): SCREAMING_SNAKE_CASE__ = [[0 for i in range(UpperCamelCase__ )] for j in range(UpperCamelCase__ )] for i in range(UpperCamelCase__ ): for j in range(UpperCamelCase__ ): SCREAMING_SNAKE_CASE__ = 1 if open_knight_tour_helper(UpperCamelCase__ , (i, j) , 1 ): return board SCREAMING_SNAKE_CASE__ = 0 SCREAMING_SNAKE_CASE__ = f'''Open Kight Tour cannot be performed on a board of size {n}''' raise ValueError(UpperCamelCase__ ) if __name__ == "__main__": import doctest doctest.testmod()
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class __A ( unittest.TestCase ): def A__ ( self :Tuple ): '''simple docstring''' debug_launcher(test_script.main ) def A__ ( self :Dict ): '''simple docstring''' debug_launcher(test_ops.main )
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"""simple docstring""" from __future__ import annotations def _snake_case ( _snake_case : list[float] , _snake_case : list[float] ) -> float: '''simple docstring''' _A = sorted(numsa + numsa ) _A , _A = divmod(len(_snake_case ) , 2 ) if mod == 1: return all_numbers[div] else: return (all_numbers[div] + all_numbers[div - 1]) / 2 if __name__ == "__main__": import doctest doctest.testmod() a = [float(x) for x in input('''Enter the elements of first array: ''').split()] a = [float(x) for x in input('''Enter the elements of second array: ''').split()] print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
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UpperCAmelCase_ : Tuple = 0 # The first color of the flag. UpperCAmelCase_ : Any = 1 # The second color of the flag. UpperCAmelCase_ : str = 2 # The third color of the flag. UpperCAmelCase_ : Tuple = (red, white, blue) def lowerCAmelCase_ ( lowerCamelCase ): if not sequence: return [] if len(lowerCamelCase ) == 1: return list(lowerCamelCase ) __magic_name__ : int =0 __magic_name__ : str =len(lowerCamelCase ) - 1 __magic_name__ : Optional[Any] =0 while mid <= high: if sequence[mid] == colors[0]: __magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low] low += 1 mid += 1 elif sequence[mid] == colors[1]: mid += 1 elif sequence[mid] == colors[2]: __magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid] high -= 1 else: __magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values" raise ValueError(lowerCamelCase ) return sequence if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip() UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")] print(F"""{dutch_national_flag_sort(unsorted)}""")
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'''simple docstring''' import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class SCREAMING_SNAKE_CASE (a__ ): lowerCAmelCase = ['''image_processor''', '''tokenizer'''] lowerCAmelCase = '''CLIPImageProcessor''' lowerCAmelCase = ('''CLIPTokenizer''', '''CLIPTokenizerFast''') def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase): '''simple docstring''' __A : Dict = None if "feature_extractor" in kwargs: warnings.warn( 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`' ' instead.' , _UpperCAmelCase , ) __A : Optional[Any] = kwargs.pop('feature_extractor') __A : str = 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__(_UpperCAmelCase , _UpperCAmelCase) def __call__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase): '''simple docstring''' 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: __A : int = self.tokenizer(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase) if images is not None: __A : Optional[int] = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase) if text is not None and images is not None: __A : str = image_features.pixel_values return encoding elif text is not None: return encoding else: return BatchEncoding(data=dict(**_UpperCAmelCase) , tensor_type=_UpperCAmelCase) def SCREAMING_SNAKE_CASE ( self , *_UpperCAmelCase , **_UpperCAmelCase): '''simple docstring''' return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase) def SCREAMING_SNAKE_CASE ( self , *_UpperCAmelCase , **_UpperCAmelCase): '''simple docstring''' return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase) @property def SCREAMING_SNAKE_CASE ( self): '''simple docstring''' __A : Dict = self.tokenizer.model_input_names __A : Tuple = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) @property def SCREAMING_SNAKE_CASE ( self): '''simple docstring''' warnings.warn( '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , _UpperCAmelCase , ) return self.image_processor_class @property def SCREAMING_SNAKE_CASE ( self): '''simple docstring''' warnings.warn( '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _UpperCAmelCase , ) return self.image_processor
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class __A ( UpperCamelCase__ , UpperCamelCase__ ): UpperCamelCase = 1 @register_to_config def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ): '''simple docstring''' __magic_name__ : Dict =None __magic_name__ : List[str] =None __magic_name__ : str =None def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ): '''simple docstring''' __magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case ) def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ): '''simple docstring''' if self.timesteps is None: raise ValueError( """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score __magic_name__ : int =( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) __magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) __magic_name__ : str =std.flatten() while len(std.shape ) < len(score.shape ): __magic_name__ : List[str] =std.unsqueeze(-1 ) __magic_name__ : Union[str, Any] =-score / std # compute __magic_name__ : Tuple =-1.0 / len(self.timesteps ) __magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) __magic_name__ : Dict =beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): __magic_name__ : Any =beta_t.unsqueeze(-1 ) __magic_name__ : Dict =-0.5 * beta_t * x __magic_name__ : Optional[int] =torch.sqrt(__snake_case ) __magic_name__ : int =drift - diffusion**2 * score __magic_name__ : List[str] =x + drift * dt # add noise __magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype ) __magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__( self :List[Any] ): '''simple docstring''' return self.config.num_train_timesteps
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from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available SCREAMING_SNAKE_CASE__ = { '''configuration_informer''': [ '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''InformerConfig''', ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE__ = [ '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''InformerForPrediction''', '''InformerModel''', '''InformerPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_informer import ( INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, InformerForPrediction, InformerModel, InformerPreTrainedModel, ) else: import sys SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from typing import Dict, List, Optional, Union import numpy as np from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy UpperCAmelCase_ : Dict = logging.get_logger(__name__) class __A ( UpperCamelCase__ ): def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =feature_size __magic_name__ : Union[str, Any] =sampling_rate __magic_name__ : List[Any] =padding_value __magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" ) __magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case ) super().__init__(**__snake_case ) def A__ ( self :Any , __snake_case :Union[ BatchFeature, List[BatchFeature], Dict[str, BatchFeature], Dict[str, List[BatchFeature]], List[Dict[str, BatchFeature]], ] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ): '''simple docstring''' if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ): __magic_name__ : Union[str, Any] ={ key: [example[key] for example in processed_features] for key in processed_features[0].keys() } # The model's main input name, usually `input_values`, has be passed for padding if self.model_input_names[0] not in processed_features: raise ValueError( """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`""" f" to this method that includes {self.model_input_names[0]}, but you provided" f" {list(processed_features.keys() )}" ) __magic_name__ : int =processed_features[self.model_input_names[0]] __magic_name__ : Union[str, Any] =( return_attention_mask if return_attention_mask is not None else self.return_attention_mask ) if len(__snake_case ) == 0: if return_attention_mask: __magic_name__ : List[str] =[] return processed_features # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch __magic_name__ : Optional[int] =required_input[0] if isinstance(__snake_case , (list, tuple) ): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. __magic_name__ : Optional[Any] =0 while len(required_input[index] ) == 0: index += 1 if index < len(__snake_case ): __magic_name__ : List[str] =required_input[index][0] if return_tensors is None: if is_tf_tensor(__snake_case ): __magic_name__ : int ="""tf""" elif is_torch_tensor(__snake_case ): __magic_name__ : str ="""pt""" elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ): __magic_name__ : List[Any] ="""np""" else: raise ValueError( f"type of {first_element} unknown: {type(__snake_case )}. " """Should be one of a python, numpy, pytorch or tensorflow object.""" ) for key, value in processed_features.items(): if isinstance(value[0] , (int, float) ): __magic_name__ : List[str] =to_numpy(__snake_case ) else: __magic_name__ : str =[to_numpy(__snake_case ) for v in value] # Convert padding_strategy in PaddingStrategy __magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case ) __magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]] __magic_name__ : Dict =len(__snake_case ) if not all(len(__snake_case ) == batch_size for v in processed_features.values() ): raise ValueError("""Some items in the output dictionary have a different batch size than others.""" ) __magic_name__ : Optional[int] =[] for i in range(__snake_case ): __magic_name__ : Any ={k: v[i] for k, v in processed_features.items()} # truncation __magic_name__ : List[str] =self._truncate( __snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , ) truncated_inputs.append(__snake_case ) if padding_strategy == PaddingStrategy.LONGEST: # make sure that `max_length` cannot be longer than the longest truncated length __magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs ) __magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH __magic_name__ : str ={} for i in range(__snake_case ): # padding __magic_name__ : List[str] =self._pad( truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , ) for key, value in outputs.items(): if key not in batch_outputs: __magic_name__ : Dict =[] if value.dtype is np.dtype(np.floataa ): __magic_name__ : Optional[int] =value.astype(np.floataa ) batch_outputs[key].append(__snake_case ) return BatchFeature(__snake_case , tensor_type=__snake_case ) def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' __magic_name__ : Dict =processed_features[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: __magic_name__ : Any =len(__snake_case ) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length if return_attention_mask and "attention_mask" not in processed_features: __magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa ) if needs_to_be_padded: __magic_name__ : List[Any] =max_length - len(__snake_case ) if self.padding_side == "right": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (0, difference) ) __magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference) __magic_name__ : str =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) elif self.padding_side == "left": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (difference, 0) ) __magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0) __magic_name__ : List[Any] =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) else: raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) ) return processed_features def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' if not truncation: return processed_features elif truncation and max_length is None: raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" ) __magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]] # find `max_length` that fits `pad_to_multiple_of` if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : Any =len(__snake_case ) > max_length if needs_to_be_truncated: __magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length] if "attention_mask" in processed_features: __magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length] return processed_features def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ): '''simple docstring''' if padding is not False: if padding is True: __magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch elif not isinstance(__snake_case , __snake_case ): __magic_name__ : Optional[int] =PaddingStrategy(__snake_case ) elif isinstance(__snake_case , __snake_case ): __magic_name__ : Any =padding else: __magic_name__ : Any =PaddingStrategy.DO_NOT_PAD # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: raise ValueError( f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" ) # Test if we have a padding value if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None): raise ValueError( """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use""" """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" ) return padding_strategy
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from __future__ import annotations import math def _snake_case ( __snake_case ): if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(math.sqrt(__snake_case ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True _lowerCAmelCase = [num for num in range(3, 100_001, 2) if not is_prime(num)] def _snake_case ( __snake_case ): if not isinstance(__snake_case , __snake_case ): raise ValueError('''n must be an integer''' ) if n <= 0: raise ValueError('''n must be >= 0''' ) _UpperCamelCase = [] for num in range(len(__snake_case ) ): _UpperCamelCase = 0 while 2 * i * i <= odd_composites[num]: _UpperCamelCase = odd_composites[num] - 2 * i * i if is_prime(__snake_case ): break i += 1 else: list_nums.append(odd_composites[num] ) if len(__snake_case ) == n: return list_nums return [] def _snake_case ( ): return compute_nums(1 )[0] if __name__ == "__main__": print(f'{solution() = }')
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import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class __A ( nn.Module ): def __init__( self :List[Any] ): '''simple docstring''' super().__init__() __magic_name__ : Tuple =nn.Linear(3 , 4 ) __magic_name__ : Union[str, Any] =nn.BatchNormad(4 ) __magic_name__ : List[str] =nn.Linear(4 , 5 ) def A__ ( self :Dict , __snake_case :Tuple ): '''simple docstring''' return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) ) class __A ( UpperCamelCase__ ): def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ): '''simple docstring''' return (args[0] + 1,) + args[1:], kwargs class __A ( UpperCamelCase__ ): def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ): '''simple docstring''' return output + 1 class __A ( unittest.TestCase ): def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : Tuple =ModelHook() add_hook_to_module(__snake_case , __snake_case ) self.assertEqual(test_model._hf_hook , __snake_case ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : List[str] =ModelHook() add_hook_to_module(__snake_case , __snake_case ) add_hook_to_module(__snake_case , __snake_case , append=__snake_case ) self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case ) self.assertEqual(len(test_model._hf_hook.hooks ) , 2 ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() __magic_name__ : Any =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(x + 1 ) __magic_name__ : Optional[Any] =test_model(x + 2 ) __magic_name__ : int =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : int =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : str =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : List[str] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) assert torch.allclose(__snake_case , __snake_case , atol=1E-5 ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() __magic_name__ : Dict =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(__snake_case ) __magic_name__ : Dict =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Optional[int] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) assert torch.allclose(__snake_case , output + 2 , atol=1E-5 ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : Tuple =ModelForTest() __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Dict =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 ) ) self.assertTrue(outputa.requires_grad ) __magic_name__ : Any =True __magic_name__ : Any =test_model(__snake_case ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) ) self.assertEqual(model.lineara.weight.device , torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[Any] =model(__snake_case ) self.assertEqual(output.device , torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) ) __magic_name__ : int =torch.randn(2 , 3 ).to(0 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , torch.device(0 ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : int =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True} add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : Union[str, Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload __magic_name__ : Tuple ={ """execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True, """offload_buffers""": True, } add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Tuple =torch.randn(2 , 3 ) __magic_name__ : int =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : str =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Optional[int] =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Dict =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Any =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
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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 __A : '''simple docstring''' 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=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , A=0 , ) -> Union[str, Any]: """simple docstring""" _a = parent _a = batch_size _a = seq_length _a = is_training _a = use_input_mask _a = use_token_type_ids _a = use_labels _a = vocab_size _a = hidden_size _a = num_hidden_layers _a = num_attention_heads _a = intermediate_size _a = hidden_act _a = hidden_dropout_prob _a = attention_probs_dropout_prob _a = max_position_embeddings _a = type_vocab_size _a = type_sequence_label_size _a = initializer_range _a = num_labels _a = num_choices _a = scope _a = projection_dim def a__ (self ) -> int: """simple docstring""" _a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) _a = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py _a = random_attention_mask([self.batch_size, self.seq_length] ) _a = None if self.use_token_type_ids: _a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) _a = None _a = None _a = None if self.use_labels: _a = ids_tensor([self.batch_size] , self.type_sequence_label_size ) _a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) _a = ids_tensor([self.batch_size] , self.num_choices ) _a = 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 , ) _a = 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 a__ (self , A , A , A , A , A , A , A ) -> int: """simple docstring""" _a = TFDPRContextEncoder(config=A ) _a = model(A , attention_mask=A , token_type_ids=A ) _a = model(A , token_type_ids=A ) _a = model(A ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) ) def a__ (self , A , A , A , A , A , A , A ) -> Tuple: """simple docstring""" _a = TFDPRQuestionEncoder(config=A ) _a = model(A , attention_mask=A , token_type_ids=A ) _a = model(A , token_type_ids=A ) _a = model(A ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) ) def a__ (self , A , A , A , A , A , A , A ) -> List[str]: """simple docstring""" _a = TFDPRReader(config=A ) _a = 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 a__ (self ) -> str: """simple docstring""" _a = self.prepare_config_and_inputs() ( ( _a ) , ( _a ) , ( _a ) , ( _a ) , ( _a ) , ( _a ) , ( _a ) , ) = config_and_inputs _a = {'''input_ids''': input_ids} return config, inputs_dict @require_tf class __A ( A , A , unittest.TestCase ): '''simple docstring''' __lowerCamelCase : Dict = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) __lowerCamelCase : List[str] = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} __lowerCamelCase : int = False __lowerCamelCase : Any = False __lowerCamelCase : Any = False __lowerCamelCase : List[Any] = False __lowerCamelCase : List[Any] = False def a__ (self ) -> List[str]: """simple docstring""" _a = TFDPRModelTester(self ) _a = ConfigTester(self , config_class=A , hidden_size=37 ) def a__ (self ) -> Tuple: """simple docstring""" self.config_tester.run_common_tests() def a__ (self ) -> Union[str, Any]: """simple docstring""" _a = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*A ) def a__ (self ) -> int: """simple docstring""" _a = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*A ) def a__ (self ) -> Dict: """simple docstring""" _a = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*A ) @slow def a__ (self ) -> List[Any]: """simple docstring""" for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _a = TFDPRContextEncoder.from_pretrained(A ) self.assertIsNotNone(A ) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _a = TFDPRContextEncoder.from_pretrained(A ) self.assertIsNotNone(A ) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _a = TFDPRQuestionEncoder.from_pretrained(A ) self.assertIsNotNone(A ) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _a = TFDPRReader.from_pretrained(A ) self.assertIsNotNone(A ) @require_tf class __A ( unittest.TestCase ): '''simple docstring''' @slow def a__ (self ) -> Any: """simple docstring""" _a = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''' ) _a = tf.constant( [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]] ) # [CLS] hello, is my dog cute? [SEP] _a = model(A )[0] # embedding shape = (1, 768) # compare the actual values for a slice. _a = tf.constant( [ [ 0.03236253, 0.12753335, 0.16818509, 0.00279786, 0.3896933, 0.24264945, 0.2178971, -0.02335227, -0.08481959, -0.14324117, ] ] ) self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1E-4 ) )
11
import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import XLMRobertaTokenizerFast from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = KandinskyInpaintPipeline UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""] UpperCamelCase = [ """prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image""", ] UpperCamelCase = [ """generator""", """height""", """width""", """latents""", """guidance_scale""", """negative_prompt""", """num_inference_steps""", """return_dict""", """guidance_scale""", """num_images_per_prompt""", """output_type""", """return_dict""", ] UpperCamelCase = False @property def A__ ( self :Union[str, Any] ): '''simple docstring''' return 32 @property def A__ ( self :Optional[Any] ): '''simple docstring''' return 32 @property def A__ ( self :List[Any] ): '''simple docstring''' return self.time_input_dim @property def A__ ( self :Dict ): '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self :List[Any] ): '''simple docstring''' return 1_00 @property def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" ) return tokenizer @property def A__ ( self :str ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : str =MCLIPConfig( numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , ) __magic_name__ : Tuple =MultilingualCLIP(__snake_case ) __magic_name__ : Optional[int] =text_encoder.eval() return text_encoder @property def A__ ( self :Dict ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Optional[Any] ={ """in_channels""": 9, # Out channels is double in channels because predicts mean and variance """out_channels""": 8, """addition_embed_type""": """text_image""", """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""), """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""), """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""", """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2), """layers_per_block""": 1, """encoder_hid_dim""": self.text_embedder_hidden_size, """encoder_hid_dim_type""": """text_image_proj""", """cross_attention_dim""": self.cross_attention_dim, """attention_head_dim""": 4, """resnet_time_scale_shift""": """scale_shift""", """class_embed_type""": None, } __magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case ) return model @property def A__ ( self :List[str] ): '''simple docstring''' return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def A__ ( self :Tuple ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs ) return model def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[str] =self.dummy_text_encoder __magic_name__ : Optional[Any] =self.dummy_tokenizer __magic_name__ : Optional[Any] =self.dummy_unet __magic_name__ : Tuple =self.dummy_movq __magic_name__ : List[str] =DDIMScheduler( num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , ) __magic_name__ : str ={ """text_encoder""": text_encoder, """tokenizer""": tokenizer, """unet""": unet, """scheduler""": scheduler, """movq""": movq, } return components def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ): '''simple docstring''' __magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case ) # create init_image __magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0] __magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) ) # create mask __magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa ) __magic_name__ : Any =0 if str(__snake_case ).startswith("""mps""" ): __magic_name__ : Dict =torch.manual_seed(__snake_case ) else: __magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case ) __magic_name__ : List[Any] ={ """prompt""": """horse""", """image""": init_image, """mask_image""": mask, """image_embeds""": image_embeds, """negative_image_embeds""": negative_image_embeds, """generator""": generator, """height""": 64, """width""": 64, """num_inference_steps""": 2, """guidance_scale""": 4.0, """output_type""": """np""", } return inputs def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Tuple ="""cpu""" __magic_name__ : List[Any] =self.get_dummy_components() __magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case ) __magic_name__ : Tuple =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) ) __magic_name__ : List[Any] =output.images __magic_name__ : Any =pipe( **self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0] __magic_name__ : int =image[0, -3:, -3:, -1] __magic_name__ : str =image_from_tuple[0, -3:, -3:, -1] print(f"image.shape {image.shape}" ) assert image.shape == (1, 64, 64, 3) __magic_name__ : Optional[Any] =np.array( [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" def A__ ( self :Dict ): '''simple docstring''' super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) @slow @require_torch_gpu class __A ( unittest.TestCase ): def A__ ( self :List[Any] ): '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : List[str] =load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" ) __magic_name__ : int =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" ) __magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa ) __magic_name__ : Any =0 __magic_name__ : int ="""a hat""" __magic_name__ : int =KandinskyPriorPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa ) pipe_prior.to(__snake_case ) __magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa ) __magic_name__ : int =pipeline.to(__snake_case ) pipeline.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 ) __magic_name__ , __magic_name__ : Dict =pipe_prior( __snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple() __magic_name__ : Optional[Any] =pipeline( __snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , ) __magic_name__ : Optional[int] =output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(__snake_case , __snake_case )
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0
def UpperCamelCase ( lowercase_ ) -> bool: '''simple docstring''' if not all(x.isalpha() for x in string ): raise ValueError("""String must only contain alphabetic characters.""" ) lowercase__ : str = sorted(string.lower() ) return len(lowercase_ ) == len(set(lowercase_ ) ) if __name__ == "__main__": lowerCamelCase__ : Optional[int] = input("""Enter a string """).strip() lowerCamelCase__ : Optional[Any] = is_isogram(input_str) print(f'''{input_str} is {"an" if isogram else "not an"} isogram.''')
12
import inspect import unittest import numpy as np from tests.test_modeling_common import floats_tensor from transformers import MaskaFormerConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel if is_vision_available(): from transformers import MaskaFormerImageProcessor if is_vision_available(): from PIL import Image class __A : def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ): '''simple docstring''' __magic_name__ : Optional[int] =parent __magic_name__ : List[Any] =batch_size __magic_name__ : List[str] =is_training __magic_name__ : List[str] =use_auxiliary_loss __magic_name__ : Union[str, Any] =num_queries __magic_name__ : str =num_channels __magic_name__ : Union[str, Any] =min_size __magic_name__ : Union[str, Any] =max_size __magic_name__ : Optional[int] =num_labels __magic_name__ : Tuple =hidden_dim __magic_name__ : Any =hidden_dim def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to( __snake_case ) __magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case ) __magic_name__ : List[str] =( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5 ).float() __magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long() __magic_name__ : str =self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Dict =MaskaFormerConfig( hidden_size=self.hidden_dim , ) __magic_name__ : str =self.num_queries __magic_name__ : Dict =self.num_labels __magic_name__ : int =[1, 1, 1, 1] __magic_name__ : List[str] =self.num_channels __magic_name__ : str =64 __magic_name__ : List[str] =1_28 __magic_name__ : Optional[Any] =self.hidden_dim __magic_name__ : Tuple =self.hidden_dim __magic_name__ : Optional[int] =self.hidden_dim return config def A__ ( self :Any ): '''simple docstring''' __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs() __magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask} return config, inputs_dict def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ): '''simple docstring''' __magic_name__ : int =output.encoder_hidden_states __magic_name__ : List[str] =output.pixel_decoder_hidden_states __magic_name__ : int =output.transformer_decoder_hidden_states self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , config.decoder_layers ) def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ): '''simple docstring''' with torch.no_grad(): __magic_name__ : List[str] =MaskaFormerModel(config=__snake_case ) model.to(__snake_case ) model.eval() __magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case ) self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(output.encoder_last_hidden_state is not None ) if output_hidden_states: self.check_output_hidden_state(__snake_case , __snake_case ) def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ): '''simple docstring''' __magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case ) model.to(__snake_case ) model.eval() def comm_check_on_output(__snake_case :List[str] ): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.encoder_last_hidden_state is not None ) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): __magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : List[str] =model(__snake_case ) comm_check_on_output(__snake_case ) __magic_name__ : Any =model( pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) comm_check_on_output(__snake_case ) self.parent.assertTrue(result.loss is not None ) self.parent.assertEqual(result.loss.shape , torch.Size([1] ) ) @require_torch class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else () UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {} UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False def A__ ( self :str ): '''simple docstring''' __magic_name__ : Any =MaskaFormerModelTester(self ) __magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case ) def A__ ( self :Dict ): '''simple docstring''' self.config_tester.run_common_tests() def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case ) @unittest.skip(reason="""Mask2Former does not use inputs_embeds""" ) def A__ ( self :List[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" ) def A__ ( self :Dict ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former is not a generative model""" ) def A__ ( self :Optional[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not use token embeddings""" ) def A__ ( self :int ): '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip( reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" ) def A__ ( self :Tuple ): '''simple docstring''' pass @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" ) def A__ ( self :Union[str, Any] ): '''simple docstring''' pass def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : Tuple =model_class(__snake_case ) __magic_name__ : Optional[Any] =inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic __magic_name__ : Tuple =[*signature.parameters.keys()] __magic_name__ : Optional[Any] =["""pixel_values"""] self.assertListEqual(arg_names[:1] , __snake_case ) @slow def A__ ( self :Tuple ): '''simple docstring''' for model_name in ["facebook/mask2former-swin-small-coco-instance"]: __magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =(self.model_tester.min_size,) * 2 __magic_name__ : Union[str, Any] ={ """pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ), """mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ), """class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(), } __magic_name__ : Optional[Any] =self.model_tester.get_config() __magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case ) __magic_name__ : Any =model(**__snake_case ) self.assertTrue(outputs.loss is not None ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case ) __magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case ) self.assertTrue(outputs.attentions is not None ) def A__ ( self :int ): '''simple docstring''' if not self.model_tester.is_training: return __magic_name__ : List[Any] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Dict =model_class(__snake_case ) model.to(__snake_case ) model.train() __magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss loss.backward() def A__ ( self :int ): '''simple docstring''' __magic_name__ : List[str] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Tuple =True __magic_name__ : Optional[int] =True __magic_name__ : int =model_class(__snake_case ).to(__snake_case ) model.train() __magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) __magic_name__ : Optional[int] =outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() __magic_name__ : Optional[int] =outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=__snake_case ) self.assertIsNotNone(encoder_hidden_states.grad ) self.assertIsNotNone(pixel_decoder_hidden_states.grad ) self.assertIsNotNone(transformer_decoder_hidden_states.grad ) self.assertIsNotNone(attentions.grad ) UpperCAmelCase_ : Dict = 1e-4 def lowerCAmelCase_ ( ): __magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_vision @slow class __A ( unittest.TestCase ): @cached_property def A__ ( self :int ): '''simple docstring''' return "facebook/mask2former-swin-small-coco-instance" @cached_property def A__ ( self :int ): '''simple docstring''' return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case ) __magic_name__ : int =self.default_image_processor __magic_name__ : List[Any] =prepare_img() __magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Dict =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : List[str] =model(**__snake_case ) __magic_name__ : Any =torch.tensor( [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Dict =torch.tensor( [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Any =torch.tensor( [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Optional[int] =self.default_image_processor __magic_name__ : Tuple =prepare_img() __magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : str =model(**__snake_case ) # masks_queries_logits __magic_name__ : List[Any] =outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ) __magic_name__ : List[Any] =[ [-8.7839, -9.0056, -8.8121], [-7.4104, -7.0313, -6.5401], [-6.6105, -6.3427, -6.4675], ] __magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) # class_queries_logits __magic_name__ : Any =outputs.class_queries_logits self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) ) __magic_name__ : int =torch.tensor( [ [1.8324, -8.0835, -4.1922], [0.8450, -9.0050, -3.6053], [0.3045, -7.7293, -3.0275], ] ).to(__snake_case ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Any =self.default_image_processor __magic_name__ : Union[str, Any] =image_processor( [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , ) __magic_name__ : str =inputs["""pixel_values"""].to(__snake_case ) __magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]] __magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]] with torch.no_grad(): __magic_name__ : Dict =model(**__snake_case ) self.assertTrue(outputs.loss is not None )
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'''simple docstring''' def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> str: return "\n".join( F'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) ) if __name__ == "__main__": print(multiplication_table(number=5, number_of_terms=10))
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import warnings from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "nvidia/segformer-b0-finetuned-ade-512-512": ( "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json" ), # See all SegFormer models at https://huggingface.co/models?filter=segformer } class __A ( UpperCamelCase__ ): UpperCamelCase = """segformer""" def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ): '''simple docstring''' super().__init__(**__snake_case ) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be""" """ removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , ) __magic_name__ : Dict =num_channels __magic_name__ : str =num_encoder_blocks __magic_name__ : List[Any] =depths __magic_name__ : Optional[Any] =sr_ratios __magic_name__ : List[str] =hidden_sizes __magic_name__ : List[str] =patch_sizes __magic_name__ : Any =strides __magic_name__ : Optional[Any] =mlp_ratios __magic_name__ : str =num_attention_heads __magic_name__ : int =hidden_act __magic_name__ : List[Any] =hidden_dropout_prob __magic_name__ : Optional[Any] =attention_probs_dropout_prob __magic_name__ : Optional[Any] =classifier_dropout_prob __magic_name__ : List[str] =initializer_range __magic_name__ : List[str] =drop_path_rate __magic_name__ : List[Any] =layer_norm_eps __magic_name__ : List[str] =decoder_hidden_size __magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case ) __magic_name__ : Dict =semantic_loss_ignore_index class __A ( UpperCamelCase__ ): UpperCamelCase = version.parse("""1.11""" ) @property def A__ ( self :List[str] ): '''simple docstring''' return OrderedDict( [ ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}), ] ) @property def A__ ( self :Any ): '''simple docstring''' return 1E-4 @property def A__ ( self :int ): '''simple docstring''' return 12
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import argparse import torch from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel from transformers.utils import logging logging.set_verbosity_info() def __UpperCAmelCase ( __a : Union[str, Any] ,__a : List[Any] ,__a : List[str] ,__a : Tuple ) -> Dict: """simple docstring""" _a : Optional[int] = FunnelConfig.from_json_file(__a ) print(F"""Building PyTorch model from configuration: {config}""" ) _a : Optional[Any] = FunnelBaseModel(__a ) if base_model else FunnelModel(__a ) # Load weights from tf checkpoint load_tf_weights_in_funnel(__a ,__a ,__a ) # Save pytorch-model print(F"""Save PyTorch model to {pytorch_dump_path}""" ) torch.save(model.state_dict() ,__a ) if __name__ == "__main__": a__ = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.''' ) parser.add_argument( '''--config_file''', default=None, type=str, required=True, help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''', ) parser.add_argument( '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.''' ) parser.add_argument( '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.''' ) a__ = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model )
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import heapq def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : list[list] =[] # 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(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] ) # chosen_vertices = set of chosen vertices __magic_name__ : Tuple =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 __magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0] chosen_vertices.add(lowerCamelCase ) # 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]: __magic_name__ : Tuple =elem[1][1].index(lowerCamelCase ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(lowerCamelCase ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : Optional[int] = {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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from typing import Optional, Tuple import jax import jax.numpy as jnp from flax import linen as nn from flax.core.frozen_dict import FrozenDict from transformers import CLIPConfig, FlaxPreTrainedModel from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=1E-1_2 ) -> str: """simple docstring""" lowercase__ = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__magic_name__ , axis=1 ) , a_min=__magic_name__ ) ).T lowercase__ = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__magic_name__ , axis=1 ) , a_min=__magic_name__ ) ).T return jnp.matmul(__magic_name__ , norm_emb_a.T ) class A ( nn.Module ): '''simple docstring''' A__ = 42 A__ = jnp.floataa def lowerCamelCase__ (self : Dict ) -> Dict: """simple docstring""" lowercase__ = FlaxCLIPVisionModule(self.config.vision_config ) lowercase__ = nn.Dense(self.config.projection_dim , use_bias=_UpperCAmelCase , dtype=self.dtype ) lowercase__ = self.param("""concept_embeds""" , jax.nn.initializers.ones , (17, self.config.projection_dim) ) lowercase__ = self.param( """special_care_embeds""" , jax.nn.initializers.ones , (3, self.config.projection_dim) ) lowercase__ = self.param("""concept_embeds_weights""" , jax.nn.initializers.ones , (17,) ) lowercase__ = self.param("""special_care_embeds_weights""" , jax.nn.initializers.ones , (3,) ) def __call__(self : List[str] , _UpperCAmelCase : List[Any] ) -> List[str]: """simple docstring""" lowercase__ = self.vision_model(_UpperCAmelCase )[1] lowercase__ = self.visual_projection(_UpperCAmelCase ) lowercase__ = jax_cosine_distance(_UpperCAmelCase , self.special_care_embeds ) lowercase__ = jax_cosine_distance(_UpperCAmelCase , self.concept_embeds ) # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign image inputs lowercase__ = 0.0 lowercase__ = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment lowercase__ = jnp.round(_UpperCAmelCase , 3 ) lowercase__ = jnp.any(special_scores > 0 , axis=1 , keepdims=_UpperCAmelCase ) # Use a lower threshold if an image has any special care concept lowercase__ = is_special_care * 0.01 lowercase__ = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment lowercase__ = jnp.round(_UpperCAmelCase , 3 ) lowercase__ = jnp.any(concept_scores > 0 , axis=1 ) return has_nsfw_concepts class A ( UpperCAmelCase__ ): '''simple docstring''' A__ = CLIPConfig A__ = '''clip_input''' A__ = FlaxStableDiffusionSafetyCheckerModule def __init__(self : List[str] , _UpperCAmelCase : CLIPConfig , _UpperCAmelCase : Optional[Tuple] = None , _UpperCAmelCase : int = 0 , _UpperCAmelCase : jnp.dtype = jnp.floataa , _UpperCAmelCase : bool = True , **_UpperCAmelCase : str , ) -> Dict: """simple docstring""" if input_shape is None: lowercase__ = (1, 224, 224, 3) lowercase__ = self.module_class(config=_UpperCAmelCase , dtype=_UpperCAmelCase , **_UpperCAmelCase ) super().__init__(_UpperCAmelCase , _UpperCAmelCase , input_shape=_UpperCAmelCase , seed=_UpperCAmelCase , dtype=_UpperCAmelCase , _do_init=_do_init ) def lowerCamelCase__ (self : int , _UpperCAmelCase : jax.random.KeyArray , _UpperCAmelCase : Tuple , _UpperCAmelCase : FrozenDict = None ) -> FrozenDict: """simple docstring""" lowercase__ = jax.random.normal(_UpperCAmelCase , _UpperCAmelCase ) lowercase__ , lowercase__ = jax.random.split(_UpperCAmelCase ) lowercase__ = {"""params""": params_rng, """dropout""": dropout_rng} lowercase__ = self.module.init(_UpperCAmelCase , _UpperCAmelCase )["""params"""] return random_params def __call__(self : Optional[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : dict = None , ) -> Dict: """simple docstring""" lowercase__ = jnp.transpose(_UpperCAmelCase , (0, 2, 3, 1) ) return self.module.apply( {"""params""": params or self.params} , jnp.array(_UpperCAmelCase , dtype=jnp.floataa ) , rngs={} , )
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UpperCAmelCase_ : int = range(2, 20 + 1) UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)] UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) ) __magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) ) __magic_name__ , __magic_name__ : Tuple =0, 0 __magic_name__ : Optional[Any] =n - i __magic_name__ : Union[str, Any] =memo.get(lowerCamelCase ) if sub_memo is not None: __magic_name__ : int =sub_memo.get(lowerCamelCase ) if jumps is not None and len(lowerCamelCase ) > 0: # find and make the largest jump without going over __magic_name__ : Dict =-1 for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ): if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: __magic_name__ : Optional[Any] =_k break if max_jump >= 0: __magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump] # since the difference between jumps is cached, add c __magic_name__ : Tuple =diff + c for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ): __magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 ) if new_c > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) else: __magic_name__ : str =[] else: __magic_name__ : List[str] ={c: []} __magic_name__ : List[str] =sub_memo if dn >= max_dn or c + diff >= base[k]: return diff, dn if k > ks[0]: while True: # keep doing smaller jumps __magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped if dn >= max_dn or c + diff >= base[k]: break else: # would be too small a jump, just compute sequential terms instead __magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped __magic_name__ : Tuple =sub_memo[c] # keep jumps sorted by # of terms skipped __magic_name__ : List[Any] =0 while j < len(lowerCamelCase ): if jumps[j][1] > dn: break j += 1 # cache the jump for this value digitsum(b) and c sub_memo[c].insert(lowerCamelCase , (diff, dn, k) ) return (diff, dn) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): if i >= n: return 0, i if k > len(lowerCamelCase ): a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] ) # note: a_i -> b * 10^k + c # ds_b -> digitsum(b) # ds_c -> digitsum(c) __magic_name__ : Tuple =i __magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0 for j in range(len(lowerCamelCase ) ): if j >= k: ds_b += a_i[j] else: ds_c += a_i[j] while i < n: i += 1 __magic_name__ : Optional[Any] =ds_c + ds_b diff += addend __magic_name__ : str =0 for j in range(lowerCamelCase ): __magic_name__ : int =a_i[j] + addend __magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 ) ds_c += a_i[j] if addend > 0: break if addend > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) return diff, i - start_i def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): for j in range(lowerCamelCase , len(lowerCamelCase ) ): __magic_name__ : Tuple =digits[j] + addend if s >= 10: __magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 ) __magic_name__ : int =addend // 10 + quotient else: __magic_name__ : Dict =s __magic_name__ : Any =addend // 10 if addend == 0: break while addend > 0: __magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 ) digits.append(lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase = 10**15 ): __magic_name__ : List[str] =[1] __magic_name__ : str =1 __magic_name__ : str =0 while True: __magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase ) dn += terms_jumped if dn == n - i: break __magic_name__ : int =0 for j in range(len(lowerCamelCase ) ): a_n += digits[j] * 10**j return a_n if __name__ == "__main__": print(F"""{solution() = }""")
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from __future__ import annotations import math def __a ( A__ : int ): if num <= 0: SCREAMING_SNAKE_CASE = F"{num}: Invalid input, please enter a positive integer." raise ValueError(A__ ) SCREAMING_SNAKE_CASE = [True] * (num + 1) SCREAMING_SNAKE_CASE = [] SCREAMING_SNAKE_CASE = 2 SCREAMING_SNAKE_CASE = int(math.sqrt(A__ ) ) while start <= end: # If start is a prime if sieve[start] is True: prime.append(A__ ) # Set multiples of start be False for i in range(start * start , num + 1 , A__ ): if sieve[i] is True: SCREAMING_SNAKE_CASE = False start += 1 for j in range(end + 1 , num + 1 ): if sieve[j] is True: prime.append(A__ ) return prime if __name__ == "__main__": print(prime_sieve(int(input('Enter a positive integer: ').strip())))
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from typing import List from .keymap import KEYMAP, get_character def lowerCAmelCase_ ( lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] ) handle += [key] setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator def lowerCAmelCase_ ( *lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] ) handle += keys setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator class __A ( UpperCamelCase__ ): def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ): '''simple docstring''' __magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case ) if not hasattr(__snake_case , """key_handler""" ): setattr(__snake_case , """key_handler""" , {} ) setattr(__snake_case , """handle_input""" , KeyHandler.handle_input ) for value in attrs.values(): __magic_name__ : int =getattr(__snake_case , """handle_key""" , [] ) for key in handled_keys: __magic_name__ : List[str] =value return new_cls @staticmethod def A__ ( cls :Optional[int] ): '''simple docstring''' __magic_name__ : Union[str, Any] =get_character() if char != KEYMAP["undefined"]: __magic_name__ : Optional[int] =ord(__snake_case ) __magic_name__ : int =cls.key_handler.get(__snake_case ) if handler: __magic_name__ : Dict =char return handler(cls ) else: return None def lowerCAmelCase_ ( cls ): return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
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from __future__ import annotations from math import pi # Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of # Pi and the function UpperCAmelCase_ : Optional[Any] = 1.0_5457_1817e-34 # unit of ℏ : J * s UpperCAmelCase_ : Union[str, Any] = 3e8 # unit of c : m * s^-1 def __SCREAMING_SNAKE_CASE ( a__ : float ,a__ : float ,a__ : float ) -> dict[str, float]: if (force, area, distance).count(0 ) != 1: raise ValueError("""One and only one argument must be 0""" ) if force < 0: raise ValueError("""Magnitude of force can not be negative""" ) if distance < 0: raise ValueError("""Distance can not be negative""" ) if area < 0: raise ValueError("""Area can not be negative""" ) if force == 0: __A : List[Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / ( 240 * (distance) ** 4 ) return {"force": force} elif area == 0: __A : Tuple = (240 * force * (distance) ** 4) / ( REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 ) return {"area": area} elif distance == 0: __A : Optional[int] = ( (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force) ) ** (1 / 4) return {"distance": distance} raise ValueError("""One and only one argument must be 0""" ) # Run doctest if __name__ == "__main__": import doctest doctest.testmod()
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import os import jsonlines import numpy as np from tqdm import tqdm UpperCAmelCase_ : Dict = 2048 UpperCAmelCase_ : int = 4096 UpperCAmelCase_ : Any = 42 UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false") UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4} def lowerCAmelCase_ ( lowerCamelCase ): def choose_first(lowerCamelCase , lowerCamelCase=False ): assert isinstance(lowerCamelCase , lowerCamelCase ) if len(lowerCamelCase ) == 1: __magic_name__ : List[str] =answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: __magic_name__ : Tuple ={k: [a[k]] for k in a} if len(a["""start_token"""] ) > 0: break return a __magic_name__ : str ={"""id""": example["""id"""]} __magic_name__ : List[Any] =example["""annotations"""] __magic_name__ : List[str] =annotation["""yes_no_answer"""] if 0 in yes_no_answer or 1 in yes_no_answer: __magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""] __magic_name__ : List[str] =[] __magic_name__ : Dict =[] __magic_name__ : str =["""<cls>"""] else: __magic_name__ : Tuple =["""short"""] __magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] ) if len(out["""start_token"""] ) == 0: # answer will be long if short is not available __magic_name__ : Tuple =["""long"""] __magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase ) __magic_name__ : List[Any] =[] answer.update(lowerCamelCase ) # disregard some samples if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]: __magic_name__ : Any =True else: __magic_name__ : List[str] =False __magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""] if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ): raise ValueError("""Issue in ID""" , example["""id"""] ) return answer def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase ) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : Any =example["""document"""]["""tokens"""] __magic_name__ : str =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": -100, # ignore index in cross-entropy "end_token": -100, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples __magic_name__ : Dict =["""start_token""", """end_token"""] answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10 __magic_name__ : Tuple =example["""document"""]["""tokens"""] __magic_name__ : Optional[int] =answer["""start_token"""] __magic_name__ : List[Any] =answer["""end_token"""] __magic_name__ : Optional[Any] =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 __magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] ) # checking above code if assertion: __magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] ) if new != old: print("""ID:""" , example["""id"""] ) print("""New:""" , lowerCamelCase , end="""\n""" ) print("""Old:""" , lowerCamelCase , end="""\n\n""" ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ): # overlap will be of doc_stride - q_len __magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase ) __magic_name__ : Union[str, Any] =out["""answer"""] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } __magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids __magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : List[str] =[] __magic_name__ : int =[] __magic_name__ : List[str] =input_ids[:q_len] __magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Tuple =input_ids[i:end_index] inputs.append(q_indices + slice ) category.append(answer["""category"""][0] ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-100] * len(lowerCamelCase ), "end_token": [-100] * len(lowerCamelCase ), "category": category, }, } __magic_name__ : int =out["""context"""].split() __magic_name__ : Any =splitted_context[answer["""end_token"""]] __magic_name__ : str =len( tokenizer( """ """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids ) __magic_name__ : Optional[int] =len( tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids ) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token __magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids ) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 __magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive __magic_name__ : Dict =answer["""start_token"""] __magic_name__ : int =answer["""end_token"""] if assertion: __magic_name__ : Any =tokenizer.decode(lowerCamelCase ) if answer["span"] != new: print("""ISSUE IN TOKENIZATION""" ) print("""OLD:""" , answer["""span"""] ) print("""NEW:""" , lowerCamelCase , end="""\n\n""" ) if len(lowerCamelCase ) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } __magic_name__ : Any =input_ids[:q_len] __magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) __magic_name__ : Any =[] __magic_name__ : List[str] =[] __magic_name__ : List[str] =[] __magic_name__ : str =[] # null, yes, no, long, short for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Dict =input_ids[i:end_index] inputs.append(q_indices + slice ) assert len(inputs[-1] ) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: __magic_name__ : List[Any] =start_token - i + q_len __magic_name__ : Optional[Any] =end_token - i + q_len answers_category.append(answer["""category"""][0] ) # ["short"] -> "short" else: __magic_name__ : Optional[Any] =-100 __magic_name__ : Optional[Any] =-100 answers_category.append("""null""" ) __magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1] answers_start_token.append(lowerCamelCase ) answers_end_token.append(lowerCamelCase ) if assertion: if new != old and new != [tokenizer.cls_token_id]: print("""ISSUE in strided for ID:""" , example["""id"""] ) print("""New:""" , tokenizer.decode(lowerCamelCase ) ) print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ): __magic_name__ : List[Any] =get_strided_contexts_and_ans( lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , ) return example def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): with jsonlines.open(lowerCamelCase , """a""" ) as writer: for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ): __magic_name__ : int =example["""labels"""] for ids, start, end, cat in zip( example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { """input_ids""": ids, """start_token""": start, """end_token""": end, """category""": CATEGORY_MAPPING[cat], } ) if __name__ == "__main__": from datasets import load_dataset from transformers import BigBirdTokenizer UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions") UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"] UpperCAmelCase_ : Optional[int] = { "tokenizer": tokenizer, "doc_stride": DOC_STRIDE, "max_length": MAX_LENGTH, "assertion": False, } UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs) UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"]) print(data) np.random.seed(SEED) UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl" save_to_disk(data, file_name=cache_file_name)
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'''simple docstring''' from typing import TYPE_CHECKING from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _SCREAMING_SNAKE_CASE = { "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"], "feature_extraction_mctct": ["MCTCTFeatureExtractor"], "processing_mctct": ["MCTCTProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _SCREAMING_SNAKE_CASE = [ "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST", "MCTCTForCTC", "MCTCTModel", "MCTCTPreTrainedModel", ] if TYPE_CHECKING: from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig from .feature_extraction_mctct import MCTCTFeatureExtractor from .processing_mctct import MCTCTProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel else: import sys _SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Tuple = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json", "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json", # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl } class __A ( UpperCamelCase__ ): UpperCamelCase = """xlm-roberta-xl""" def __init__( self :Dict , __snake_case :Optional[Any]=25_08_80 , __snake_case :List[Any]=25_60 , __snake_case :Optional[Any]=36 , __snake_case :Any=32 , __snake_case :int=1_02_40 , __snake_case :List[Any]="gelu" , __snake_case :Union[str, Any]=0.1 , __snake_case :Optional[Any]=0.1 , __snake_case :str=5_14 , __snake_case :Union[str, Any]=1 , __snake_case :Optional[int]=0.02 , __snake_case :str=1E-05 , __snake_case :str=1 , __snake_case :int=0 , __snake_case :Tuple=2 , __snake_case :Optional[int]="absolute" , __snake_case :str=True , __snake_case :Any=None , **__snake_case :Dict , ): '''simple docstring''' super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case ) __magic_name__ : List[str] =vocab_size __magic_name__ : List[str] =hidden_size __magic_name__ : Union[str, Any] =num_hidden_layers __magic_name__ : Any =num_attention_heads __magic_name__ : Any =hidden_act __magic_name__ : List[str] =intermediate_size __magic_name__ : Any =hidden_dropout_prob __magic_name__ : Union[str, Any] =attention_probs_dropout_prob __magic_name__ : Any =max_position_embeddings __magic_name__ : Any =type_vocab_size __magic_name__ : List[str] =initializer_range __magic_name__ : Optional[int] =layer_norm_eps __magic_name__ : Dict =position_embedding_type __magic_name__ : Any =use_cache __magic_name__ : Dict =classifier_dropout class __A ( UpperCamelCase__ ): @property def A__ ( self :Dict ): '''simple docstring''' if self.task == "multiple-choice": __magic_name__ : str ={0: """batch""", 1: """choice""", 2: """sequence"""} else: __magic_name__ : Optional[Any] ={0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ] )
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"""simple docstring""" from collections import defaultdict from math import gcd def lowerCamelCase__ ( __snake_case = 1_50_00_00 ) -> int: """simple docstring""" _UpperCamelCase = defaultdict(__snake_case ) _UpperCamelCase = 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 _UpperCamelCase = 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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from pathlib import Path import fire from tqdm import tqdm def lowerCAmelCase_ ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ): try: import datasets except (ModuleNotFoundError, ImportError): raise ImportError("""run pip install datasets""" ) __magic_name__ : Dict =F"{src_lang}-{tgt_lang}" print(F"Converting {dataset}-{pair}" ) __magic_name__ : Dict =datasets.load_dataset(lowerCamelCase , lowerCamelCase ) if save_dir is None: __magic_name__ : Optional[int] =F"{dataset}-{pair}" __magic_name__ : int =Path(lowerCamelCase ) save_dir.mkdir(exist_ok=lowerCamelCase ) for split in ds.keys(): print(F"Splitting {split} with {ds[split].num_rows} records" ) # to save to val.source, val.target like summary datasets __magic_name__ : Dict ="""val""" if split == """validation""" else split __magic_name__ : List[Any] =save_dir.joinpath(F"{fn}.source" ) __magic_name__ : Optional[int] =save_dir.joinpath(F"{fn}.target" ) __magic_name__ : Optional[Any] =src_path.open("""w+""" ) __magic_name__ : List[Any] =tgt_path.open("""w+""" ) # reader is the bottleneck so writing one record at a time doesn't slow things down for x in tqdm(ds[split] ): __magic_name__ : str =x["""translation"""] src_fp.write(ex[src_lang] + """\n""" ) tgt_fp.write(ex[tgt_lang] + """\n""" ) print(F"Saved {dataset} dataset to {save_dir}" ) if __name__ == "__main__": fire.Fire(download_wmt_dataset)
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def _lowercase( __a : Optional[Any] , __a : Optional[int] ): # "extended trapezoidal rule" # int(f) = dx/2 * (f1 + 2f2 + ... + fn) a__ =(boundary[1] - boundary[0]) / steps a__ =boundary[0] a__ =boundary[1] a__ =make_points(__a , __a , __a ) a__ =0.0 y += (h / 2.0) * f(__a ) for i in x_i: # print(i) y += h * f(__a ) y += (h / 2.0) * f(__a ) return y def _lowercase( __a : Tuple , __a : str , __a : Union[str, Any] ): a__ =a + h while x < (b - h): yield x a__ =x + h def _lowercase( __a : Dict ): # enter your function here a__ =(x - 0) * (x - 0) return y def _lowercase( ): a__ =0.0 # Lower bound of integration a__ =1.0 # Upper bound of integration a__ =10.0 # define number of steps or resolution a__ =[a, b] # define boundary of integration a__ =method_a(__a , __a ) print(f"""y = {y}""" ) if __name__ == "__main__": main()
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from __future__ import annotations from fractions import Fraction def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): return ( num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =[] __magic_name__ : List[Any] =11 __magic_name__ : Tuple =int("""1""" + """0""" * digit_len ) for num in range(lowerCamelCase , lowerCamelCase ): while den <= 99: if (num != den) and (num % 10 == den // 10) and (den % 10 != 0): if is_digit_cancelling(lowerCamelCase , lowerCamelCase ): solutions.append(F"{num}/{den}" ) den += 1 num += 1 __magic_name__ : List[str] =10 return solutions def lowerCAmelCase_ ( lowerCamelCase = 2 ): __magic_name__ : str =1.0 for fraction in fraction_list(lowerCamelCase ): __magic_name__ : int =Fraction(lowerCamelCase ) result *= frac.denominator / frac.numerator return int(lowerCamelCase ) if __name__ == "__main__": print(solution())
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) _snake_case : Optional[int] = { 'configuration_mobilebert': [ 'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileBertConfig', 'MobileBertOnnxConfig', ], 'tokenization_mobilebert': ['MobileBertTokenizer'], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _snake_case : Dict = ['MobileBertTokenizerFast'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _snake_case : int = [ 'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST', 'MobileBertForMaskedLM', 'MobileBertForMultipleChoice', 'MobileBertForNextSentencePrediction', 'MobileBertForPreTraining', 'MobileBertForQuestionAnswering', 'MobileBertForSequenceClassification', 'MobileBertForTokenClassification', 'MobileBertLayer', 'MobileBertModel', 'MobileBertPreTrainedModel', 'load_tf_weights_in_mobilebert', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _snake_case : List[Any] = [ 'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFMobileBertForMaskedLM', 'TFMobileBertForMultipleChoice', 'TFMobileBertForNextSentencePrediction', 'TFMobileBertForPreTraining', 'TFMobileBertForQuestionAnswering', 'TFMobileBertForSequenceClassification', 'TFMobileBertForTokenClassification', 'TFMobileBertMainLayer', 'TFMobileBertModel', 'TFMobileBertPreTrainedModel', ] if TYPE_CHECKING: from .configuration_mobilebert import ( MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertOnnxConfig, ) from .tokenization_mobilebert import MobileBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_mobilebert_fast import MobileBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mobilebert import ( MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileBertForMaskedLM, MobileBertForMultipleChoice, MobileBertForNextSentencePrediction, MobileBertForPreTraining, MobileBertForQuestionAnswering, MobileBertForSequenceClassification, MobileBertForTokenClassification, MobileBertLayer, MobileBertModel, MobileBertPreTrainedModel, load_tf_weights_in_mobilebert, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_mobilebert import ( TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileBertForMaskedLM, TFMobileBertForMultipleChoice, TFMobileBertForNextSentencePrediction, TFMobileBertForPreTraining, TFMobileBertForQuestionAnswering, TFMobileBertForSequenceClassification, TFMobileBertForTokenClassification, TFMobileBertMainLayer, TFMobileBertModel, TFMobileBertPreTrainedModel, ) else: import sys _snake_case : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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from collections.abc import Callable from math import pi, sqrt from random import uniform from statistics import mean def lowerCAmelCase_ ( lowerCamelCase ): # A local function to see if a dot lands in the circle. def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool: __magic_name__ : Dict =sqrt((x**2) + (y**2) ) # Our circle has a radius of 1, so a distance # greater than 1 would land outside the circle. return distance_from_centre <= 1 # The proportion of guesses that landed in the circle __magic_name__ : Union[str, Any] =mean( int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) ) for _ in range(lowerCamelCase ) ) # The ratio of the area for circle to square is pi/4. __magic_name__ : List[Any] =proportion * 4 print(F"The estimated value of pi is {pi_estimate}" ) print(F"The numpy value of pi is {pi}" ) print(F"The total error is {abs(pi - pi_estimate )}" ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 , ): return mean( function_to_integrate(uniform(lowerCamelCase , lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ): def identity_function(lowerCamelCase ) -> float: return x __magic_name__ : Optional[int] =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) __magic_name__ : str =(max_value * max_value - min_value * min_value) / 2 print("""******************""" ) print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {expected_value}" ) print(F"Total error is {abs(estimated_value - expected_value )}" ) print("""******************""" ) def lowerCAmelCase_ ( lowerCamelCase ): def function_to_integrate(lowerCamelCase ) -> float: return sqrt(4.0 - x * x ) __magic_name__ : Dict =area_under_curve_estimator( lowerCamelCase , lowerCamelCase , 0.0 , 2.0 ) print("""******************""" ) print("""Estimating pi using area_under_curve_estimator""" ) print(F"Estimated value is {estimated_value}" ) print(F"Expected value is {pi}" ) print(F"Total error is {abs(estimated_value - pi )}" ) print("""******************""" ) if __name__ == "__main__": import doctest doctest.testmod()
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from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging snake_case__ : Tuple = logging.get_logger(__name__) snake_case__ : List[str] = { """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""", # See all YOLOS models at https://huggingface.co/models?filter=yolos } class _a ( UpperCAmelCase__ ): """simple docstring""" A_ = """yolos""" def __init__( self , _UpperCAmelCase=768 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.0 , _UpperCAmelCase=0.0_2 , _UpperCAmelCase=1e-12 , _UpperCAmelCase=[512, 864] , _UpperCAmelCase=16 , _UpperCAmelCase=3 , _UpperCAmelCase=True , _UpperCAmelCase=100 , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=1 , _UpperCAmelCase=5 , _UpperCAmelCase=2 , _UpperCAmelCase=5 , _UpperCAmelCase=2 , _UpperCAmelCase=0.1 , **_UpperCAmelCase , ) -> List[str]: super().__init__(**_UpperCAmelCase ) 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_ = initializer_range UpperCamelCase_ = layer_norm_eps UpperCamelCase_ = image_size UpperCamelCase_ = patch_size UpperCamelCase_ = num_channels UpperCamelCase_ = qkv_bias UpperCamelCase_ = num_detection_tokens UpperCamelCase_ = use_mid_position_embeddings UpperCamelCase_ = auxiliary_loss # Hungarian matcher UpperCamelCase_ = class_cost UpperCamelCase_ = bbox_cost UpperCamelCase_ = giou_cost # Loss coefficients UpperCamelCase_ = bbox_loss_coefficient UpperCamelCase_ = giou_loss_coefficient UpperCamelCase_ = eos_coefficient class _a ( UpperCAmelCase__ ): """simple docstring""" A_ = version.parse("""1.11""" ) @property def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}), ] ) @property def _UpperCAmelCase ( self ) -> float: return 1e-4 @property def _UpperCAmelCase ( self ) -> int: return 12
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import os from typing import Dict, List, Union import tensorflow as tf from keras_nlp.tokenizers import BytePairTokenizer from tensorflow_text import pad_model_inputs from .tokenization_gpta import GPTaTokenizer class __A ( tf.keras.layers.Layer ): def __init__( self :Optional[int] , __snake_case :Dict[str, int] , __snake_case :List[str] , __snake_case :int = None , __snake_case :int = None ): '''simple docstring''' super().__init__() __magic_name__ : Optional[int] =pad_token_id __magic_name__ : List[Any] =max_length __magic_name__ : Dict =vocab __magic_name__ : int =merges __magic_name__ : Optional[int] =BytePairTokenizer(__snake_case , __snake_case , sequence_length=__snake_case ) @classmethod def A__ ( cls :List[Any] , __snake_case :GPTaTokenizer , *__snake_case :int , **__snake_case :Any ): '''simple docstring''' __magic_name__ : List[Any] =[""" """.join(__snake_case ) for m in tokenizer.bpe_ranks.keys()] __magic_name__ : str =tokenizer.get_vocab() return cls(__snake_case , __snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Dict , __snake_case :Union[str, os.PathLike] , *__snake_case :Union[str, Any] , **__snake_case :int ): '''simple docstring''' __magic_name__ : Dict =GPTaTokenizer.from_pretrained(__snake_case , *__snake_case , **__snake_case ) return cls.from_tokenizer(__snake_case , *__snake_case , **__snake_case ) @classmethod def A__ ( cls :Optional[Any] , __snake_case :List[Any] ): '''simple docstring''' return cls(**__snake_case ) def A__ ( self :Union[str, Any] ): '''simple docstring''' return { "vocab": self.vocab, "merges": self.merges, "max_length": self.max_length, "pad_token_id": self.pad_token_id, } def A__ ( self :List[Any] , __snake_case :Dict , __snake_case :int = None ): '''simple docstring''' __magic_name__ : Optional[Any] =self.tf_tokenizer(__snake_case ) __magic_name__ : Tuple =tf.ones_like(__snake_case ) if self.pad_token_id is not None: # pad the tokens up to max length __magic_name__ : Tuple =max_length if max_length is not None else self.max_length if max_length is not None: __magic_name__ , __magic_name__ : Tuple =pad_model_inputs( __snake_case , max_seq_length=__snake_case , pad_value=self.pad_token_id ) return {"attention_mask": attention_mask, "input_ids": input_ids}
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'''simple docstring''' def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> int: '''simple docstring''' return number | (1 << position) def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> int: '''simple docstring''' return number & ~(1 << position) def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> int: '''simple docstring''' return number ^ (1 << position) def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> bool: '''simple docstring''' return ((number >> position) & 1) == 1 def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : int )-> int: '''simple docstring''' return int((number & (1 << position)) != 0 ) if __name__ == "__main__": import doctest doctest.testmod()
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import math import tensorflow as tf from packaging import version def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : List[str] =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : str =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Optional[Any] =tf.cast(math.pi , x.dtype ) __magic_name__ : int =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase , 3 )) )) return x * cdf def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Any =tf.convert_to_tensor(lowerCamelCase ) return x * tf.tanh(tf.math.softplus(lowerCamelCase ) ) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : Optional[Any] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Union[str, Any] =tf.cast(0.0_4_4_7_1_5 , x.dtype ) __magic_name__ : Tuple =tf.cast(0.7_9_7_8_8_4_5_6_0_8 , x.dtype ) return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) )) def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : List[str] =tf.convert_to_tensor(lowerCamelCase ) __magic_name__ : Dict =tf.cast(1.7_0_2 , x.dtype ) return x * tf.math.sigmoid(coeff * x ) def lowerCAmelCase_ ( lowerCamelCase ): return tf.clip_by_value(_gelu(lowerCamelCase ) , -10 , 10 ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=-1 ): __magic_name__ , __magic_name__ : List[Any] =tf.split(lowerCamelCase , 2 , axis=lowerCamelCase ) return a * tf.math.sigmoid(lowerCamelCase ) if version.parse(tf.version.VERSION) >= version.parse("2.4"): def lowerCAmelCase_ ( lowerCamelCase ): return tf.keras.activations.gelu(lowerCamelCase , approximate=lowerCamelCase ) UpperCAmelCase_ : List[str] = tf.keras.activations.gelu UpperCAmelCase_ : Dict = approximate_gelu_wrap else: UpperCAmelCase_ : Dict = _gelu UpperCAmelCase_ : str = _gelu_new UpperCAmelCase_ : Any = { "gelu": gelu, "gelu_10": gelu_aa, "gelu_fast": gelu_fast, "gelu_new": gelu_new, "glu": glu, "mish": mish, "quick_gelu": quick_gelu, "relu": tf.keras.activations.relu, "sigmoid": tf.keras.activations.sigmoid, "silu": tf.keras.activations.swish, "swish": tf.keras.activations.swish, "tanh": tf.keras.activations.tanh, } def lowerCAmelCase_ ( lowerCamelCase ): if activation_string in ACTaFN: return ACTaFN[activation_string] else: raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )
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from __future__ import annotations import math class _UpperCamelCase : '''simple docstring''' def __init__( self : Dict , a : int ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE : Dict = size # approximate the overall size of segment tree with given value SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # create array to store lazy update SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )] SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update def __UpperCamelCase ( self : Tuple , a : int ) -> int: """simple docstring""" return idx * 2 def __UpperCamelCase ( self : str , a : int ) -> int: """simple docstring""" return idx * 2 + 1 def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None: """simple docstring""" if left_element == right_element: SCREAMING_SNAKE_CASE : int = a[left_element - 1] else: SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2 self.build(self.left(a ) , a , a , a ) self.build(self.right(a ) , mid + 1 , a , a ) SCREAMING_SNAKE_CASE : List[Any] = max( self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] ) def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool: """simple docstring""" if self.flag[idx] is True: SCREAMING_SNAKE_CASE : Any = self.lazy[idx] SCREAMING_SNAKE_CASE : List[str] = False if left_element != right_element: SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx] SCREAMING_SNAKE_CASE : int = self.lazy[idx] SCREAMING_SNAKE_CASE : Any = True SCREAMING_SNAKE_CASE : List[Any] = True if right_element < a or left_element > b: return True if left_element >= a and right_element <= b: SCREAMING_SNAKE_CASE : Optional[Any] = val if left_element != right_element: SCREAMING_SNAKE_CASE : str = val SCREAMING_SNAKE_CASE : str = val SCREAMING_SNAKE_CASE : Tuple = True SCREAMING_SNAKE_CASE : Optional[Any] = True return True SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2 self.update(self.left(a ) , a , a , a , a , a ) self.update(self.right(a ) , mid + 1 , a , a , a , a ) SCREAMING_SNAKE_CASE : Optional[int] = max( self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] ) return True def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float: """simple docstring""" if self.flag[idx] is True: SCREAMING_SNAKE_CASE : int = self.lazy[idx] SCREAMING_SNAKE_CASE : List[Any] = False if left_element != right_element: SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx] SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx] SCREAMING_SNAKE_CASE : Optional[Any] = True SCREAMING_SNAKE_CASE : Union[str, Any] = True if right_element < a or left_element > b: return -math.inf if left_element >= a and right_element <= b: return self.segment_tree[idx] SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2 SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a ) SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a ) return max(a , a ) def __str__( self : str ) -> str: """simple docstring""" return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] ) if __name__ == "__main__": a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8] a_ = 15 a_ = SegmentTree(size) segt.build(1, 1, size, A) print(segt.query(1, 1, size, 4, 6)) print(segt.query(1, 1, size, 7, 11)) print(segt.query(1, 1, size, 7, 12)) segt.update(1, 1, size, 1, 3, 111) print(segt.query(1, 1, size, 1, 15)) segt.update(1, 1, size, 7, 8, 235) print(segt)
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from collections.abc import Sequence def lowerCAmelCase_ ( lowerCamelCase = None ): if nums is None or not nums: raise ValueError("""Input sequence should not be empty""" ) __magic_name__ : str =nums[0] for i in range(1 , len(lowerCamelCase ) ): __magic_name__ : Any =nums[i] __magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase ) return ans if __name__ == "__main__": import doctest doctest.testmod() # Try on a sample input from the user UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip()) UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n] print(max_subsequence_sum(array))
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'''simple docstring''' import numpy as np import qiskit def _a ( _lowerCamelCase = 8 , _lowerCamelCase = None ) -> str: """simple docstring""" __snake_case : Any = np.random.default_rng(seed=_lowerCamelCase ) # Roughly 25% of the qubits will contribute to the key. # So we take more than we need. __snake_case : Optional[int] = 6 * key_len # Measurement basis for Alice's qubits. __snake_case : str = rng.integers(2 , size=_lowerCamelCase ) # The set of states Alice will prepare. __snake_case : Any = rng.integers(2 , size=_lowerCamelCase ) # Measurement basis for Bob's qubits. __snake_case : Any = rng.integers(2 , size=_lowerCamelCase ) # Quantum Circuit to simulate BB84 __snake_case : Dict = qiskit.QuantumCircuit(_lowerCamelCase , name="""BB84""" ) # Alice prepares her qubits according to rules above. for index, _ in enumerate(_lowerCamelCase ): if alice_state[index] == 1: bbaa_circ.x(_lowerCamelCase ) if alice_basis[index] == 1: bbaa_circ.h(_lowerCamelCase ) bbaa_circ.barrier() # Bob measures the received qubits according to rules above. for index, _ in enumerate(_lowerCamelCase ): if bob_basis[index] == 1: bbaa_circ.h(_lowerCamelCase ) bbaa_circ.barrier() bbaa_circ.measure_all() # Simulate the quantum circuit. __snake_case : int = qiskit.Aer.get_backend("""aer_simulator""" ) # We only need to run one shot because the key is unique. # Multiple shots will produce the same key. __snake_case : Any = qiskit.execute(_lowerCamelCase , _lowerCamelCase , shots=1 , seed_simulator=_lowerCamelCase ) # Returns the result of measurement. __snake_case : str = job.result().get_counts(_lowerCamelCase ).most_frequent() # Extracting the generated key from the simulation results. # Only keep measurement results where Alice and Bob chose the same basis. __snake_case : Any = """""".join( [ result_bit for alice_basis_bit, bob_basis_bit, result_bit in zip( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) if alice_basis_bit == bob_basis_bit ] ) # Get final key. Pad with 0 if too short, otherwise truncate. __snake_case : Any = gen_key[:key_len] if len(_lowerCamelCase ) >= key_len else gen_key.ljust(_lowerCamelCase , """0""" ) return key if __name__ == "__main__": print(f"""The generated key is : {bbaa(8, seed=0)}""") from doctest import testmod testmod()
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from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union import pyarrow as pa if TYPE_CHECKING: from .features import FeatureType @dataclass class __A : UpperCamelCase = 42 UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""Translation""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def __call__( self :Union[str, Any] ): '''simple docstring''' return pa.struct({lang: pa.string() for lang in sorted(self.languages )} ) def A__ ( self :List[Any] ): '''simple docstring''' from .features import Value return {k: Value("""string""" ) for k in sorted(self.languages )} @dataclass class __A : UpperCamelCase = None UpperCamelCase = None UpperCamelCase = None # Automatically constructed UpperCamelCase = "dict" UpperCamelCase = None UpperCamelCase = field(default="""TranslationVariableLanguages""" , init=UpperCamelCase__ , repr=UpperCamelCase__ ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =sorted(set(self.languages ) ) if self.languages else None __magic_name__ : Optional[int] =len(self.languages ) if self.languages else None def __call__( self :List[str] ): '''simple docstring''' return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} ) def A__ ( self :str , __snake_case :str ): '''simple docstring''' __magic_name__ : Optional[int] =set(self.languages ) if self.languages and set(__snake_case ) - lang_set: raise ValueError( f"Some languages in example ({', '.join(sorted(set(__snake_case ) - lang_set ) )}) are not in valid set ({', '.join(__snake_case )})." ) # Convert dictionary into tuples, splitting out cases where there are # multiple translations for a single language. __magic_name__ : Any =[] for lang, text in translation_dict.items(): if isinstance(__snake_case , __snake_case ): translation_tuples.append((lang, text) ) else: translation_tuples.extend([(lang, el) for el in text] ) # Ensure translations are in ascending order by language code. __magic_name__ , __magic_name__ : List[str] =zip(*sorted(__snake_case ) ) return {"language": languages, "translation": translations} def A__ ( self :List[Any] ): '''simple docstring''' from .features import Sequence, Value return { "language": Sequence(Value("""string""" ) ), "translation": Sequence(Value("""string""" ) ), }
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import math def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> str: """simple docstring""" _A = 0 _A = 0 while num > 0: _A = num % 8 _A = octal + (remainder * math.floor(math.pow(10 , _SCREAMING_SNAKE_CASE ) )) counter += 1 _A = math.floor(num / 8 ) # basically /= 8 without remainder if any # This formatting removes trailing '.0' from `octal`. return F"0o{int(_SCREAMING_SNAKE_CASE )}" def __lowerCAmelCase( ) -> None: """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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from sklearn.metrics import matthews_corrcoef import datasets UpperCAmelCase_ : Dict = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n" UpperCAmelCase_ : Any = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n" UpperCAmelCase_ : Dict = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n" @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class __A ( datasets.Metric ): def A__ ( self :List[str] ): '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""int32""" ), """references""": datasets.Value("""int32""" ), } ) , reference_urls=[ """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html""" ] , ) def A__ ( self :Tuple , __snake_case :str , __snake_case :Tuple , __snake_case :List[str]=None ): '''simple docstring''' return { "matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ), }
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'''simple docstring''' UpperCamelCase_ = [ [0, 1_6, 1_3, 0, 0, 0], [0, 0, 1_0, 1_2, 0, 0], [0, 4, 0, 0, 1_4, 0], [0, 0, 9, 0, 0, 2_0], [0, 0, 0, 7, 0, 4], [0, 0, 0, 0, 0, 0], ] def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: Dict ,__UpperCamelCase: Optional[int] ,__UpperCamelCase: List[str] ): """simple docstring""" SCREAMING_SNAKE_CASE : Union[str, Any] = [False] * len(__UpperCamelCase ) SCREAMING_SNAKE_CASE : Optional[int] = [s] SCREAMING_SNAKE_CASE : Optional[Any] = True while queue: SCREAMING_SNAKE_CASE : Tuple = queue.pop(0 ) for ind in range(len(graph[u] ) ): if visited[ind] is False and graph[u][ind] > 0: queue.append(__UpperCamelCase ) SCREAMING_SNAKE_CASE : int = True SCREAMING_SNAKE_CASE : Any = u return visited[t] def lowercase__( __UpperCamelCase: Any ,__UpperCamelCase: Union[str, Any] ,__UpperCamelCase: List[Any] ): """simple docstring""" SCREAMING_SNAKE_CASE : Any = [-1] * (len(__UpperCamelCase )) SCREAMING_SNAKE_CASE : int = 0 SCREAMING_SNAKE_CASE : Dict = [] SCREAMING_SNAKE_CASE : List[Any] = [i[:] for i in graph] # Record original cut, copy. while bfs(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ): SCREAMING_SNAKE_CASE : str = float('Inf' ) SCREAMING_SNAKE_CASE : str = sink while s != source: # Find the minimum value in select path SCREAMING_SNAKE_CASE : List[str] = min(__UpperCamelCase ,graph[parent[s]][s] ) SCREAMING_SNAKE_CASE : Optional[Any] = parent[s] max_flow += path_flow SCREAMING_SNAKE_CASE : Union[str, Any] = sink while v != source: SCREAMING_SNAKE_CASE : Any = parent[v] graph[u][v] -= path_flow graph[v][u] += path_flow SCREAMING_SNAKE_CASE : Dict = parent[v] for i in range(len(__UpperCamelCase ) ): for j in range(len(graph[0] ) ): if graph[i][j] == 0 and temp[i][j] > 0: res.append((i, j) ) return res if __name__ == "__main__": print(mincut(test_graph, source=0, sink=5))
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import importlib import torch import yaml from omegaconf import OmegaConf from taming.models.vqgan import VQModel def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase ) if display: print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) ) return config def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None ): if conf_path is None: __magic_name__ : List[str] ="""./model_checkpoints/vqgan_only.yaml""" __magic_name__ : Dict =load_config(lowerCamelCase , display=lowerCamelCase ) __magic_name__ : Tuple =VQModel(**config.model.params ) if ckpt_path is None: __magic_name__ : Optional[Any] ="""./model_checkpoints/vqgan_only.pt""" __magic_name__ : Tuple =torch.load(lowerCamelCase , map_location=lowerCamelCase ) if ".ckpt" in ckpt_path: __magic_name__ : Any =sd["""state_dict"""] model.load_state_dict(lowerCamelCase , strict=lowerCamelCase ) model.to(lowerCamelCase ) del sd return model def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): __magic_name__ , __magic_name__ , __magic_name__ : Optional[Any] =model.encode(lowerCamelCase ) print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" ) __magic_name__ : List[Any] =model.decode(lowerCamelCase ) return xrec def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ , __magic_name__ : Optional[int] =string.rsplit(""".""" , 1 ) if reload: __magic_name__ : Optional[int] =importlib.import_module(lowerCamelCase ) importlib.reload(lowerCamelCase ) return getattr(importlib.import_module(lowerCamelCase , package=lowerCamelCase ) , cls ) def lowerCAmelCase_ ( lowerCamelCase ): if "target" not in config: raise KeyError("""Expected key `target` to instantiate.""" ) return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) ) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=True ): __magic_name__ : str =instantiate_from_config(lowerCamelCase ) if sd is not None: model.load_state_dict(lowerCamelCase ) if gpu: model.cuda() if eval_mode: model.eval() return {"model": model} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): # load the specified checkpoint if ckpt: __magic_name__ : str =torch.load(lowerCamelCase , map_location="""cpu""" ) __magic_name__ : Any =pl_sd["""global_step"""] print(F"loaded model from global step {global_step}." ) else: __magic_name__ : List[Any] ={"""state_dict""": None} __magic_name__ : Optional[Any] =None __magic_name__ : Tuple =load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=lowerCamelCase , eval_mode=lowerCamelCase )["""model"""] return model, global_step
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"""simple docstring""" import warnings from ...utils import logging from .image_processing_segformer import SegformerImageProcessor A_ = logging.get_logger(__name__) class __lowerCamelCase ( lowerCAmelCase ): def __init__( self , *UpperCAmelCase , **UpperCAmelCase ): warnings.warn( '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.''' ''' Please use SegformerImageProcessor instead.''' , UpperCAmelCase , ) super().__init__(*UpperCAmelCase , **UpperCAmelCase )
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class __A ( unittest.TestCase ): def A__ ( self :Tuple ): '''simple docstring''' debug_launcher(test_script.main ) def A__ ( self :Dict ): '''simple docstring''' debug_launcher(test_ops.main )
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__a = 8.314462 # Unit - J mol-1 K-1 def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ): '''simple docstring''' if moles < 0 or kelvin < 0 or volume < 0: raise ValueError('''Invalid inputs. Enter positive value.''' ) return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ): '''simple docstring''' if moles < 0 or kelvin < 0 or pressure < 0: raise ValueError('''Invalid inputs. Enter positive value.''' ) return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure if __name__ == "__main__": from doctest import testmod testmod()
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UpperCAmelCase_ : Tuple = 0 # The first color of the flag. UpperCAmelCase_ : Any = 1 # The second color of the flag. UpperCAmelCase_ : str = 2 # The third color of the flag. UpperCAmelCase_ : Tuple = (red, white, blue) def lowerCAmelCase_ ( lowerCamelCase ): if not sequence: return [] if len(lowerCamelCase ) == 1: return list(lowerCamelCase ) __magic_name__ : int =0 __magic_name__ : str =len(lowerCamelCase ) - 1 __magic_name__ : Optional[Any] =0 while mid <= high: if sequence[mid] == colors[0]: __magic_name__ , __magic_name__ : Tuple =sequence[mid], sequence[low] low += 1 mid += 1 elif sequence[mid] == colors[1]: mid += 1 elif sequence[mid] == colors[2]: __magic_name__ , __magic_name__ : Optional[Any] =sequence[high], sequence[mid] high -= 1 else: __magic_name__ : Optional[int] =F"The elements inside the sequence must contains only {colors} values" raise ValueError(lowerCamelCase ) return sequence if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : List[Any] = input("Enter numbers separated by commas:\n").strip() UpperCAmelCase_ : Optional[int] = [int(item.strip()) for item in user_input.split(",")] print(F"""{dutch_national_flag_sort(unsorted)}""")
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import operator as op def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> Any: SCREAMING_SNAKE_CASE_ = [] SCREAMING_SNAKE_CASE_ = lambda __UpperCAmelCase , __UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation SCREAMING_SNAKE_CASE_ = { '^': op.pow, '*': op.mul, '/': div, '+': op.add, '-': op.sub, } # operators & their respective operation # print table header print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' ) print('-' * (30 + len(__UpperCAmelCase )) ) for x in post_fix: if x.isdigit(): # if x in digit stack.append(__UpperCAmelCase ) # append x to stack # output in tabular format print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' ) else: SCREAMING_SNAKE_CASE_ = stack.pop() # pop stack # output in tabular format print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' ) SCREAMING_SNAKE_CASE_ = stack.pop() # pop stack # output in tabular format print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' ) stack.append( str(opr[x](int(__UpperCAmelCase ) , int(__UpperCAmelCase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack # output in tabular format print( x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' , ) return int(stack[0] ) if __name__ == "__main__": lowerCamelCase__ : Tuple = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ') print('\n\tResult = ', solve(Postfix))
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class __A ( UpperCamelCase__ , UpperCamelCase__ ): UpperCamelCase = 1 @register_to_config def __init__( self :Any , __snake_case :Tuple=20_00 , __snake_case :Optional[Any]=0.1 , __snake_case :Any=20 , __snake_case :Optional[int]=1E-3 ): '''simple docstring''' __magic_name__ : Dict =None __magic_name__ : List[str] =None __magic_name__ : str =None def A__ ( self :Dict , __snake_case :Optional[int] , __snake_case :Union[str, torch.device] = None ): '''simple docstring''' __magic_name__ : Union[str, Any] =torch.linspace(1 , self.config.sampling_eps , __snake_case , device=__snake_case ) def A__ ( self :List[str] , __snake_case :List[str] , __snake_case :int , __snake_case :int , __snake_case :List[str]=None ): '''simple docstring''' if self.timesteps is None: raise ValueError( """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score __magic_name__ : int =( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) __magic_name__ : Optional[int] =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) __magic_name__ : str =std.flatten() while len(std.shape ) < len(score.shape ): __magic_name__ : List[str] =std.unsqueeze(-1 ) __magic_name__ : Union[str, Any] =-score / std # compute __magic_name__ : Tuple =-1.0 / len(self.timesteps ) __magic_name__ : int =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) __magic_name__ : Dict =beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): __magic_name__ : Any =beta_t.unsqueeze(-1 ) __magic_name__ : Dict =-0.5 * beta_t * x __magic_name__ : Optional[int] =torch.sqrt(__snake_case ) __magic_name__ : int =drift - diffusion**2 * score __magic_name__ : List[str] =x + drift * dt # add noise __magic_name__ : Optional[int] =randn_tensor(x.shape , layout=x.layout , generator=__snake_case , device=x.device , dtype=x.dtype ) __magic_name__ : Optional[Any] =x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__( self :List[Any] ): '''simple docstring''' return self.config.num_train_timesteps
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from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json", # See all LeViT models at https://huggingface.co/models?filter=levit } class __UpperCamelCase ( A__ ): __A : Tuple = """levit""" def __init__( self , _UpperCamelCase=224 , _UpperCamelCase=3 , _UpperCamelCase=3 , _UpperCamelCase=2 , _UpperCamelCase=1 , _UpperCamelCase=16 , _UpperCamelCase=[128, 256, 384] , _UpperCamelCase=[4, 8, 12] , _UpperCamelCase=[4, 4, 4] , _UpperCamelCase=[16, 16, 16] , _UpperCamelCase=0 , _UpperCamelCase=[2, 2, 2] , _UpperCamelCase=[2, 2, 2] , _UpperCamelCase=0.02 , **_UpperCamelCase , ): super().__init__(**_UpperCamelCase ) _UpperCAmelCase = image_size _UpperCAmelCase = num_channels _UpperCAmelCase = kernel_size _UpperCAmelCase = stride _UpperCAmelCase = padding _UpperCAmelCase = hidden_sizes _UpperCAmelCase = num_attention_heads _UpperCAmelCase = depths _UpperCAmelCase = key_dim _UpperCAmelCase = drop_path_rate _UpperCAmelCase = patch_size _UpperCAmelCase = attention_ratio _UpperCAmelCase = mlp_ratio _UpperCAmelCase = initializer_range _UpperCAmelCase = [ ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2], ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2], ] class __UpperCamelCase ( A__ ): __A : List[Any] = version.parse("""1.11""" ) @property def UpperCamelCase( self ): return OrderedDict( [ ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}), ] ) @property def UpperCamelCase( self ): return 1e-4
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from typing import Dict, List, Optional, Union import numpy as np from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy UpperCAmelCase_ : Dict = logging.get_logger(__name__) class __A ( UpperCamelCase__ ): def __init__( self :List[str] , __snake_case :int , __snake_case :int , __snake_case :float , **__snake_case :Optional[Any] ): '''simple docstring''' __magic_name__ : List[Any] =feature_size __magic_name__ : Union[str, Any] =sampling_rate __magic_name__ : List[Any] =padding_value __magic_name__ : List[str] =kwargs.pop("""padding_side""" , """right""" ) __magic_name__ : Tuple =kwargs.pop("""return_attention_mask""" , __snake_case ) super().__init__(**__snake_case ) def A__ ( self :Any , __snake_case :Union[ BatchFeature, List[BatchFeature], Dict[str, BatchFeature], Dict[str, List[BatchFeature]], List[Dict[str, BatchFeature]], ] , __snake_case :Union[bool, str, PaddingStrategy] = True , __snake_case :Optional[int] = None , __snake_case :bool = False , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , __snake_case :Optional[Union[str, TensorType]] = None , ): '''simple docstring''' if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ): __magic_name__ : Union[str, Any] ={ key: [example[key] for example in processed_features] for key in processed_features[0].keys() } # The model's main input name, usually `input_values`, has be passed for padding if self.model_input_names[0] not in processed_features: raise ValueError( """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`""" f" to this method that includes {self.model_input_names[0]}, but you provided" f" {list(processed_features.keys() )}" ) __magic_name__ : int =processed_features[self.model_input_names[0]] __magic_name__ : Union[str, Any] =( return_attention_mask if return_attention_mask is not None else self.return_attention_mask ) if len(__snake_case ) == 0: if return_attention_mask: __magic_name__ : List[str] =[] return processed_features # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch __magic_name__ : Optional[int] =required_input[0] if isinstance(__snake_case , (list, tuple) ): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. __magic_name__ : Optional[Any] =0 while len(required_input[index] ) == 0: index += 1 if index < len(__snake_case ): __magic_name__ : List[str] =required_input[index][0] if return_tensors is None: if is_tf_tensor(__snake_case ): __magic_name__ : int ="""tf""" elif is_torch_tensor(__snake_case ): __magic_name__ : str ="""pt""" elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ): __magic_name__ : List[Any] ="""np""" else: raise ValueError( f"type of {first_element} unknown: {type(__snake_case )}. " """Should be one of a python, numpy, pytorch or tensorflow object.""" ) for key, value in processed_features.items(): if isinstance(value[0] , (int, float) ): __magic_name__ : List[str] =to_numpy(__snake_case ) else: __magic_name__ : str =[to_numpy(__snake_case ) for v in value] # Convert padding_strategy in PaddingStrategy __magic_name__ : Dict =self._get_padding_strategies(padding=__snake_case , max_length=__snake_case ) __magic_name__ : Optional[Any] =processed_features[self.model_input_names[0]] __magic_name__ : Dict =len(__snake_case ) if not all(len(__snake_case ) == batch_size for v in processed_features.values() ): raise ValueError("""Some items in the output dictionary have a different batch size than others.""" ) __magic_name__ : Optional[int] =[] for i in range(__snake_case ): __magic_name__ : Any ={k: v[i] for k, v in processed_features.items()} # truncation __magic_name__ : List[str] =self._truncate( __snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , ) truncated_inputs.append(__snake_case ) if padding_strategy == PaddingStrategy.LONGEST: # make sure that `max_length` cannot be longer than the longest truncated length __magic_name__ : Optional[int] =max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs ) __magic_name__ : Tuple =PaddingStrategy.MAX_LENGTH __magic_name__ : str ={} for i in range(__snake_case ): # padding __magic_name__ : List[str] =self._pad( truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , ) for key, value in outputs.items(): if key not in batch_outputs: __magic_name__ : Dict =[] if value.dtype is np.dtype(np.floataa ): __magic_name__ : Optional[int] =value.astype(np.floataa ) batch_outputs[key].append(__snake_case ) return BatchFeature(__snake_case , tensor_type=__snake_case ) def A__ ( self :Any , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' __magic_name__ : Dict =processed_features[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: __magic_name__ : Any =len(__snake_case ) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : Dict =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : List[Any] =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length if return_attention_mask and "attention_mask" not in processed_features: __magic_name__ : int =np.ones(len(__snake_case ) , dtype=np.intaa ) if needs_to_be_padded: __magic_name__ : List[Any] =max_length - len(__snake_case ) if self.padding_side == "right": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (0, difference) ) __magic_name__ : Tuple =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference) __magic_name__ : str =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) elif self.padding_side == "left": if return_attention_mask: __magic_name__ : str =np.pad( processed_features["""attention_mask"""] , (difference, 0) ) __magic_name__ : Optional[int] =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0) __magic_name__ : List[Any] =np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) else: raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) ) return processed_features def A__ ( self :Optional[Any] , __snake_case :Union[Dict[str, np.ndarray], BatchFeature] , __snake_case :Optional[int] = None , __snake_case :Optional[int] = None , __snake_case :Optional[bool] = None , ): '''simple docstring''' if not truncation: return processed_features elif truncation and max_length is None: raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" ) __magic_name__ : Union[str, Any] =processed_features[self.model_input_names[0]] # find `max_length` that fits `pad_to_multiple_of` if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): __magic_name__ : List[str] =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of __magic_name__ : Any =len(__snake_case ) > max_length if needs_to_be_truncated: __magic_name__ : List[Any] =processed_features[self.model_input_names[0]][:max_length] if "attention_mask" in processed_features: __magic_name__ : List[str] =processed_features["""attention_mask"""][:max_length] return processed_features def A__ ( self :List[Any] , __snake_case :str=False , __snake_case :Optional[int]=None ): '''simple docstring''' if padding is not False: if padding is True: __magic_name__ : Union[str, Any] =PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch elif not isinstance(__snake_case , __snake_case ): __magic_name__ : Optional[int] =PaddingStrategy(__snake_case ) elif isinstance(__snake_case , __snake_case ): __magic_name__ : Any =padding else: __magic_name__ : Any =PaddingStrategy.DO_NOT_PAD # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: raise ValueError( f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" ) # Test if we have a padding value if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None): raise ValueError( """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use""" """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" ) return padding_strategy
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import argparse import json from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import List import timm import torch import torch.nn as nn from huggingface_hub import hf_hub_download from torch import Tensor from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification from transformers.utils import logging logging.set_verbosity_info() lowerCamelCase__ : List[Any] = logging.get_logger() @dataclass class __magic_name__ : '''simple docstring''' __lowercase : nn.Module __lowercase : List[nn.Module] = field(default_factory=snake_case_ ) __lowercase : list = field(default_factory=snake_case_ ) def SCREAMING_SNAKE_CASE__ ( self:Dict , _a:str , _a:Tensor , _a:Tensor ): snake_case__ = len(list(m.modules() ) ) == 1 or isinstance(_a , nn.Convad ) or isinstance(_a , nn.BatchNormad ) if has_not_submodules: self.traced.append(_a ) def __call__( self:str , _a:Tensor ): for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook ) ) self.module(_a ) [x.remove() for x in self.handles] return self @property def SCREAMING_SNAKE_CASE__ ( self:Optional[int] ): # check the len of the state_dict keys to see if we have learnable params return list(filter(lambda _a : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) ) @dataclass class __magic_name__ : '''simple docstring''' __lowercase : nn.Module __lowercase : nn.Module __lowercase : int = 0 __lowercase : List = field(default_factory=snake_case_ ) __lowercase : List = field(default_factory=snake_case_ ) def __call__( self:Tuple , _a:Tensor ): snake_case__ = Tracker(self.dest )(_a ).parametrized snake_case__ = Tracker(self.src )(_a ).parametrized snake_case__ = list(filter(lambda _a : type(_a ) not in self.src_skip , _a ) ) snake_case__ = list(filter(lambda _a : type(_a ) not in self.dest_skip , _a ) ) if len(_a ) != len(_a ): raise Exception( F"""Numbers of operations are different. Source module has {len(_a )} operations while""" F""" destination module has {len(_a )}.""" ) for dest_m, src_m in zip(_a , _a ): dest_m.load_state_dict(src_m.state_dict() ) if self.verbose == 1: print(F"""Transfered from={src_m} to={dest_m}""" ) def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = True ) -> int: print(F"""Converting {name}...""" ) with torch.no_grad(): snake_case__ = timm.create_model(__lowerCAmelCase , pretrained=__lowerCAmelCase ).eval() snake_case__ = ResNetForImageClassification(__lowerCAmelCase ).eval() snake_case__ = ModuleTransfer(src=__lowerCAmelCase , dest=__lowerCAmelCase ) snake_case__ = torch.randn((1, 3, 224, 224) ) module_transfer(__lowerCAmelCase ) assert torch.allclose(from_model(__lowerCAmelCase ) , our_model(__lowerCAmelCase ).logits ), "The model logits don't match the original one." snake_case__ = F"""resnet{'-'.join(name.split('resnet' ) )}""" print(__lowerCAmelCase ) if push_to_hub: our_model.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add model''' , use_temp_dir=__lowerCAmelCase , ) # we can use the convnext one snake_case__ = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' ) image_processor.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add image processor''' , use_temp_dir=__lowerCAmelCase , ) print(F"""Pushed {checkpoint_name}""" ) def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = True ) -> List[Any]: snake_case__ = '''imagenet-1k-id2label.json''' snake_case__ = 1000 snake_case__ = (1, num_labels) snake_case__ = '''huggingface/label-files''' snake_case__ = num_labels snake_case__ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase , repo_type='''dataset''' ) , '''r''' ) ) snake_case__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()} snake_case__ = idalabel snake_case__ = {v: k for k, v in idalabel.items()} snake_case__ = partial(__lowerCAmelCase , num_labels=__lowerCAmelCase , idalabel=__lowerCAmelCase , labelaid=__lowerCAmelCase ) snake_case__ = { '''resnet18''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic''' ), '''resnet26''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ), '''resnet34''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic''' ), '''resnet50''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ), '''resnet101''': ImageNetPreTrainedConfig( depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ), '''resnet152''': ImageNetPreTrainedConfig( depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck''' ), } if model_name: convert_weight_and_push(__lowerCAmelCase , names_to_config[model_name] , __lowerCAmelCase , __lowerCAmelCase ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) return config, expected_shape if __name__ == "__main__": lowerCamelCase__ : List[str] = argparse.ArgumentParser() # Required parameters parser.add_argument( """--model_name""", default=None, type=str, help=( """The name of the model you wish to convert, it must be one of the supported resnet* architecture,""" """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.""" ), ) parser.add_argument( """--pytorch_dump_folder_path""", default=None, type=Path, required=True, help="""Path to the output PyTorch model directory.""", ) parser.add_argument( """--push_to_hub""", default=True, type=bool, required=False, help="""If True, push model and image processor to the hub.""", ) lowerCamelCase__ : Tuple = parser.parse_args() lowerCamelCase__ : Path = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
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import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class __A ( nn.Module ): def __init__( self :List[Any] ): '''simple docstring''' super().__init__() __magic_name__ : Tuple =nn.Linear(3 , 4 ) __magic_name__ : Union[str, Any] =nn.BatchNormad(4 ) __magic_name__ : List[str] =nn.Linear(4 , 5 ) def A__ ( self :Dict , __snake_case :Tuple ): '''simple docstring''' return self.lineara(self.batchnorm(self.lineara(__snake_case ) ) ) class __A ( UpperCamelCase__ ): def A__ ( self :Any , __snake_case :Optional[Any] , *__snake_case :List[Any] , **__snake_case :Any ): '''simple docstring''' return (args[0] + 1,) + args[1:], kwargs class __A ( UpperCamelCase__ ): def A__ ( self :List[str] , __snake_case :Tuple , __snake_case :Union[str, Any] ): '''simple docstring''' return output + 1 class __A ( unittest.TestCase ): def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : Tuple =ModelHook() add_hook_to_module(__snake_case , __snake_case ) self.assertEqual(test_model._hf_hook , __snake_case ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : int =ModelForTest() __magic_name__ : List[str] =ModelHook() add_hook_to_module(__snake_case , __snake_case ) add_hook_to_module(__snake_case , __snake_case , append=__snake_case ) self.assertEqual(isinstance(test_model._hf_hook , __snake_case ) , __snake_case ) self.assertEqual(len(test_model._hf_hook.hooks ) , 2 ) self.assertTrue(hasattr(__snake_case , """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["""x"""] ) remove_hook_from_module(__snake_case ) self.assertFalse(hasattr(__snake_case , """_hf_hook""" ) ) self.assertFalse(hasattr(__snake_case , """_old_forward""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() __magic_name__ : Any =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(x + 1 ) __magic_name__ : Optional[Any] =test_model(x + 2 ) __magic_name__ : int =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : int =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : str =PreForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : List[str] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Optional[Any] =SequentialHook(PreForwardHook() , PreForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) assert torch.allclose(__snake_case , __snake_case , atol=1E-5 ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() __magic_name__ : Dict =torch.randn(2 , 3 ) __magic_name__ : Any =test_model(__snake_case ) __magic_name__ : Dict =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Any =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Optional[int] =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __magic_name__ : Union[str, Any] =SequentialHook(PostForwardHook() , PostForwardHook() ) add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) assert torch.allclose(__snake_case , output + 2 , atol=1E-5 ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ : Tuple =ModelForTest() __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =test_model(__snake_case ) __magic_name__ : Union[str, Any] =PostForwardHook() add_hook_to_module(__snake_case , __snake_case ) __magic_name__ : Dict =test_model(__snake_case ) self.assertTrue(torch.allclose(__snake_case , output + 1 ) ) self.assertTrue(outputa.requires_grad ) __magic_name__ : Any =True __magic_name__ : Any =test_model(__snake_case ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Optional[Any] =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) ) self.assertEqual(model.lineara.weight.device , torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[Any] =model(__snake_case ) self.assertEqual(output.device , torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(__snake_case , AlignDevicesHook(io_same_device=__snake_case ) ) __magic_name__ : int =torch.randn(2 , 3 ).to(0 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , torch.device(0 ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : int =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : int ={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True} add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[int] =torch.device(hook_kwargs["""execution_device"""] ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : Union[str, Any] =torch.randn(2 , 3 ) __magic_name__ : Optional[int] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload __magic_name__ : Tuple ={ """execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True, """offload_buffers""": True, } add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**__snake_case ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**__snake_case ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Tuple =torch.randn(2 , 3 ) __magic_name__ : int =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[Any] ): '''simple docstring''' __magic_name__ : Any =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : str =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook(__snake_case , execution_device=__snake_case , offload=__snake_case , offload_buffers=__snake_case ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : Optional[int] =torch.randn(2 , 3 ) __magic_name__ : Union[str, Any] =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Dict =ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # This will move each submodule on different devices __magic_name__ : List[str] =0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device __magic_name__ : Optional[Any] =torch.device(__snake_case ) self.assertEqual(model.batchnorm.running_mean.device , __snake_case ) __magic_name__ : int =torch.randn(2 , 3 ) __magic_name__ : Any =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook( __snake_case , execution_device=__snake_case , offload=__snake_case , weights_map=model.state_dict() , offload_buffers=__snake_case , ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device("""meta""" ) ) __magic_name__ : List[Any] =torch.randn(2 , 3 ) __magic_name__ : str =model(__snake_case ) self.assertEqual(output.device , __snake_case ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(__snake_case ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device , torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device , torch.device("""cpu""" ) )
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"""simple docstring""" import json import os from collections import Counter import torch import torchvision import torchvision.transforms as transforms from PIL import Image from torch import nn from torch.utils.data import Dataset SCREAMING_SNAKE_CASE_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)} class snake_case_ ( nn.Module ): """simple docstring""" def __init__( self , lowerCamelCase_) -> Optional[int]: super().__init__() UpperCamelCase = torchvision.models.resnetaaa(pretrained=lowerCamelCase_) UpperCamelCase = list(model.children())[:-2] UpperCamelCase = nn.Sequential(*lowerCamelCase_) UpperCamelCase = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]) def UpperCAmelCase__ ( self , lowerCamelCase_) -> Tuple: # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048 UpperCamelCase = self.pool(self.model(lowerCamelCase_)) UpperCamelCase = torch.flatten(lowerCamelCase_ , start_dim=2) UpperCamelCase = out.transpose(1 , 2).contiguous() return out # BxNx2048 class snake_case_ ( lowerCamelCase_ ): """simple docstring""" def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_) -> int: UpperCamelCase = [json.loads(lowerCamelCase_) for l in open(lowerCamelCase_)] UpperCamelCase = os.path.dirname(lowerCamelCase_) UpperCamelCase = tokenizer UpperCamelCase = labels UpperCamelCase = len(lowerCamelCase_) UpperCamelCase = max_seq_length UpperCamelCase = transforms def __len__( self) -> int: return len(self.data) def __getitem__( self , lowerCamelCase_) -> int: UpperCamelCase = torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowerCamelCase_)) UpperCamelCase , UpperCamelCase , UpperCamelCase = sentence[0], sentence[1:-1], sentence[-1] UpperCamelCase = sentence[: self.max_seq_length] UpperCamelCase = torch.zeros(self.n_classes) UpperCamelCase = 1 UpperCamelCase = Image.open(os.path.join(self.data_dir , self.data[index]['''img'''])).convert('''RGB''') UpperCamelCase = self.transforms(lowerCamelCase_) return { "image_start_token": start_token, "image_end_token": end_token, "sentence": sentence, "image": image, "label": label, } def UpperCAmelCase__ ( self) -> List[str]: UpperCamelCase = Counter() for row in self.data: label_freqs.update(row['''label''']) return label_freqs def __snake_case ( _lowercase ): """simple docstring""" UpperCamelCase = [len(row['''sentence'''] ) for row in batch] UpperCamelCase , UpperCamelCase = len(_lowercase ), max(_lowercase ) UpperCamelCase = torch.zeros(_lowercase ,_lowercase ,dtype=torch.long ) UpperCamelCase = torch.zeros(_lowercase ,_lowercase ,dtype=torch.long ) for i_batch, (input_row, length) in enumerate(zip(_lowercase ,_lowercase ) ): UpperCamelCase = input_row['''sentence'''] UpperCamelCase = 1 UpperCamelCase = torch.stack([row['''image'''] for row in batch] ) UpperCamelCase = torch.stack([row['''label'''] for row in batch] ) UpperCamelCase = torch.stack([row['''image_start_token'''] for row in batch] ) UpperCamelCase = torch.stack([row['''image_end_token'''] for row in batch] ) return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor def __snake_case ( ): """simple docstring""" return [ "Crime", "Drama", "Thriller", "Action", "Comedy", "Romance", "Documentary", "Short", "Mystery", "History", "Family", "Adventure", "Fantasy", "Sci-Fi", "Western", "Horror", "Sport", "War", "Music", "Musical", "Animation", "Biography", "Film-Noir", ] def __snake_case ( ): """simple docstring""" return transforms.Compose( [ transforms.Resize(256 ), transforms.CenterCrop(224 ), transforms.ToTensor(), transforms.Normalize( mean=[0.46777044, 0.44531429, 0.40661017] ,std=[0.12221994, 0.12145835, 0.14380469] ,), ] )
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import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import XLMRobertaTokenizerFast from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class __A ( UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = KandinskyInpaintPipeline UpperCamelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""] UpperCamelCase = [ """prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image""", ] UpperCamelCase = [ """generator""", """height""", """width""", """latents""", """guidance_scale""", """negative_prompt""", """num_inference_steps""", """return_dict""", """guidance_scale""", """num_images_per_prompt""", """output_type""", """return_dict""", ] UpperCamelCase = False @property def A__ ( self :Union[str, Any] ): '''simple docstring''' return 32 @property def A__ ( self :Optional[Any] ): '''simple docstring''' return 32 @property def A__ ( self :List[Any] ): '''simple docstring''' return self.time_input_dim @property def A__ ( self :Dict ): '''simple docstring''' return self.time_input_dim * 4 @property def A__ ( self :List[Any] ): '''simple docstring''' return 1_00 @property def A__ ( self :Dict ): '''simple docstring''' __magic_name__ : Dict =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" ) return tokenizer @property def A__ ( self :str ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : str =MCLIPConfig( numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , ) __magic_name__ : Tuple =MultilingualCLIP(__snake_case ) __magic_name__ : Optional[int] =text_encoder.eval() return text_encoder @property def A__ ( self :Dict ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Optional[Any] ={ """in_channels""": 9, # Out channels is double in channels because predicts mean and variance """out_channels""": 8, """addition_embed_type""": """text_image""", """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""), """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""), """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""", """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2), """layers_per_block""": 1, """encoder_hid_dim""": self.text_embedder_hidden_size, """encoder_hid_dim_type""": """text_image_proj""", """cross_attention_dim""": self.cross_attention_dim, """attention_head_dim""": 4, """resnet_time_scale_shift""": """scale_shift""", """class_embed_type""": None, } __magic_name__ : Union[str, Any] =UNetaDConditionModel(**__snake_case ) return model @property def A__ ( self :List[str] ): '''simple docstring''' return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def A__ ( self :Tuple ): '''simple docstring''' torch.manual_seed(0 ) __magic_name__ : Dict =VQModel(**self.dummy_movq_kwargs ) return model def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : List[str] =self.dummy_text_encoder __magic_name__ : Optional[Any] =self.dummy_tokenizer __magic_name__ : Optional[Any] =self.dummy_unet __magic_name__ : Tuple =self.dummy_movq __magic_name__ : List[str] =DDIMScheduler( num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__snake_case , set_alpha_to_one=__snake_case , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__snake_case , ) __magic_name__ : str ={ """text_encoder""": text_encoder, """tokenizer""": tokenizer, """unet""": unet, """scheduler""": scheduler, """movq""": movq, } return components def A__ ( self :str , __snake_case :Optional[Any] , __snake_case :int=0 ): '''simple docstring''' __magic_name__ : Union[str, Any] =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : Dict =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case ) # create init_image __magic_name__ : str =floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case ) __magic_name__ : int =image.cpu().permute(0 , 2 , 3 , 1 )[0] __magic_name__ : str =Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) ) # create mask __magic_name__ : Dict =np.ones((64, 64) , dtype=np.floataa ) __magic_name__ : Any =0 if str(__snake_case ).startswith("""mps""" ): __magic_name__ : Dict =torch.manual_seed(__snake_case ) else: __magic_name__ : Tuple =torch.Generator(device=__snake_case ).manual_seed(__snake_case ) __magic_name__ : List[Any] ={ """prompt""": """horse""", """image""": init_image, """mask_image""": mask, """image_embeds""": image_embeds, """negative_image_embeds""": negative_image_embeds, """generator""": generator, """height""": 64, """width""": 64, """num_inference_steps""": 2, """guidance_scale""": 4.0, """output_type""": """np""", } return inputs def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Tuple ="""cpu""" __magic_name__ : List[Any] =self.get_dummy_components() __magic_name__ : Union[str, Any] =self.pipeline_class(**__snake_case ) __magic_name__ : Tuple =pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Tuple =pipe(**self.get_dummy_inputs(__snake_case ) ) __magic_name__ : List[Any] =output.images __magic_name__ : Any =pipe( **self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0] __magic_name__ : int =image[0, -3:, -3:, -1] __magic_name__ : str =image_from_tuple[0, -3:, -3:, -1] print(f"image.shape {image.shape}" ) assert image.shape == (1, 64, 64, 3) __magic_name__ : Optional[Any] =np.array( [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" def A__ ( self :Dict ): '''simple docstring''' super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) @slow @require_torch_gpu class __A ( unittest.TestCase ): def A__ ( self :List[Any] ): '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self :Union[str, Any] ): '''simple docstring''' __magic_name__ : List[str] =load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" ) __magic_name__ : int =load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" ) __magic_name__ : List[Any] =np.ones((7_68, 7_68) , dtype=np.floataa ) __magic_name__ : Any =0 __magic_name__ : int ="""a hat""" __magic_name__ : int =KandinskyPriorPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa ) pipe_prior.to(__snake_case ) __magic_name__ : Dict =KandinskyInpaintPipeline.from_pretrained( """kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa ) __magic_name__ : int =pipeline.to(__snake_case ) pipeline.set_progress_bar_config(disable=__snake_case ) __magic_name__ : Union[str, Any] =torch.Generator(device="""cpu""" ).manual_seed(0 ) __magic_name__ , __magic_name__ : Dict =pipe_prior( __snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple() __magic_name__ : Optional[Any] =pipeline( __snake_case , image=__snake_case , mask_image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type="""np""" , ) __magic_name__ : Optional[int] =output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(__snake_case , __snake_case )
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0
import json import os import unittest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class lowercase ( _UpperCAmelCase , unittest.TestCase ): lowerCamelCase : Union[str, Any] = MgpstrTokenizer lowerCamelCase : Tuple = False lowerCamelCase : str = {} lowerCamelCase : Dict = False def lowercase__ ( self : Dict ): super().setUp() # fmt: off SCREAMING_SNAKE_CASE__ : Optional[int] = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z'''] # fmt: on SCREAMING_SNAKE_CASE__ : str = dict(zip(_lowercase , range(len(_lowercase ) ) ) ) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write(json.dumps(_lowercase ) + '''\n''' ) def lowercase__ ( self : Any , **_lowercase : List[Any] ): return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase ) def lowercase__ ( self : int , _lowercase : List[str] ): SCREAMING_SNAKE_CASE__ : Tuple = '''tester''' SCREAMING_SNAKE_CASE__ : Union[str, Any] = '''tester''' return input_text, output_text @unittest.skip('''MGP-STR always lower cases letters.''' ) def lowercase__ ( self : Dict ): pass def lowercase__ ( self : str ): SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizers(do_lower_case=_lowercase ) for tokenizer in tokenizers: with self.subTest(f"""{tokenizer.__class__.__name__}""" ): SCREAMING_SNAKE_CASE__ : List[str] = '''[SPECIAL_TOKEN]''' tokenizer.add_special_tokens({'''cls_token''': special_token} ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.encode([special_token] , add_special_tokens=_lowercase ) self.assertEqual(len(_lowercase ) , 1 ) SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.decode(_lowercase , skip_special_tokens=_lowercase ) self.assertTrue(special_token not in decoded ) def lowercase__ ( self : Dict ): SCREAMING_SNAKE_CASE__ : List[Any] = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"""{tokenizer.__class__.__name__}""" ): SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_input_output_texts(_lowercase ) SCREAMING_SNAKE_CASE__ : Any = tokenizer.tokenize(_lowercase ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.convert_tokens_to_ids(_lowercase ) SCREAMING_SNAKE_CASE__ : str = tokenizer.encode(_lowercase , add_special_tokens=_lowercase ) self.assertListEqual(_lowercase , _lowercase ) SCREAMING_SNAKE_CASE__ : int = tokenizer.convert_ids_to_tokens(_lowercase ) self.assertNotEqual(len(_lowercase ) , 0 ) SCREAMING_SNAKE_CASE__ : str = tokenizer.decode(_lowercase ) self.assertIsInstance(_lowercase , _lowercase ) self.assertEqual(text_a.replace(''' ''' , '''''' ) , _lowercase ) @unittest.skip('''MGP-STR tokenizer only handles one sequence.''' ) def lowercase__ ( self : Optional[int] ): pass @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' ) def lowercase__ ( self : int ): pass
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import inspect import unittest import numpy as np from tests.test_modeling_common import floats_tensor from transformers import MaskaFormerConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel if is_vision_available(): from transformers import MaskaFormerImageProcessor if is_vision_available(): from PIL import Image class __A : def __init__( self :int , __snake_case :List[Any] , __snake_case :List[Any]=2 , __snake_case :Dict=True , __snake_case :Tuple=False , __snake_case :List[str]=10 , __snake_case :List[str]=3 , __snake_case :Union[str, Any]=32 * 8 , __snake_case :Optional[int]=32 * 8 , __snake_case :Any=4 , __snake_case :Union[str, Any]=64 , ): '''simple docstring''' __magic_name__ : Optional[int] =parent __magic_name__ : List[Any] =batch_size __magic_name__ : List[str] =is_training __magic_name__ : List[str] =use_auxiliary_loss __magic_name__ : Union[str, Any] =num_queries __magic_name__ : str =num_channels __magic_name__ : Union[str, Any] =min_size __magic_name__ : Union[str, Any] =max_size __magic_name__ : Optional[int] =num_labels __magic_name__ : Tuple =hidden_dim __magic_name__ : Any =hidden_dim def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to( __snake_case ) __magic_name__ : List[Any] =torch.ones([self.batch_size, self.min_size, self.max_size] , device=__snake_case ) __magic_name__ : List[str] =( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__snake_case ) > 0.5 ).float() __magic_name__ : Union[str, Any] =(torch.rand((self.batch_size, self.num_labels) , device=__snake_case ) > 0.5).long() __magic_name__ : str =self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Dict =MaskaFormerConfig( hidden_size=self.hidden_dim , ) __magic_name__ : str =self.num_queries __magic_name__ : Dict =self.num_labels __magic_name__ : int =[1, 1, 1, 1] __magic_name__ : List[str] =self.num_channels __magic_name__ : str =64 __magic_name__ : List[str] =1_28 __magic_name__ : Optional[Any] =self.hidden_dim __magic_name__ : Tuple =self.hidden_dim __magic_name__ : Optional[int] =self.hidden_dim return config def A__ ( self :Any ): '''simple docstring''' __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Tuple =self.prepare_config_and_inputs() __magic_name__ : Optional[Any] ={"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask} return config, inputs_dict def A__ ( self :Union[str, Any] , __snake_case :Tuple , __snake_case :Dict ): '''simple docstring''' __magic_name__ : int =output.encoder_hidden_states __magic_name__ : List[str] =output.pixel_decoder_hidden_states __magic_name__ : int =output.transformer_decoder_hidden_states self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(__snake_case ) , config.decoder_layers ) def A__ ( self :List[Any] , __snake_case :Optional[Any] , __snake_case :int , __snake_case :str , __snake_case :str=False ): '''simple docstring''' with torch.no_grad(): __magic_name__ : List[str] =MaskaFormerModel(config=__snake_case ) model.to(__snake_case ) model.eval() __magic_name__ : Union[str, Any] =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : int =model(__snake_case , output_hidden_states=__snake_case ) self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(output.encoder_last_hidden_state is not None ) if output_hidden_states: self.check_output_hidden_state(__snake_case , __snake_case ) def A__ ( self :Optional[Any] , __snake_case :List[str] , __snake_case :List[Any] , __snake_case :int , __snake_case :Any , __snake_case :Union[str, Any] ): '''simple docstring''' __magic_name__ : str =MaskaFormerForUniversalSegmentation(config=__snake_case ) model.to(__snake_case ) model.eval() def comm_check_on_output(__snake_case :List[str] ): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.encoder_last_hidden_state is not None ) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): __magic_name__ : int =model(pixel_values=__snake_case , pixel_mask=__snake_case ) __magic_name__ : List[str] =model(__snake_case ) comm_check_on_output(__snake_case ) __magic_name__ : Any =model( pixel_values=__snake_case , pixel_mask=__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) comm_check_on_output(__snake_case ) self.parent.assertTrue(result.loss is not None ) self.parent.assertEqual(result.loss.shape , torch.Size([1] ) ) @require_torch class __A ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ): UpperCamelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else () UpperCamelCase = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {} UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False def A__ ( self :str ): '''simple docstring''' __magic_name__ : Any =MaskaFormerModelTester(self ) __magic_name__ : Union[str, Any] =ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case ) def A__ ( self :Dict ): '''simple docstring''' self.config_tester.run_common_tests() def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case ) @unittest.skip(reason="""Mask2Former does not use inputs_embeds""" ) def A__ ( self :List[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" ) def A__ ( self :Dict ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former is not a generative model""" ) def A__ ( self :Optional[Any] ): '''simple docstring''' pass @unittest.skip(reason="""Mask2Former does not use token embeddings""" ) def A__ ( self :int ): '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip( reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" ) def A__ ( self :Tuple ): '''simple docstring''' pass @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" ) def A__ ( self :Union[str, Any] ): '''simple docstring''' pass def A__ ( self :Optional[int] ): '''simple docstring''' __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : Tuple =model_class(__snake_case ) __magic_name__ : Optional[Any] =inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic __magic_name__ : Tuple =[*signature.parameters.keys()] __magic_name__ : Optional[Any] =["""pixel_values"""] self.assertListEqual(arg_names[:1] , __snake_case ) @slow def A__ ( self :Tuple ): '''simple docstring''' for model_name in ["facebook/mask2former-swin-small-coco-instance"]: __magic_name__ : int =MaskaFormerModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ : Any =(self.model_tester.min_size,) * 2 __magic_name__ : Union[str, Any] ={ """pixel_values""": torch.randn((2, 3, *size) , device=__snake_case ), """mask_labels""": torch.randn((2, 10, *size) , device=__snake_case ), """class_labels""": torch.zeros(2 , 10 , device=__snake_case ).long(), } __magic_name__ : Optional[Any] =self.model_tester.get_config() __magic_name__ : Dict =MaskaFormerForUniversalSegmentation(__snake_case ).to(__snake_case ) __magic_name__ : Any =model(**__snake_case ) self.assertTrue(outputs.loss is not None ) def A__ ( self :List[str] ): '''simple docstring''' __magic_name__ , __magic_name__ : int =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskaformer_model(__snake_case , **__snake_case , output_hidden_states=__snake_case ) def A__ ( self :Tuple ): '''simple docstring''' __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __magic_name__ : List[Any] =model_class(__snake_case ).to(__snake_case ) __magic_name__ : Optional[int] =model(**__snake_case , output_attentions=__snake_case ) self.assertTrue(outputs.attentions is not None ) def A__ ( self :int ): '''simple docstring''' if not self.model_tester.is_training: return __magic_name__ : List[Any] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Dict =model_class(__snake_case ) model.to(__snake_case ) model.train() __magic_name__ : Optional[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ).loss loss.backward() def A__ ( self :int ): '''simple docstring''' __magic_name__ : List[str] =self.all_model_classes[1] __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] =self.model_tester.prepare_config_and_inputs() __magic_name__ : Tuple =True __magic_name__ : Optional[int] =True __magic_name__ : int =model_class(__snake_case ).to(__snake_case ) model.train() __magic_name__ : List[Any] =model(__snake_case , mask_labels=__snake_case , class_labels=__snake_case ) __magic_name__ : Optional[int] =outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() __magic_name__ : Union[str, Any] =outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() __magic_name__ : Optional[int] =outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=__snake_case ) self.assertIsNotNone(encoder_hidden_states.grad ) self.assertIsNotNone(pixel_decoder_hidden_states.grad ) self.assertIsNotNone(transformer_decoder_hidden_states.grad ) self.assertIsNotNone(attentions.grad ) UpperCAmelCase_ : Dict = 1e-4 def lowerCAmelCase_ ( ): __magic_name__ : Dict =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_vision @slow class __A ( unittest.TestCase ): @cached_property def A__ ( self :int ): '''simple docstring''' return "facebook/mask2former-swin-small-coco-instance" @cached_property def A__ ( self :int ): '''simple docstring''' return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Optional[Any] =MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__snake_case ) __magic_name__ : int =self.default_image_processor __magic_name__ : List[Any] =prepare_img() __magic_name__ : Any =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Dict =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : List[str] =model(**__snake_case ) __magic_name__ : Any =torch.tensor( [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.encoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Dict =torch.tensor( [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) __magic_name__ : Any =torch.tensor( [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(__snake_case ) self.assertTrue( torch.allclose( outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Any ): '''simple docstring''' __magic_name__ : Tuple =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Optional[int] =self.default_image_processor __magic_name__ : Tuple =prepare_img() __magic_name__ : List[Any] =image_processor(__snake_case , return_tensors="""pt""" ).to(__snake_case ) __magic_name__ : Union[str, Any] =inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(__snake_case , (1, 3, 3_84, 3_84) ) with torch.no_grad(): __magic_name__ : str =model(**__snake_case ) # masks_queries_logits __magic_name__ : List[Any] =outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ) __magic_name__ : List[Any] =[ [-8.7839, -9.0056, -8.8121], [-7.4104, -7.0313, -6.5401], [-6.6105, -6.3427, -6.4675], ] __magic_name__ : Dict =torch.tensor(__snake_case ).to(__snake_case ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __snake_case , atol=__snake_case ) ) # class_queries_logits __magic_name__ : Any =outputs.class_queries_logits self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) ) __magic_name__ : int =torch.tensor( [ [1.8324, -8.0835, -4.1922], [0.8450, -9.0050, -3.6053], [0.3045, -7.7293, -3.0275], ] ).to(__snake_case ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __snake_case , atol=__snake_case ) ) def A__ ( self :Optional[Any] ): '''simple docstring''' __magic_name__ : Union[str, Any] =MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__snake_case ).eval() __magic_name__ : Any =self.default_image_processor __magic_name__ : Union[str, Any] =image_processor( [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , ) __magic_name__ : str =inputs["""pixel_values"""].to(__snake_case ) __magic_name__ : Tuple =[el.to(__snake_case ) for el in inputs["""mask_labels"""]] __magic_name__ : Union[str, Any] =[el.to(__snake_case ) for el in inputs["""class_labels"""]] with torch.no_grad(): __magic_name__ : Dict =model(**__snake_case ) self.assertTrue(outputs.loss is not None )
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0
import os import sys __lowercase : Union[str, Any] = os.path.join(os.path.dirname(__file__), '''src''') sys.path.append(SRC_DIR) from transformers import ( AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForMaskedLM, AutoModelForQuestionAnswering, AutoModelForSequenceClassification, AutoTokenizer, add_start_docstrings, ) __lowercase : str = [ '''torch''', '''numpy''', '''tokenizers''', '''filelock''', '''requests''', '''tqdm''', '''regex''', '''sentencepiece''', '''sacremoses''', '''importlib_metadata''', '''huggingface_hub''', ] @add_start_docstrings(AutoConfig.__doc__ ) def lowercase ( *__A : Optional[int] , **__A : Union[str, Any] ) -> Dict: '''simple docstring''' return AutoConfig.from_pretrained(*__A , **__A ) @add_start_docstrings(AutoTokenizer.__doc__ ) def lowercase ( *__A : List[Any] , **__A : str ) -> List[Any]: '''simple docstring''' return AutoTokenizer.from_pretrained(*__A , **__A ) @add_start_docstrings(AutoModel.__doc__ ) def lowercase ( *__A : Tuple , **__A : Optional[Any] ) -> int: '''simple docstring''' return AutoModel.from_pretrained(*__A , **__A ) @add_start_docstrings(AutoModelForCausalLM.__doc__ ) def lowercase ( *__A : Any , **__A : Union[str, Any] ) -> Union[str, Any]: '''simple docstring''' return AutoModelForCausalLM.from_pretrained(*__A , **__A ) @add_start_docstrings(AutoModelForMaskedLM.__doc__ ) def lowercase ( *__A : Tuple , **__A : Dict ) -> Union[str, Any]: '''simple docstring''' return AutoModelForMaskedLM.from_pretrained(*__A , **__A ) @add_start_docstrings(AutoModelForSequenceClassification.__doc__ ) def lowercase ( *__A : Optional[Any] , **__A : Tuple ) -> Optional[int]: '''simple docstring''' return AutoModelForSequenceClassification.from_pretrained(*__A , **__A ) @add_start_docstrings(AutoModelForQuestionAnswering.__doc__ ) def lowercase ( *__A : Union[str, Any] , **__A : int ) -> int: '''simple docstring''' return AutoModelForQuestionAnswering.from_pretrained(*__A , **__A )
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import warnings from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : List[str] = { "nvidia/segformer-b0-finetuned-ade-512-512": ( "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json" ), # See all SegFormer models at https://huggingface.co/models?filter=segformer } class __A ( UpperCamelCase__ ): UpperCamelCase = """segformer""" def __init__( self :List[str] , __snake_case :str=3 , __snake_case :Optional[Any]=4 , __snake_case :List[Any]=[2, 2, 2, 2] , __snake_case :Dict=[8, 4, 2, 1] , __snake_case :Optional[int]=[32, 64, 1_60, 2_56] , __snake_case :Union[str, Any]=[7, 3, 3, 3] , __snake_case :Optional[Any]=[4, 2, 2, 2] , __snake_case :Tuple=[1, 2, 5, 8] , __snake_case :List[Any]=[4, 4, 4, 4] , __snake_case :Optional[Any]="gelu" , __snake_case :Tuple=0.0 , __snake_case :Dict=0.0 , __snake_case :Optional[int]=0.1 , __snake_case :Optional[int]=0.02 , __snake_case :Tuple=0.1 , __snake_case :Union[str, Any]=1E-6 , __snake_case :int=2_56 , __snake_case :Optional[int]=2_55 , **__snake_case :Dict , ): '''simple docstring''' super().__init__(**__snake_case ) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be""" """ removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , ) __magic_name__ : Dict =num_channels __magic_name__ : str =num_encoder_blocks __magic_name__ : List[Any] =depths __magic_name__ : Optional[Any] =sr_ratios __magic_name__ : List[str] =hidden_sizes __magic_name__ : List[str] =patch_sizes __magic_name__ : Any =strides __magic_name__ : Optional[Any] =mlp_ratios __magic_name__ : str =num_attention_heads __magic_name__ : int =hidden_act __magic_name__ : List[Any] =hidden_dropout_prob __magic_name__ : Optional[Any] =attention_probs_dropout_prob __magic_name__ : Optional[Any] =classifier_dropout_prob __magic_name__ : List[str] =initializer_range __magic_name__ : List[str] =drop_path_rate __magic_name__ : List[Any] =layer_norm_eps __magic_name__ : List[str] =decoder_hidden_size __magic_name__ : Union[str, Any] =kwargs.get("""reshape_last_stage""" , __snake_case ) __magic_name__ : Dict =semantic_loss_ignore_index class __A ( UpperCamelCase__ ): UpperCamelCase = version.parse("""1.11""" ) @property def A__ ( self :List[str] ): '''simple docstring''' return OrderedDict( [ ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}), ] ) @property def A__ ( self :Any ): '''simple docstring''' return 1E-4 @property def A__ ( self :int ): '''simple docstring''' return 12
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class A__ : """simple docstring""" def __init__( self : List[Any] , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] ): a__ : str = name a__ : Optional[int] = value a__ : Dict = weight def __repr__( self : Union[str, Any] ): return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})''' def _UpperCamelCase( self : Dict ): return self.value def _UpperCamelCase( self : Optional[Any] ): return self.name def _UpperCamelCase( self : Optional[Any] ): return self.weight def _UpperCamelCase( self : Optional[int] ): return self.value / self.weight def UpperCamelCase_ ( __a , __a , __a ) -> Optional[Any]: a__ : Optional[Any] = [] for i in range(len(__a ) ): menu.append(Things(name[i] , value[i] , weight[i] ) ) return menu def UpperCamelCase_ ( __a , __a , __a ) -> Union[str, Any]: a__ : List[str] = sorted(__a , key=__a , reverse=__a ) a__ : List[Any] = [] a__, a__ : Union[str, Any] = 0.0, 0.0 for i in range(len(__a ) ): if (total_cost + items_copy[i].get_weight()) <= max_cost: result.append(items_copy[i] ) total_cost += items_copy[i].get_weight() total_value += items_copy[i].get_value() return (result, total_value) def UpperCamelCase_ ( ) -> Union[str, Any]: pass if __name__ == "__main__": import doctest doctest.testmod()
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import heapq def lowerCAmelCase_ ( lowerCamelCase ): __magic_name__ : list[list] =[] # 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(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] ) # chosen_vertices = set of chosen vertices __magic_name__ : Tuple =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 __magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0] chosen_vertices.add(lowerCamelCase ) # 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]: __magic_name__ : Tuple =elem[1][1].index(lowerCamelCase ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(lowerCamelCase ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() UpperCAmelCase_ : Optional[int] = {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 import os import re A_ : Optional[int] = "src/diffusers" # Pattern that looks at the indentation in a line. A_ : Optional[int] = re.compile(R"^(\s*)\S") # Pattern that matches `"key":" and puts `key` in group 0. A_ : str = re.compile(R"^\s*\"([^\"]+)\":") # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. A_ : Optional[int] = re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]") # Pattern that matches `"key",` and puts `key` in group 0. A_ : str = re.compile(R"^\s*\"([^\"]+)\",\s*$") # Pattern that matches any `[stuff]` and puts `stuff` in group 0. A_ : List[Any] = re.compile(R"\[([^\]]+)\]") def UpperCamelCase__ ( __magic_name__ : str ) -> List[str]: '''simple docstring''' snake_case__ : Dict = _re_indent.search(__magic_name__ ) return "" if search is None else search.groups()[0] def UpperCamelCase__ ( __magic_name__ : Optional[int] , __magic_name__ : List[Any]="" , __magic_name__ : Optional[Any]=None , __magic_name__ : List[str]=None ) -> List[str]: '''simple docstring''' snake_case__ : List[str] = 0 snake_case__ : List[Any] = code.split("""\n""" ) if start_prompt is not None: while not lines[index].startswith(__magic_name__ ): index += 1 snake_case__ : Dict = ["""\n""".join(lines[:index] )] else: snake_case__ : Tuple = [] # We split into blocks until we get to the `end_prompt` (or the end of the block). snake_case__ : Tuple = [lines[index]] index += 1 while index < len(__magic_name__ ) and (end_prompt is None or not lines[index].startswith(__magic_name__ )): if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level: if len(__magic_name__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ): current_block.append(lines[index] ) blocks.append("""\n""".join(__magic_name__ ) ) if index < len(__magic_name__ ) - 1: snake_case__ : List[str] = [lines[index + 1]] index += 1 else: snake_case__ : Union[str, Any] = [] else: blocks.append("""\n""".join(__magic_name__ ) ) snake_case__ : List[Any] = [lines[index]] else: current_block.append(lines[index] ) index += 1 # Adds current block if it's nonempty. if len(__magic_name__ ) > 0: blocks.append("""\n""".join(__magic_name__ ) ) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(__magic_name__ ): blocks.append("""\n""".join(lines[index:] ) ) return blocks def UpperCamelCase__ ( __magic_name__ : int ) -> int: '''simple docstring''' def _inner(__magic_name__ : List[Any] ): return key(__magic_name__ ).lower().replace("""_""" , """""" ) return _inner def UpperCamelCase__ ( __magic_name__ : Dict , __magic_name__ : Optional[int]=None ) -> Optional[Any]: '''simple docstring''' def noop(__magic_name__ : Union[str, Any] ): return x if key is None: snake_case__ : Optional[int] = noop # Constants are all uppercase, they go first. snake_case__ : Tuple = [obj for obj in objects if key(__magic_name__ ).isupper()] # Classes are not all uppercase but start with a capital, they go second. snake_case__ : Any = [obj for obj in objects if key(__magic_name__ )[0].isupper() and not key(__magic_name__ ).isupper()] # Functions begin with a lowercase, they go last. snake_case__ : str = [obj for obj in objects if not key(__magic_name__ )[0].isupper()] snake_case__ : Dict = ignore_underscore(__magic_name__ ) return sorted(__magic_name__ , key=__magic_name__ ) + sorted(__magic_name__ , key=__magic_name__ ) + sorted(__magic_name__ , key=__magic_name__ ) def UpperCamelCase__ ( __magic_name__ : str ) -> str: '''simple docstring''' def _replace(__magic_name__ : Tuple ): snake_case__ : Union[str, Any] = match.groups()[0] if "," not in imports: return f"[{imports}]" snake_case__ : Any = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: snake_case__ : Optional[int] = keys[:-1] return "[" + ", ".join([f"\"{k}\"" for k in sort_objects(__magic_name__ )] ) + "]" snake_case__ : Any = import_statement.split("""\n""" ) if len(__magic_name__ ) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. snake_case__ : List[str] = 2 if lines[1].strip() == """[""" else 1 snake_case__ : Union[str, Any] = [(i, _re_strip_line.search(__magic_name__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )] snake_case__ : List[Any] = sort_objects(__magic_name__ , key=lambda __magic_name__ : x[1] ) snake_case__ : Any = [lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] ) elif len(__magic_name__ ) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1] ) is not None: snake_case__ : Dict = _re_bracket_content.sub(_replace , lines[1] ) else: snake_case__ : int = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: snake_case__ : Any = keys[:-1] snake_case__ : Tuple = get_indent(lines[1] ) + """, """.join([f"\"{k}\"" for k in sort_objects(__magic_name__ )] ) return "\n".join(__magic_name__ ) else: # Finally we have to deal with imports fitting on one line snake_case__ : List[Any] = _re_bracket_content.sub(_replace , __magic_name__ ) return import_statement def UpperCamelCase__ ( __magic_name__ : List[str] , __magic_name__ : Optional[int]=True ) -> Optional[int]: '''simple docstring''' with open(__magic_name__ , """r""" ) as f: snake_case__ : List[str] = f.read() if "_import_structure" not in code: return # Blocks of indent level 0 snake_case__ : Any = split_code_in_indented_blocks( __magic_name__ , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" ) # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt). for block_idx in range(1 , len(__magic_name__ ) - 1 ): # Check if the block contains some `_import_structure`s thingy to sort. snake_case__ : int = main_blocks[block_idx] snake_case__ : Optional[Any] = block.split("""\n""" ) # Get to the start of the imports. snake_case__ : Union[str, Any] = 0 while line_idx < len(__magic_name__ ) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: snake_case__ : Dict = len(__magic_name__ ) else: line_idx += 1 if line_idx >= len(__magic_name__ ): continue # Ignore beginning and last line: they don't contain anything. snake_case__ : Any = """\n""".join(block_lines[line_idx:-1] ) snake_case__ : Optional[int] = get_indent(block_lines[1] ) # Slit the internal block into blocks of indent level 1. snake_case__ : int = split_code_in_indented_blocks(__magic_name__ , indent_level=__magic_name__ ) # We have two categories of import key: list or _import_structure[key].append/extend snake_case__ : Any = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or just comments. snake_case__ : Any = [(pattern.search(__magic_name__ ).groups()[0] if pattern.search(__magic_name__ ) is not None else None) for b in internal_blocks] # We only sort the lines with a key. snake_case__ : Optional[int] = [(i, key) for i, key in enumerate(__magic_name__ ) if key is not None] snake_case__ : Any = [x[0] for x in sorted(__magic_name__ , key=lambda __magic_name__ : x[1] )] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. snake_case__ : Dict = 0 snake_case__ : List[str] = [] for i in range(len(__magic_name__ ) ): if keys[i] is None: reordered_blocks.append(internal_blocks[i] ) else: snake_case__ : Union[str, Any] = sort_objects_in_import(internal_blocks[sorted_indices[count]] ) reordered_blocks.append(__magic_name__ ) count += 1 # And we put our main block back together with its first and last line. snake_case__ : List[Any] = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] ) if code != "\n".join(__magic_name__ ): if check_only: return True else: print(f"Overwriting {file}." ) with open(__magic_name__ , """w""" ) as f: f.write("""\n""".join(__magic_name__ ) ) def UpperCamelCase__ ( __magic_name__ : int=True ) -> Optional[Any]: '''simple docstring''' snake_case__ : Dict = [] for root, _, files in os.walk(__magic_name__ ): if "__init__.py" in files: snake_case__ : List[Any] = sort_imports(os.path.join(__magic_name__ , """__init__.py""" ) , check_only=__magic_name__ ) if result: snake_case__ : Optional[Any] = [os.path.join(__magic_name__ , """__init__.py""" )] if len(__magic_name__ ) > 0: raise ValueError(f"Would overwrite {len(__magic_name__ )} files, run `make style`." ) if __name__ == "__main__": A_ : Tuple = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") A_ : Union[str, Any] = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)
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UpperCAmelCase_ : int = range(2, 20 + 1) UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)] UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {} def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): __magic_name__ : Union[str, Any] =sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) ) __magic_name__ : Any =sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) ) __magic_name__ , __magic_name__ : Tuple =0, 0 __magic_name__ : Optional[Any] =n - i __magic_name__ : Union[str, Any] =memo.get(lowerCamelCase ) if sub_memo is not None: __magic_name__ : int =sub_memo.get(lowerCamelCase ) if jumps is not None and len(lowerCamelCase ) > 0: # find and make the largest jump without going over __magic_name__ : Dict =-1 for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ): if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: __magic_name__ : Optional[Any] =_k break if max_jump >= 0: __magic_name__ , __magic_name__ , __magic_name__ : Optional[int] =jumps[max_jump] # since the difference between jumps is cached, add c __magic_name__ : Tuple =diff + c for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ): __magic_name__ , __magic_name__ : Tuple =divmod(lowerCamelCase , 10 ) if new_c > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) else: __magic_name__ : str =[] else: __magic_name__ : List[str] ={c: []} __magic_name__ : List[str] =sub_memo if dn >= max_dn or c + diff >= base[k]: return diff, dn if k > ks[0]: while True: # keep doing smaller jumps __magic_name__ , __magic_name__ : Union[str, Any] =next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped if dn >= max_dn or c + diff >= base[k]: break else: # would be too small a jump, just compute sequential terms instead __magic_name__ , __magic_name__ : Optional[int] =compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase ) diff += _diff dn += terms_jumped __magic_name__ : Tuple =sub_memo[c] # keep jumps sorted by # of terms skipped __magic_name__ : List[Any] =0 while j < len(lowerCamelCase ): if jumps[j][1] > dn: break j += 1 # cache the jump for this value digitsum(b) and c sub_memo[c].insert(lowerCamelCase , (diff, dn, k) ) return (diff, dn) def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ): if i >= n: return 0, i if k > len(lowerCamelCase ): a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] ) # note: a_i -> b * 10^k + c # ds_b -> digitsum(b) # ds_c -> digitsum(c) __magic_name__ : Tuple =i __magic_name__ , __magic_name__ , __magic_name__ : Tuple =0, 0, 0 for j in range(len(lowerCamelCase ) ): if j >= k: ds_b += a_i[j] else: ds_c += a_i[j] while i < n: i += 1 __magic_name__ : Optional[Any] =ds_c + ds_b diff += addend __magic_name__ : str =0 for j in range(lowerCamelCase ): __magic_name__ : int =a_i[j] + addend __magic_name__ , __magic_name__ : Any =divmod(lowerCamelCase , 10 ) ds_c += a_i[j] if addend > 0: break if addend > 0: add(lowerCamelCase , lowerCamelCase , lowerCamelCase ) return diff, i - start_i def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ): for j in range(lowerCamelCase , len(lowerCamelCase ) ): __magic_name__ : Tuple =digits[j] + addend if s >= 10: __magic_name__ , __magic_name__ : int =divmod(lowerCamelCase , 10 ) __magic_name__ : int =addend // 10 + quotient else: __magic_name__ : Dict =s __magic_name__ : Any =addend // 10 if addend == 0: break while addend > 0: __magic_name__ , __magic_name__ : Union[str, Any] =divmod(lowerCamelCase , 10 ) digits.append(lowerCamelCase ) def lowerCAmelCase_ ( lowerCamelCase = 10**15 ): __magic_name__ : List[str] =[1] __magic_name__ : str =1 __magic_name__ : str =0 while True: __magic_name__ , __magic_name__ : List[str] =next_term(lowerCamelCase , 20 , i + dn , lowerCamelCase ) dn += terms_jumped if dn == n - i: break __magic_name__ : int =0 for j in range(len(lowerCamelCase ) ): a_n += digits[j] * 10**j return a_n if __name__ == "__main__": print(F"""{solution() = }""")
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available lowerCAmelCase_ = { '''configuration_squeezebert''': [ '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SqueezeBertConfig''', '''SqueezeBertOnnxConfig''', ], '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCAmelCase_ = ['''SqueezeBertTokenizerFast'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCAmelCase_ = [ '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''SqueezeBertForMaskedLM''', '''SqueezeBertForMultipleChoice''', '''SqueezeBertForQuestionAnswering''', '''SqueezeBertForSequenceClassification''', '''SqueezeBertForTokenClassification''', '''SqueezeBertModel''', '''SqueezeBertModule''', '''SqueezeBertPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_squeezebert import ( SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertOnnxConfig, ) from .tokenization_squeezebert import SqueezeBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_squeezebert import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, SqueezeBertModule, SqueezeBertPreTrainedModel, ) else: import sys lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from typing import List from .keymap import KEYMAP, get_character def lowerCAmelCase_ ( lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : str =getattr(lowerCamelCase , """handle_key""" , [] ) handle += [key] setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator def lowerCAmelCase_ ( *lowerCamelCase ): def decorator(lowerCamelCase ): __magic_name__ : Dict =getattr(lowerCamelCase , """handle_key""" , [] ) handle += keys setattr(lowerCamelCase , """handle_key""" , lowerCamelCase ) return func return decorator class __A ( UpperCamelCase__ ): def __new__( cls :Dict , __snake_case :Optional[Any] , __snake_case :Union[str, Any] , __snake_case :List[str] ): '''simple docstring''' __magic_name__ : int =super().__new__(cls , __snake_case , __snake_case , __snake_case ) if not hasattr(__snake_case , """key_handler""" ): setattr(__snake_case , """key_handler""" , {} ) setattr(__snake_case , """handle_input""" , KeyHandler.handle_input ) for value in attrs.values(): __magic_name__ : int =getattr(__snake_case , """handle_key""" , [] ) for key in handled_keys: __magic_name__ : List[str] =value return new_cls @staticmethod def A__ ( cls :Optional[int] ): '''simple docstring''' __magic_name__ : Union[str, Any] =get_character() if char != KEYMAP["undefined"]: __magic_name__ : Optional[int] =ord(__snake_case ) __magic_name__ : int =cls.key_handler.get(__snake_case ) if handler: __magic_name__ : Dict =char return handler(cls ) else: return None def lowerCAmelCase_ ( cls ): return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
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import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging __UpperCAmelCase = logging.get_logger(__name__) __UpperCAmelCase = { '''microsoft/unispeech-large-1500h-cv''': ( '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json''' ), # See all UniSpeech models at https://huggingface.co/models?filter=unispeech } class lowerCAmelCase_ ( a__ ): UpperCAmelCase__ : List[str] = "unispeech" def __init__( self, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=768, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=12, SCREAMING_SNAKE_CASE_=3072, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=1e-5, SCREAMING_SNAKE_CASE_="group", SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=(512, 512, 512, 512, 512, 512, 512), SCREAMING_SNAKE_CASE_=(5, 2, 2, 2, 2, 2, 2), SCREAMING_SNAKE_CASE_=(10, 3, 3, 3, 3, 2, 2), SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=128, SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=0.05, SCREAMING_SNAKE_CASE_=10, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=10, SCREAMING_SNAKE_CASE_=0, SCREAMING_SNAKE_CASE_=320, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=100, SCREAMING_SNAKE_CASE_=256, SCREAMING_SNAKE_CASE_=256, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_="mean", SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=256, SCREAMING_SNAKE_CASE_=80, SCREAMING_SNAKE_CASE_=0, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.5, **SCREAMING_SNAKE_CASE_, ) -> List[str]: super().__init__(**SCREAMING_SNAKE_CASE_, pad_token_id=SCREAMING_SNAKE_CASE_, bos_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_ ) UpperCamelCase : int = hidden_size UpperCamelCase : Dict = feat_extract_norm UpperCamelCase : Any = feat_extract_activation UpperCamelCase : str = list(SCREAMING_SNAKE_CASE_ ) UpperCamelCase : Any = list(SCREAMING_SNAKE_CASE_ ) UpperCamelCase : int = list(SCREAMING_SNAKE_CASE_ ) UpperCamelCase : Tuple = conv_bias UpperCamelCase : Union[str, Any] = num_conv_pos_embeddings UpperCamelCase : Union[str, Any] = num_conv_pos_embedding_groups UpperCamelCase : int = len(self.conv_dim ) UpperCamelCase : str = num_hidden_layers UpperCamelCase : List[Any] = intermediate_size UpperCamelCase : List[str] = hidden_act UpperCamelCase : str = num_attention_heads UpperCamelCase : Dict = hidden_dropout UpperCamelCase : Any = attention_dropout UpperCamelCase : List[Any] = activation_dropout UpperCamelCase : Any = feat_proj_dropout UpperCamelCase : Tuple = final_dropout UpperCamelCase : Optional[Any] = layerdrop UpperCamelCase : Tuple = layer_norm_eps UpperCamelCase : Tuple = initializer_range UpperCamelCase : Optional[Any] = num_ctc_classes UpperCamelCase : Any = vocab_size UpperCamelCase : int = do_stable_layer_norm UpperCamelCase : int = use_weighted_layer_sum UpperCamelCase : Tuple = 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 : str = apply_spec_augment UpperCamelCase : Optional[int] = mask_time_prob UpperCamelCase : Any = mask_time_length UpperCamelCase : Optional[Any] = mask_time_min_masks UpperCamelCase : int = mask_feature_prob UpperCamelCase : Union[str, Any] = mask_feature_length UpperCamelCase : Optional[Any] = mask_feature_min_masks # parameters for pretraining with codevector quantized representations UpperCamelCase : int = num_codevectors_per_group UpperCamelCase : str = num_codevector_groups UpperCamelCase : Optional[Any] = contrastive_logits_temperature UpperCamelCase : Optional[Any] = feat_quantizer_dropout UpperCamelCase : Union[str, Any] = num_negatives UpperCamelCase : Dict = codevector_dim UpperCamelCase : Tuple = proj_codevector_dim UpperCamelCase : int = diversity_loss_weight # ctc loss UpperCamelCase : int = ctc_loss_reduction UpperCamelCase : str = ctc_zero_infinity # pretraining loss UpperCamelCase : Dict = replace_prob @property def snake_case_ ( self ) -> List[str]: return functools.reduce(operator.mul, self.conv_stride, 1 )
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import os import jsonlines import numpy as np from tqdm import tqdm UpperCAmelCase_ : Dict = 2048 UpperCAmelCase_ : int = 4096 UpperCAmelCase_ : Any = 42 UpperCAmelCase_ : Optional[int] = os.environ.pop("PROCESS_TRAIN", "false") UpperCAmelCase_ : str = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4} def lowerCAmelCase_ ( lowerCamelCase ): def choose_first(lowerCamelCase , lowerCamelCase=False ): assert isinstance(lowerCamelCase , lowerCamelCase ) if len(lowerCamelCase ) == 1: __magic_name__ : List[str] =answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: __magic_name__ : Tuple ={k: [a[k]] for k in a} if len(a["""start_token"""] ) > 0: break return a __magic_name__ : str ={"""id""": example["""id"""]} __magic_name__ : List[Any] =example["""annotations"""] __magic_name__ : List[str] =annotation["""yes_no_answer"""] if 0 in yes_no_answer or 1 in yes_no_answer: __magic_name__ : Optional[int] =["""yes"""] if 1 in yes_no_answer else ["""no"""] __magic_name__ : List[str] =[] __magic_name__ : Dict =[] __magic_name__ : str =["""<cls>"""] else: __magic_name__ : Tuple =["""short"""] __magic_name__ : Optional[int] =choose_first(annotation["""short_answers"""] ) if len(out["""start_token"""] ) == 0: # answer will be long if short is not available __magic_name__ : Tuple =["""long"""] __magic_name__ : Tuple =choose_first(annotation["""long_answer"""] , is_long_answer=lowerCamelCase ) __magic_name__ : List[Any] =[] answer.update(lowerCamelCase ) # disregard some samples if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]: __magic_name__ : Any =True else: __magic_name__ : List[str] =False __magic_name__ : int =["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""] if not all(isinstance(answer[k] , lowerCamelCase ) for k in cols ): raise ValueError("""Issue in ID""" , example["""id"""] ) return answer def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ): __magic_name__ : Optional[int] =_get_single_answer(lowerCamelCase ) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : Any =example["""document"""]["""tokens"""] __magic_name__ : str =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": -100, # ignore index in cross-entropy "end_token": -100, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples __magic_name__ : Dict =["""start_token""", """end_token"""] answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10 __magic_name__ : Tuple =example["""document"""]["""tokens"""] __magic_name__ : Optional[int] =answer["""start_token"""] __magic_name__ : List[Any] =answer["""end_token"""] __magic_name__ : Optional[Any] =[] for i in range(len(doc["""token"""] ) ): if not doc["is_html"][i]: context.append(doc["""token"""][i] ) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 __magic_name__ : Optional[int] =""" """.join(context[start_token:end_token] ) # checking above code if assertion: __magic_name__ : List[str] =doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : str =doc["""token"""][answer["""start_token"""] : answer["""end_token"""]] __magic_name__ : Dict =""" """.join([old[i] for i in range(len(lowerCamelCase ) ) if not is_html[i]] ) if new != old: print("""ID:""" , example["""id"""] ) print("""New:""" , lowerCamelCase , end="""\n""" ) print("""Old:""" , lowerCamelCase , end="""\n\n""" ) return { "context": " ".join(lowerCamelCase ), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=True ): # overlap will be of doc_stride - q_len __magic_name__ : Any =get_context_and_ans(lowerCamelCase , assertion=lowerCamelCase ) __magic_name__ : Union[str, Any] =out["""answer"""] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } __magic_name__ : List[Any] =tokenizer(example["""question"""]["""text"""] , out["""context"""] ).input_ids __magic_name__ : Dict =input_ids.index(tokenizer.sep_token_id ) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element __magic_name__ : List[str] =[] __magic_name__ : int =[] __magic_name__ : List[str] =input_ids[:q_len] __magic_name__ : Dict =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Tuple =input_ids[i:end_index] inputs.append(q_indices + slice ) category.append(answer["""category"""][0] ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-100] * len(lowerCamelCase ), "end_token": [-100] * len(lowerCamelCase ), "category": category, }, } __magic_name__ : int =out["""context"""].split() __magic_name__ : Any =splitted_context[answer["""end_token"""]] __magic_name__ : str =len( tokenizer( """ """.join(splitted_context[: answer["""start_token"""]] ) , add_special_tokens=lowerCamelCase , ).input_ids ) __magic_name__ : Optional[int] =len( tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) , add_special_tokens=lowerCamelCase ).input_ids ) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token __magic_name__ : Union[str, Any] =len(tokenizer(lowerCamelCase , add_special_tokens=lowerCamelCase ).input_ids ) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 __magic_name__ : str =input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive __magic_name__ : Dict =answer["""start_token"""] __magic_name__ : int =answer["""end_token"""] if assertion: __magic_name__ : Any =tokenizer.decode(lowerCamelCase ) if answer["span"] != new: print("""ISSUE IN TOKENIZATION""" ) print("""OLD:""" , answer["""span"""] ) print("""NEW:""" , lowerCamelCase , end="""\n\n""" ) if len(lowerCamelCase ) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } __magic_name__ : Any =input_ids[:q_len] __magic_name__ : Union[str, Any] =range(lowerCamelCase , len(lowerCamelCase ) , max_length - doc_stride ) __magic_name__ : Any =[] __magic_name__ : List[str] =[] __magic_name__ : List[str] =[] __magic_name__ : str =[] # null, yes, no, long, short for i in doc_start_indices: __magic_name__ : List[Any] =i + max_length - q_len __magic_name__ : Dict =input_ids[i:end_index] inputs.append(q_indices + slice ) assert len(inputs[-1] ) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: __magic_name__ : List[Any] =start_token - i + q_len __magic_name__ : Optional[Any] =end_token - i + q_len answers_category.append(answer["""category"""][0] ) # ["short"] -> "short" else: __magic_name__ : Optional[Any] =-100 __magic_name__ : Optional[Any] =-100 answers_category.append("""null""" ) __magic_name__ : Optional[int] =inputs[-1][start_token : end_token + 1] answers_start_token.append(lowerCamelCase ) answers_end_token.append(lowerCamelCase ) if assertion: if new != old and new != [tokenizer.cls_token_id]: print("""ISSUE in strided for ID:""" , example["""id"""] ) print("""New:""" , tokenizer.decode(lowerCamelCase ) ) print("""Old:""" , tokenizer.decode(lowerCamelCase ) , end="""\n\n""" ) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=2048 , lowerCamelCase=4096 , lowerCamelCase=False ): __magic_name__ : List[Any] =get_strided_contexts_and_ans( lowerCamelCase , lowerCamelCase , doc_stride=lowerCamelCase , max_length=lowerCamelCase , assertion=lowerCamelCase , ) return example def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ): with jsonlines.open(lowerCamelCase , """a""" ) as writer: for example in tqdm(lowerCamelCase , total=len(lowerCamelCase ) , desc="""Saving samples ... """ ): __magic_name__ : int =example["""labels"""] for ids, start, end, cat in zip( example["""input_ids"""] , labels["""start_token"""] , labels["""end_token"""] , labels["""category"""] , ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { """input_ids""": ids, """start_token""": start, """end_token""": end, """category""": CATEGORY_MAPPING[cat], } ) if __name__ == "__main__": from datasets import load_dataset from transformers import BigBirdTokenizer UpperCAmelCase_ : Optional[int] = load_dataset("natural_questions") UpperCAmelCase_ : Optional[int] = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") UpperCAmelCase_ : str = data["train" if PROCESS_TRAIN == "true" else "validation"] UpperCAmelCase_ : Optional[int] = { "tokenizer": tokenizer, "doc_stride": DOC_STRIDE, "max_length": MAX_LENGTH, "assertion": False, } UpperCAmelCase_ : int = data.map(prepare_inputs, fn_kwargs=fn_kwargs) UpperCAmelCase_ : Optional[Any] = data.remove_columns(["annotations", "document", "id", "question"]) print(data) np.random.seed(SEED) UpperCAmelCase_ : int = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl" save_to_disk(data, file_name=cache_file_name)
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