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from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Optional[int] = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
snake_case__ : Dict = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
snake_case__ : List[Any] = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
snake_case__ : Optional[Any] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
snake_case__ : List[str] = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
snake_case__ : Dict = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
snake_case__ : Dict = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
snake_case__ : Any = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
snake_case__ : Optional[int] = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
snake_case__ : List[Any] = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
snake_case__ : Union[str, Any] = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
snake_case__ : Union[str, Any] = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
snake_case__ : Optional[Any] = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
snake_case__ : Union[str, Any] = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
snake_case__ : Any = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
snake_case__ : Union[str, Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
snake_case__ : Any = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
snake_case__ : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
snake_case__ : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
snake_case__ : Union[str, Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
snake_case__ : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
snake_case__ : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
snake_case__ : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
snake_case__ : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
snake_case__ : List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
snake_case__ : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
snake_case__ : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
snake_case__ : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : Any = FLAX_MODEL_MAPPING
snake_case__ : Dict = auto_class_update(FlaxAutoModel)
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : List[str] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
snake_case__ : Optional[int] = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : List[str] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
snake_case__ : List[Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : Any = FLAX_MODEL_FOR_MASKED_LM_MAPPING
snake_case__ : int = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : Optional[int] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
snake_case__ : List[Any] = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : List[str] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
snake_case__ : Any = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : Tuple = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
snake_case__ : int = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : List[str] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
snake_case__ : Tuple = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
snake_case__ : Optional[Any] = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : Any = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
snake_case__ : Any = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : List[Any] = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
snake_case__ : Any = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : List[Any] = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
snake_case__ : int = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class _A ( _BaseAutoModelClass ):
'''simple docstring'''
_snake_case : Tuple = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
snake_case__ : str = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 655 |
# 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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """openai/whisper-base"""
_snake_case : Union[str, Any] = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
_snake_case : Any = """transcriber"""
_snake_case : Any = WhisperProcessor
_snake_case : Optional[int] = WhisperForConditionalGeneration
_snake_case : str = ["""audio"""]
_snake_case : Optional[int] = ["""text"""]
def _snake_case ( self : List[str] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.pre_processor(lowerCamelCase , return_tensors="pt" ).input_features
def _snake_case ( self : str , lowerCamelCase : List[Any] ):
'''simple docstring'''
return self.model.generate(inputs=lowerCamelCase )
def _snake_case ( self : List[str] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(lowerCamelCase , skip_special_tokens=lowerCamelCase )[0]
| 655 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : List[Any] = {
"""Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Tuple = """marian"""
_snake_case : int = ["""past_key_values"""]
_snake_case : Any = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : str , lowerCamelCase : Dict=58_101 , lowerCamelCase : Any=None , lowerCamelCase : Optional[Any]=1_024 , lowerCamelCase : Tuple=12 , lowerCamelCase : Dict=4_096 , lowerCamelCase : Optional[int]=16 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Optional[int]=4_096 , lowerCamelCase : Any=16 , lowerCamelCase : Any=0.0 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : List[Any]=True , lowerCamelCase : Optional[Any]=True , lowerCamelCase : Union[str, Any]="gelu" , lowerCamelCase : Any=1_024 , lowerCamelCase : Dict=0.1 , lowerCamelCase : List[Any]=0.0 , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : Dict=0.02 , lowerCamelCase : Any=58_100 , lowerCamelCase : Optional[int]=False , lowerCamelCase : Union[str, Any]=58_100 , lowerCamelCase : Dict=0 , lowerCamelCase : Union[str, Any]=0 , lowerCamelCase : Union[str, Any]=True , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = vocab_size
__lowercase = decoder_vocab_size or vocab_size
__lowercase = max_position_embeddings
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = use_cache
__lowercase = encoder_layers
__lowercase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowercase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , is_encoder_decoder=lowerCamelCase , decoder_start_token_id=lowerCamelCase , forced_eos_token_id=lowerCamelCase , **lowerCamelCase , )
class _A ( _lowercase ):
'''simple docstring'''
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowercase = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
__lowercase = {0: "batch"}
__lowercase = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
__lowercase = {0: "batch", 1: "decoder_sequence"}
__lowercase = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
__lowercase = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
__lowercase , __lowercase = self.num_layers
for i in range(lowerCamelCase ):
__lowercase = {0: "batch", 2: "past_sequence + sequence"}
__lowercase = {0: "batch", 2: "past_sequence + sequence"}
else:
__lowercase = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowercase = super().outputs
else:
__lowercase = super(lowerCamelCase , self ).outputs
if self.use_past:
__lowercase , __lowercase = self.num_layers
for i in range(lowerCamelCase ):
__lowercase = {0: "batch", 2: "past_sequence + sequence"}
__lowercase = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def _snake_case ( self : int , lowerCamelCase : PreTrainedTokenizer , lowerCamelCase : int = -1 , lowerCamelCase : int = -1 , lowerCamelCase : bool = False , lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
__lowercase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Generate decoder inputs
__lowercase = seq_length if not self.use_past else 1
__lowercase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
__lowercase = dict(**lowerCamelCase , **lowerCamelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
__lowercase , __lowercase = common_inputs["input_ids"].shape
__lowercase = common_inputs["decoder_input_ids"].shape[1]
__lowercase , __lowercase = self.num_attention_heads
__lowercase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowercase = decoder_seq_length + 3
__lowercase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__lowercase = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(lowerCamelCase , lowerCamelCase )] , dim=1 )
__lowercase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__lowercase , __lowercase = self.num_layers
__lowercase = min(lowerCamelCase , lowerCamelCase )
__lowercase = max(lowerCamelCase , lowerCamelCase ) - min_num_layers
__lowercase = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(lowerCamelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowerCamelCase ),
torch.zeros(lowerCamelCase ),
torch.zeros(lowerCamelCase ),
torch.zeros(lowerCamelCase ),
) )
# TODO: test this.
__lowercase = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(lowerCamelCase , lowerCamelCase ):
common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase ), torch.zeros(lowerCamelCase )) )
return common_inputs
def _snake_case ( self : Optional[Any] , lowerCamelCase : PreTrainedTokenizer , lowerCamelCase : int = -1 , lowerCamelCase : int = -1 , lowerCamelCase : bool = False , lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
__lowercase = self._generate_dummy_inputs_for_encoder_and_decoder(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
__lowercase , __lowercase = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
__lowercase = seqlen + 2
__lowercase , __lowercase = self.num_layers
__lowercase , __lowercase = self.num_attention_heads
__lowercase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__lowercase = common_inputs["attention_mask"].dtype
__lowercase = torch.cat(
[common_inputs["attention_mask"], torch.ones(lowerCamelCase , lowerCamelCase , dtype=lowerCamelCase )] , dim=1 )
__lowercase = [
(torch.zeros(lowerCamelCase ), torch.zeros(lowerCamelCase )) for _ in range(lowerCamelCase )
]
return common_inputs
def _snake_case ( self : Optional[Any] , lowerCamelCase : PreTrainedTokenizer , lowerCamelCase : int = -1 , lowerCamelCase : int = -1 , lowerCamelCase : bool = False , lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
__lowercase = compute_effective_axis_dimension(
lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
__lowercase = tokenizer.num_special_tokens_to_add(lowerCamelCase )
__lowercase = compute_effective_axis_dimension(
lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCamelCase )
# Generate dummy inputs according to compute batch and sequence
__lowercase = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
__lowercase = dict(tokenizer(lowerCamelCase , return_tensors=lowerCamelCase ) )
return common_inputs
def _snake_case ( self : List[str] , lowerCamelCase : PreTrainedTokenizer , lowerCamelCase : int = -1 , lowerCamelCase : int = -1 , lowerCamelCase : bool = False , lowerCamelCase : Optional[TensorType] = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowercase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowerCamelCase , batch_size=lowerCamelCase , seq_length=lowerCamelCase , is_pair=lowerCamelCase , framework=lowerCamelCase )
else:
__lowercase = self._generate_dummy_inputs_for_causal_lm(
lowerCamelCase , batch_size=lowerCamelCase , seq_length=lowerCamelCase , is_pair=lowerCamelCase , framework=lowerCamelCase )
return common_inputs
def _snake_case ( self : str , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : List[str] , lowerCamelCase : Tuple ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__lowercase = super()._flatten_past_key_values_(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
__lowercase = super(lowerCamelCase , self )._flatten_past_key_values_(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
@property
def _snake_case ( self : Dict ):
'''simple docstring'''
return 1e-4
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _A :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["prompt"]
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
if "image" in inputs:
__lowercase = inputs["image"]
else:
__lowercase = None
if "mask_image" in inputs:
__lowercase = inputs["mask_image"]
else:
__lowercase = None
if "original_image" in inputs:
__lowercase = inputs["original_image"]
else:
__lowercase = None
__lowercase , __lowercase = pipe.encode_prompt(lowerCamelCase )
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase , lowerCamelCase ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
| 655 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
snake_case__ : Dict = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Tuple = ["""input_features""", """attention_mask"""]
def __init__( self : Any , lowerCamelCase : List[str]=80 , lowerCamelCase : Optional[int]=16_000 , lowerCamelCase : List[str]=80 , lowerCamelCase : Dict=0.0 , lowerCamelCase : int=True , lowerCamelCase : List[Any]=True , lowerCamelCase : Dict=True , **lowerCamelCase : List[str] , ):
'''simple docstring'''
super().__init__(feature_size=lowerCamelCase , sampling_rate=lowerCamelCase , padding_value=lowerCamelCase , **lowerCamelCase )
__lowercase = num_mel_bins
__lowercase = do_ceptral_normalize
__lowercase = normalize_means
__lowercase = normalize_vars
__lowercase = True
def _snake_case ( self : Any , lowerCamelCase : np.ndarray , ):
'''simple docstring'''
__lowercase = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
__lowercase = torch.from_numpy(lowerCamelCase ).unsqueeze(0 )
__lowercase = ta_kaldi.fbank(lowerCamelCase , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _snake_case ( lowerCamelCase : np.ndarray , lowerCamelCase : int , lowerCamelCase : Optional[bool] = True , lowerCamelCase : Optional[bool] = True , lowerCamelCase : float = 0.0 , ):
'''simple docstring'''
if normalize_means:
__lowercase = x[:input_length].mean(axis=0 )
__lowercase = np.subtract(lowerCamelCase , lowerCamelCase )
if normalize_vars:
__lowercase = x[:input_length].std(axis=0 )
__lowercase = np.divide(lowerCamelCase , lowerCamelCase )
if input_length < x.shape[0]:
__lowercase = padding_value
# make sure array is in float32
__lowercase = x.astype(np.floataa )
return x
def _snake_case ( self : Union[str, Any] , lowerCamelCase : List[np.ndarray] , lowerCamelCase : Optional[np.ndarray] = None ):
'''simple docstring'''
__lowercase = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(lowerCamelCase , lowerCamelCase , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(lowerCamelCase , lowerCamelCase )
]
def __call__( self : Dict , lowerCamelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , lowerCamelCase : Union[bool, str, PaddingStrategy] = False , lowerCamelCase : Optional[int] = None , lowerCamelCase : bool = False , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[Union[str, TensorType]] = None , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[bool] = None , **lowerCamelCase : Dict , ):
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
f""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
__lowercase = isinstance(lowerCamelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
__lowercase = is_batched_numpy or (
isinstance(lowerCamelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__lowercase = [np.asarray(lowerCamelCase , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCamelCase , np.ndarray ):
__lowercase = np.asarray(lowerCamelCase , dtype=np.floataa )
elif isinstance(lowerCamelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__lowercase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__lowercase = [raw_speech]
# extract fbank features
__lowercase = [self._extract_fbank_features(lowerCamelCase ) for waveform in raw_speech]
# convert into correct format for padding
__lowercase = BatchFeature({"input_features": features} )
__lowercase = self.pad(
lowerCamelCase , padding=lowerCamelCase , max_length=lowerCamelCase , truncation=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , **lowerCamelCase , )
# make sure list is in array format
__lowercase = padded_inputs.get("input_features" )
if isinstance(input_features[0] , lowerCamelCase ):
__lowercase = [np.asarray(lowerCamelCase , dtype=np.floataa ) for feature in input_features]
__lowercase = padded_inputs.get("attention_mask" )
if attention_mask is not None:
__lowercase = [np.asarray(lowerCamelCase , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
__lowercase = (
np.array(lowerCamelCase , dtype=np.intaa )
if self._get_padding_strategies(lowerCamelCase , max_length=lowerCamelCase ) is not PaddingStrategy.DO_NOT_PAD
else None
)
__lowercase = self.normalize(
padded_inputs["input_features"] , attention_mask=lowerCamelCase )
if return_tensors is not None:
__lowercase = padded_inputs.convert_to_tensors(lowerCamelCase )
return padded_inputs
| 655 |
import numpy as np
snake_case__ : Tuple = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class _A :
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase , __lowercase = np.where(letter == self.SQUARE )
__lowercase = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def _snake_case ( self : List[Any] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = self.SQUARE[indexa - 1, indexa - 1]
return letter
def _snake_case ( self : int , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
__lowercase = message.replace(" " , "" )
__lowercase = message.replace("j" , "i" )
__lowercase = np.empty((2, len(lowerCamelCase )) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape(2 * len(lowerCamelCase ) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[numbers_index * 2] )
__lowercase = int(second_step[(numbers_index * 2) + 1] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = encoded_message + letter
return encoded_message
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
message.replace(" " , "" )
__lowercase = np.empty(2 * len(lowerCamelCase ) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape((2, len(lowerCamelCase )) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[0, numbers_index] )
__lowercase = int(second_step[1, numbers_index] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = decoded_message + letter
return decoded_message
| 655 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = [
"decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = emb.weight.shape
__lowercase = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , bias=_SCREAMING_SNAKE_CASE )
__lowercase = emb.weight.data
return lin_layer
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = torch.load(_SCREAMING_SNAKE_CASE , map_location="cpu" )
__lowercase = Namespace(**checkpoint["cfg"]["model"] )
__lowercase = checkpoint["model"]
remove_ignore_keys_(_SCREAMING_SNAKE_CASE )
__lowercase = state_dict["decoder.embed_tokens.weight"].shape[0]
__lowercase = {key.replace("decoder" , "model" ): val for key, val in state_dict.items()}
__lowercase = XGLMConfig(
vocab_size=_SCREAMING_SNAKE_CASE , max_position_embeddings=args.max_target_positions , num_layers=args.decoder_layers , attention_heads=args.decoder_attention_heads , ffn_dim=args.decoder_ffn_embed_dim , d_model=args.decoder_embed_dim , layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="gelu" , scale_embedding=not args.no_scale_embedding , tie_word_embeddings=args.share_decoder_input_output_embed , )
__lowercase = XGLMForCausalLM(_SCREAMING_SNAKE_CASE )
__lowercase = model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
print(_SCREAMING_SNAKE_CASE )
__lowercase = make_linear_from_emb(model.model.embed_tokens )
return model
if __name__ == "__main__":
snake_case__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
snake_case__ : int = parser.parse_args()
snake_case__ : str = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
model.save_pretrained(args.pytorch_dump_folder_path)
| 655 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
'''simple docstring'''
_snake_case : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def snake_case_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 655 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
snake_case__ : Optional[int] = logging.getLogger(__name__)
@dataclass(frozen=_lowercase )
class _A :
'''simple docstring'''
_snake_case : str
_snake_case : str
_snake_case : Optional[str] = None
_snake_case : Optional[str] = None
_snake_case : Optional[str] = None
@dataclass(frozen=_lowercase )
class _A :
'''simple docstring'''
_snake_case : List[int]
_snake_case : Optional[List[int]] = None
_snake_case : Optional[List[int]] = None
_snake_case : Optional[Union[int, float]] = None
_snake_case : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[InputFeatures]
def __init__( self : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : PreTrainedTokenizer , lowerCamelCase : str , lowerCamelCase : Optional[int] = None , lowerCamelCase : Dict=False , lowerCamelCase : bool = False , ):
'''simple docstring'''
__lowercase = hans_processors[task]()
__lowercase = os.path.join(
lowerCamelCase , "cached_{}_{}_{}_{}".format(
"dev" if evaluate else "train" , tokenizer.__class__.__name__ , str(lowerCamelCase ) , lowerCamelCase , ) , )
__lowercase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + ".lock"
with FileLock(lowerCamelCase ):
if os.path.exists(lowerCamelCase ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
__lowercase = torch.load(lowerCamelCase )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
__lowercase = (
processor.get_dev_examples(lowerCamelCase ) if evaluate else processor.get_train_examples(lowerCamelCase )
)
logger.info("Training examples: %s" , len(lowerCamelCase ) )
__lowercase = hans_convert_examples_to_features(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
logger.info("Saving features into cached file %s" , lowerCamelCase )
torch.save(self.features , lowerCamelCase )
def __len__( self : str ):
'''simple docstring'''
return len(self.features )
def __getitem__( self : Tuple , lowerCamelCase : Dict ):
'''simple docstring'''
return self.features[i]
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self.label_list
if is_tf_available():
import tensorflow as tf
class _A :
'''simple docstring'''
_snake_case : List[InputFeatures]
def __init__( self : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : PreTrainedTokenizer , lowerCamelCase : str , lowerCamelCase : Optional[int] = 128 , lowerCamelCase : Tuple=False , lowerCamelCase : bool = False , ):
'''simple docstring'''
__lowercase = hans_processors[task]()
__lowercase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
__lowercase = processor.get_dev_examples(lowerCamelCase ) if evaluate else processor.get_train_examples(lowerCamelCase )
__lowercase = hans_convert_examples_to_features(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc="convert examples to features" ):
if ex_index % 10_000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(lowerCamelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
__lowercase = tf.data.Dataset.from_generator(
lowerCamelCase , (
{
"example_id": tf.intaa,
"input_ids": tf.intaa,
"attention_mask": tf.intaa,
"token_type_ids": tf.intaa,
},
tf.intaa,
) , (
{
"example_id": tf.TensorShape([] ),
"input_ids": tf.TensorShape([None, None] ),
"attention_mask": tf.TensorShape([None, None] ),
"token_type_ids": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def _snake_case ( self : Dict ):
'''simple docstring'''
return self.dataset
def __len__( self : List[str] ):
'''simple docstring'''
return len(self.features )
def __getitem__( self : Optional[int] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.features[i]
def _snake_case ( self : List[Any] ):
'''simple docstring'''
return self.label_list
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Tuple ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(lowerCamelCase , "heuristics_train_set.txt" ) ) , "train" )
def _snake_case ( self : Any , lowerCamelCase : Tuple ):
'''simple docstring'''
return self._create_examples(self._read_tsv(os.path.join(lowerCamelCase , "heuristics_evaluation_set.txt" ) ) , "dev" )
def _snake_case ( self : Tuple ):
'''simple docstring'''
return ["contradiction", "entailment", "neutral"]
def _snake_case ( self : Tuple , lowerCamelCase : int , lowerCamelCase : List[Any] ):
'''simple docstring'''
__lowercase = []
for i, line in enumerate(lowerCamelCase ):
if i == 0:
continue
__lowercase = "%s-%s" % (set_type, line[0])
__lowercase = line[5]
__lowercase = line[6]
__lowercase = line[7][2:] if line[7].startswith("ex" ) else line[7]
__lowercase = line[0]
examples.append(InputExample(guid=lowerCamelCase , text_a=lowerCamelCase , text_b=lowerCamelCase , label=lowerCamelCase , pairID=lowerCamelCase ) )
return examples
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
__lowercase = {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE )}
__lowercase = []
for ex_index, example in tqdm.tqdm(enumerate(_SCREAMING_SNAKE_CASE ) , desc="convert examples to features" ):
if ex_index % 1_0_0_0_0 == 0:
logger.info("Writing example %d" % (ex_index) )
__lowercase = tokenizer(
example.text_a , example.text_b , add_special_tokens=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , padding="max_length" , truncation=_SCREAMING_SNAKE_CASE , return_overflowing_tokens=_SCREAMING_SNAKE_CASE , )
__lowercase = label_map[example.label] if example.label in label_map else 0
__lowercase = int(example.pairID )
features.append(InputFeatures(**_SCREAMING_SNAKE_CASE , label=_SCREAMING_SNAKE_CASE , pairID=_SCREAMING_SNAKE_CASE ) )
for i, example in enumerate(examples[:5] ):
logger.info("*** Example ***" )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
snake_case__ : Dict = {
"""hans""": 3,
}
snake_case__ : Tuple = {
"""hans""": HansProcessor,
}
| 655 |
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__ : List[Any] = 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 ( _lowercase ):
'''simple docstring'''
_snake_case : List[Any] = """yolos"""
def __init__( self : Union[str, Any] , lowerCamelCase : Union[str, Any]=768 , lowerCamelCase : int=12 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Optional[Any]=3_072 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Dict=0.0 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Any=0.02 , lowerCamelCase : Optional[Any]=1e-12 , lowerCamelCase : Optional[Any]=[512, 864] , lowerCamelCase : str=16 , lowerCamelCase : Dict=3 , lowerCamelCase : str=True , lowerCamelCase : List[Any]=100 , lowerCamelCase : Dict=True , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=1 , lowerCamelCase : str=5 , lowerCamelCase : Any=2 , lowerCamelCase : str=5 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : List[Any]=0.1 , **lowerCamelCase : List[Any] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = num_detection_tokens
__lowercase = use_mid_position_embeddings
__lowercase = auxiliary_loss
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = version.parse("""1.11""" )
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : str ):
'''simple docstring'''
return 1e-4
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return 12
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if any(not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(_SCREAMING_SNAKE_CASE ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_SCREAMING_SNAKE_CASE , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 655 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Optional[int] = logging.get_logger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = MobileNetVaConfig(layer_norm_eps=0.0_0_1 )
if "_quant" in model_name:
raise ValueError("Quantized models are not supported." )
__lowercase = re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" , _SCREAMING_SNAKE_CASE )
if matches:
__lowercase = float(matches[1] )
__lowercase = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
__lowercase = 1_0_0_1
__lowercase = "imagenet-1k-id2label.json"
__lowercase = "huggingface/label-files"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ) + 1: v for k, v in idalabel.items()}
__lowercase = "background"
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ( ):
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = get_mobilenet_va_config(_SCREAMING_SNAKE_CASE )
# Load 🤗 model
__lowercase = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
__lowercase = MobileNetVaImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size} , size={"shortest_edge": config.image_size + 3_2} , )
__lowercase = image_processor(images=prepare_img() , return_tensors="pt" )
__lowercase = model(**_SCREAMING_SNAKE_CASE )
__lowercase = outputs.logits
assert logits.shape == (1, 1_0_0_1)
if model_name == "mobilenet_v1_1.0_224":
__lowercase = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] )
elif model_name == "mobilenet_v1_0.75_192":
__lowercase = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3] )
else:
__lowercase = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("Pushing to the hub..." )
__lowercase = "google/" + model_name
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""mobilenet_v1_1.0_224""",
type=str,
help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt 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."""
)
snake_case__ : Dict = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 655 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
snake_case__ : List[str] = logging.getLogger(__name__)
require_version("""pytorch_lightning>=1.0.4""")
snake_case__ : Optional[Any] = {
"""base""": AutoModel,
"""sequence-classification""": AutoModelForSequenceClassification,
"""question-answering""": AutoModelForQuestionAnswering,
"""pretraining""": AutoModelForPreTraining,
"""token-classification""": AutoModelForTokenClassification,
"""language-modeling""": AutoModelWithLMHead,
"""summarization""": AutoModelForSeqaSeqLM,
"""translation""": AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
snake_case__ : Optional[int] = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
snake_case__ : List[Any] = sorted(arg_to_scheduler.keys())
snake_case__ : str = """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : argparse.Namespace , lowerCamelCase : List[Any]=None , lowerCamelCase : str="base" , lowerCamelCase : int=None , lowerCamelCase : List[Any]=None , lowerCamelCase : Optional[Any]=None , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowerCamelCase )
__lowercase = 0
__lowercase = Path(self.hparams.output_dir )
__lowercase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
__lowercase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({"num_labels": num_labels} if num_labels is not None else {}) , cache_dir=lowerCamelCase , **lowerCamelCase , )
else:
__lowercase = config
__lowercase = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(self.hparams , lowerCamelCase , lowerCamelCase ):
assert hasattr(self.config , lowerCamelCase ), f"""model config doesn't have a `{p}` attribute"""
setattr(self.config , lowerCamelCase , getattr(self.hparams , lowerCamelCase ) )
if tokenizer is None:
__lowercase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowerCamelCase , )
else:
__lowercase = tokenizer
__lowercase = MODEL_MODES[mode]
if model is None:
__lowercase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool(".ckpt" in self.hparams.model_name_or_path ) , config=self.config , cache_dir=lowerCamelCase , )
else:
__lowercase = model
def _snake_case ( self : List[str] , *lowerCamelCase : Union[str, Any] , **lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowercase = self.model_type.from_pretrained(*lowerCamelCase , **lowerCamelCase )
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = arg_to_scheduler[self.hparams.lr_scheduler]
__lowercase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
__lowercase = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return scheduler
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.model
__lowercase = ["bias", "LayerNorm.weight"]
__lowercase = [
{
"params": [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
if self.hparams.adafactor:
__lowercase = Adafactor(
lowerCamelCase , lr=self.hparams.learning_rate , scale_parameter=lowerCamelCase , relative_step=lowerCamelCase )
else:
__lowercase = AdamW(
lowerCamelCase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
__lowercase = optimizer
__lowercase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int , lowerCamelCase : Any ):
'''simple docstring'''
return self.validation_step(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Tuple , lowerCamelCase : Any ):
'''simple docstring'''
return self.validation_end(lowerCamelCase )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
__lowercase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def _snake_case ( self : Union[str, Any] , lowerCamelCase : List[Any] ):
'''simple docstring'''
if stage == "test":
__lowercase = len(self.test_dataloader().dataset )
else:
__lowercase = self.get_dataloader("train" , self.hparams.train_batch_size , shuffle=lowerCamelCase )
__lowercase = len(self.train_dataloader().dataset )
def _snake_case ( self : Optional[int] , lowerCamelCase : str , lowerCamelCase : int , lowerCamelCase : bool = False ):
'''simple docstring'''
raise NotImplementedError("You must implement this for your task" )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
return self.train_loader
def _snake_case ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader("dev" , self.hparams.eval_batch_size , shuffle=lowerCamelCase )
def _snake_case ( self : List[str] ):
'''simple docstring'''
return self.get_dataloader("test" , self.hparams.eval_batch_size , shuffle=lowerCamelCase )
def _snake_case ( self : Optional[Any] , lowerCamelCase : Any ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , "cached_{}_{}_{}".format(
lowerCamelCase , list(filter(lowerCamelCase , self.hparams.model_name_or_path.split("/" ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def _snake_case ( self : Optional[int] , lowerCamelCase : Dict[str, Any] ):
'''simple docstring'''
__lowercase = self.output_dir.joinpath("best_tfmr" )
__lowercase = self.step_count
self.model.save_pretrained(lowerCamelCase )
self.tokenizer.save_pretrained(lowerCamelCase )
@staticmethod
def _snake_case ( lowerCamelCase : Any , lowerCamelCase : List[str] ):
'''simple docstring'''
parser.add_argument(
"--model_name_or_path" , default=lowerCamelCase , type=lowerCamelCase , required=lowerCamelCase , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--config_name" , default="" , type=lowerCamelCase , help="Pretrained config name or path if not the same as model_name" )
parser.add_argument(
"--tokenizer_name" , default=lowerCamelCase , type=lowerCamelCase , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument(
"--cache_dir" , default=str(Path(lowerCamelCase ).parent / "test_run" / "cache" ) , type=lowerCamelCase , help="Where do you want to store the pre-trained models downloaded from huggingface.co" , )
parser.add_argument(
"--encoder_layerdrop" , type=lowerCamelCase , help="Encoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--decoder_layerdrop" , type=lowerCamelCase , help="Decoder layer dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--dropout" , type=lowerCamelCase , help="Dropout probability (Optional). Goes into model.config" , )
parser.add_argument(
"--attention_dropout" , type=lowerCamelCase , help="Attention dropout probability (Optional). Goes into model.config" , )
parser.add_argument("--learning_rate" , default=5e-5 , type=lowerCamelCase , help="The initial learning rate for Adam." )
parser.add_argument(
"--lr_scheduler" , default="linear" , choices=lowerCamelCase , metavar=lowerCamelCase , type=lowerCamelCase , help="Learning rate scheduler" , )
parser.add_argument("--weight_decay" , default=0.0 , type=lowerCamelCase , help="Weight decay if we apply some." )
parser.add_argument("--adam_epsilon" , default=1e-8 , type=lowerCamelCase , help="Epsilon for Adam optimizer." )
parser.add_argument("--warmup_steps" , default=0 , type=lowerCamelCase , help="Linear warmup over warmup_steps." )
parser.add_argument("--num_workers" , default=4 , type=lowerCamelCase , help="kwarg passed to DataLoader" )
parser.add_argument("--num_train_epochs" , dest="max_epochs" , default=3 , type=lowerCamelCase )
parser.add_argument("--train_batch_size" , default=32 , type=lowerCamelCase )
parser.add_argument("--eval_batch_size" , default=32 , type=lowerCamelCase )
parser.add_argument("--adafactor" , action="store_true" )
class _A ( pl.Callback ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : List[Any] ):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _A ( pl.Callback ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : List[str] ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowerCamelCase )
class _A ( pl.Callback ):
'''simple docstring'''
def _snake_case ( self : str , lowerCamelCase : Tuple , lowerCamelCase : List[str] ):
'''simple docstring'''
__lowercase = trainer.lr_schedulers[0]["scheduler"]
__lowercase = {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowerCamelCase )
def _snake_case ( self : List[str] , lowerCamelCase : pl.Trainer , lowerCamelCase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info("***** Validation results *****" )
__lowercase = trainer.callback_metrics
# Log results
for key in sorted(lowerCamelCase ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(lowerCamelCase , str(metrics[key] ) ) )
def _snake_case ( self : List[str] , lowerCamelCase : pl.Trainer , lowerCamelCase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info("***** Test results *****" )
__lowercase = trainer.callback_metrics
# Log and save results to file
__lowercase = os.path.join(pl_module.hparams.output_dir , "test_results.txt" )
with open(lowerCamelCase , "w" ) as writer:
for key in sorted(lowerCamelCase ):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(lowerCamelCase , str(metrics[key] ) ) )
writer.write("{} = {}\n".format(lowerCamelCase , str(metrics[key] ) ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"--output_dir" , default=str(Path(_SCREAMING_SNAKE_CASE ).parent / "test_run" / "model_checkpoints" ) , type=_SCREAMING_SNAKE_CASE , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=_SCREAMING_SNAKE_CASE , default="O2" , help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_tpu_cores" , dest="tpu_cores" , type=_SCREAMING_SNAKE_CASE )
parser.add_argument("--max_grad_norm" , dest="gradient_clip_val" , default=1.0 , type=_SCREAMING_SNAKE_CASE , help="Max gradient norm" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_predict" , action="store_true" , help="Whether to run predictions on the test set." )
parser.add_argument(
"--gradient_accumulation_steps" , dest="accumulate_grad_batches" , type=_SCREAMING_SNAKE_CASE , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--seed" , type=_SCREAMING_SNAKE_CASE , default=4_2 , help="random seed for initialization" )
parser.add_argument(
"--data_dir" , default=str(Path(_SCREAMING_SNAKE_CASE ).parent / "test_run" / "dummy-train-data" ) , type=_SCREAMING_SNAKE_CASE , help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." , )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=[] , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ):
pl.seed_everything(args.seed )
# init model
__lowercase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
# add custom checkpoints
if checkpoint_callback is None:
__lowercase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix="checkpoint" , monitor="val_loss" , mode="min" , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_SCREAMING_SNAKE_CASE )
if logging_callback is None:
__lowercase = LoggingCallback()
__lowercase = {}
if args.fpaa:
__lowercase = 1_6
if args.gpus > 1:
__lowercase = "auto"
__lowercase = "ddp"
__lowercase = args.accumulate_grad_batches
__lowercase = None
__lowercase = "auto"
__lowercase = pl.Trainer.from_argparse_args(
_SCREAMING_SNAKE_CASE , weights_summary=_SCREAMING_SNAKE_CASE , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_SCREAMING_SNAKE_CASE , val_check_interval=1 , num_sanity_val_steps=2 , **_SCREAMING_SNAKE_CASE , )
if args.do_train:
trainer.fit(_SCREAMING_SNAKE_CASE )
else:
print("RAG modeling tests with new set functions successfuly executed!" )
return trainer
| 655 |
from __future__ import annotations
from typing import Any
class _A :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = num_of_nodes
__lowercase = []
__lowercase = {}
def _snake_case ( self : Dict , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def _snake_case ( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
__lowercase = self.find_component(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : list[int] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
__lowercase = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__lowercase = self.find_component(lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCamelCase )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = []
__lowercase = 0
__lowercase = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__lowercase = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__lowercase = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
__lowercase = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def snake_case_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
from transformers.models.mobilevitva.modeling_mobilevitva import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCamelCase , "width_multiplier" ) )
class _A :
'''simple docstring'''
def __init__( self : int , lowerCamelCase : str , lowerCamelCase : List[str]=13 , lowerCamelCase : Optional[int]=64 , lowerCamelCase : List[str]=2 , lowerCamelCase : int=3 , lowerCamelCase : List[str]="swish" , lowerCamelCase : Dict=3 , lowerCamelCase : Any=32 , lowerCamelCase : Any=0.1 , lowerCamelCase : Optional[int]=0.02 , lowerCamelCase : Any=True , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[str]=10 , lowerCamelCase : List[Any]=None , lowerCamelCase : Optional[int]=0.25 , lowerCamelCase : Dict=0.0 , lowerCamelCase : str=0.0 , ):
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = make_divisible(512 * width_multiplier , divisor=8 )
__lowercase = hidden_act
__lowercase = conv_kernel_size
__lowercase = output_stride
__lowercase = classifier_dropout_prob
__lowercase = use_labels
__lowercase = is_training
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = scope
__lowercase = width_multiplier
__lowercase = ffn_dropout
__lowercase = attn_dropout
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels, pixel_labels
def _snake_case ( self : str ):
'''simple docstring'''
return MobileViTVaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , width_multiplier=self.width_multiplier , ffn_dropout=self.ffn_dropout_prob , attn_dropout=self.attn_dropout_prob , )
def _snake_case ( self : Dict , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : List[str] , lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = MobileViTVaModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _snake_case ( self : int , lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = self.num_labels
__lowercase = MobileViTVaForImageClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Any , lowerCamelCase : Optional[Any] , lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.num_labels
__lowercase = MobileViTVaForSemanticSegmentation(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
__lowercase = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _A ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : List[str] = (
(MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
if is_torch_available()
else ()
)
_snake_case : Dict = (
{
"""feature-extraction""": MobileViTVaModel,
"""image-classification""": MobileViTVaForImageClassification,
"""image-segmentation""": MobileViTVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_snake_case : Any = False
_snake_case : int = False
_snake_case : Optional[Any] = False
_snake_case : Any = False
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = MobileViTVaModelTester(self )
__lowercase = MobileViTVaConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase )
def _snake_case ( self : Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViTV2 does not use inputs_embeds" )
def _snake_case ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="MobileViTV2 does not support input and output embeddings" )
def _snake_case ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="MobileViTV2 does not output attentions" )
def _snake_case ( self : str ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run." )
def _snake_case ( self : Tuple ):
'''simple docstring'''
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : int ):
'''simple docstring'''
pass
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowerCamelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def _snake_case ( self : int ):
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase : Optional[Any] , lowerCamelCase : List[str] , lowerCamelCase : Dict ):
__lowercase = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
__lowercase = outputs.hidden_states
__lowercase = 5
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__lowercase = 2
for i in range(len(lowerCamelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase )
@slow
def _snake_case ( self : Any ):
'''simple docstring'''
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = MobileViTVaModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def snake_case_ ( ):
__lowercase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return (
MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" )
if is_vision_available()
else None
)
@slow
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" ).to(
lowerCamelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=lowerCamelCase , return_tensors="pt" ).to(lowerCamelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
# verify the logits
__lowercase = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
__lowercase = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1e-4 ) )
@slow
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__lowercase = model.to(lowerCamelCase )
__lowercase = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__lowercase = prepare_img()
__lowercase = image_processor(images=lowerCamelCase , return_tensors="pt" ).to(lowerCamelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.logits
# verify the logits
__lowercase = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , lowerCamelCase )
__lowercase = torch.tensor(
[
[[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
[[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
[[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
] , device=lowerCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1e-4 ) )
@slow
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__lowercase = model.to(lowerCamelCase )
__lowercase = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__lowercase = prepare_img()
__lowercase = image_processor(images=lowerCamelCase , return_tensors="pt" ).to(lowerCamelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.logits.detach().cpu()
__lowercase = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase , target_sizes=[(50, 60)] )
__lowercase = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , lowerCamelCase )
__lowercase = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase )
__lowercase = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , lowerCamelCase )
| 655 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[str] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
snake_case__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
snake_case__ : Optional[Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : Optional[Any] , *lowerCamelCase : int , **lowerCamelCase : int ):
'''simple docstring'''
warnings.warn(
"The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PerceiverImageProcessor instead." , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 655 |
from __future__ import annotations
import bisect
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = 0
__lowercase = len(_SCREAMING_SNAKE_CASE ) - 1
while left <= right:
__lowercase = left + (right - left) // 2
__lowercase = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__lowercase = midpoint - 1
else:
__lowercase = midpoint + 1
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = bisect.bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
return index
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if right < left:
return None
__lowercase = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint - 1 )
else:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint + 1 , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Optional[Any] = input("""Enter numbers separated by comma:\n""").strip()
snake_case__ : Any = sorted(int(item) for item in user_input.split(""","""))
snake_case__ : Any = int(input("""Enter a single number to be found in the list:\n"""))
snake_case__ : List[Any] = binary_search(collection, target)
if result is None:
print(F'''{target} was not found in {collection}.''')
else:
print(F'''{target} was found at position {result} in {collection}.''')
| 655 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = analyze_text(_SCREAMING_SNAKE_CASE )
__lowercase = list(" " + ascii_lowercase )
# what is our total sum of probabilities.
__lowercase = sum(single_char_strings.values() )
# one length string
__lowercase = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
__lowercase = single_char_strings[ch]
__lowercase = my_str / all_sum
my_fir_sum += prob * math.loga(_SCREAMING_SNAKE_CASE ) # entropy formula.
# print entropy
print(F"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
__lowercase = sum(two_char_strings.values() )
__lowercase = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
__lowercase = cha + cha
if sequence in two_char_strings:
__lowercase = two_char_strings[sequence]
__lowercase = int(_SCREAMING_SNAKE_CASE ) / all_sum
my_sec_sum += prob * math.loga(_SCREAMING_SNAKE_CASE )
# print second entropy
print(F"""{round(-1 * my_sec_sum ):.1f}""" )
# print the difference between them
print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = Counter() # type: ignore
__lowercase = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(_SCREAMING_SNAKE_CASE ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def snake_case_ ( ):
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 655 |
import copy
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
from ..auto import CONFIG_MAPPING
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """conditional_detr"""
_snake_case : Union[str, Any] = ["""past_key_values"""]
_snake_case : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Optional[Any] , lowerCamelCase : int=True , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[int]=3 , lowerCamelCase : Optional[int]=300 , lowerCamelCase : List[Any]=6 , lowerCamelCase : str=2_048 , lowerCamelCase : Any=8 , lowerCamelCase : List[str]=6 , lowerCamelCase : Any=2_048 , lowerCamelCase : List[Any]=8 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : List[str]=0.0 , lowerCamelCase : List[Any]=True , lowerCamelCase : str="relu" , lowerCamelCase : int=256 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Dict=0.0 , lowerCamelCase : Tuple=0.02 , lowerCamelCase : int=1.0 , lowerCamelCase : Tuple=False , lowerCamelCase : List[str]="sine" , lowerCamelCase : List[Any]="resnet50" , lowerCamelCase : Any=True , lowerCamelCase : Any=False , lowerCamelCase : List[Any]=2 , lowerCamelCase : List[Any]=5 , lowerCamelCase : str=2 , lowerCamelCase : Dict=1 , lowerCamelCase : List[str]=1 , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Dict=5 , lowerCamelCase : List[Any]=2 , lowerCamelCase : Tuple=0.25 , **lowerCamelCase : List[str] , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
__lowercase = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = backbone_config.get("model_type" )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowerCamelCase )
__lowercase = use_timm_backbone
__lowercase = backbone_config
__lowercase = num_channels
__lowercase = num_queries
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = encoder_layers
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
__lowercase = backbone
__lowercase = use_pretrained_backbone
__lowercase = dilation
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = cls_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase , **lowerCamelCase )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _snake_case ( self : str ):
'''simple docstring'''
return self.d_model
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Any = version.parse("""1.11""" )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _snake_case ( self : Any ):
'''simple docstring'''
return 1e-5
@property
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
return 12
| 655 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case__ : int = """."""
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case__ : List[Any] = [
"""Assert""",
"""AssignVariableOp""",
"""EmptyTensorList""",
"""MergeV2Checkpoints""",
"""ReadVariableOp""",
"""ResourceGather""",
"""RestoreV2""",
"""SaveV2""",
"""ShardedFilename""",
"""StatefulPartitionedCall""",
"""StaticRegexFullMatch""",
"""VarHandleOp""",
]
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = SavedModel()
__lowercase = []
with open(os.path.join(_SCREAMING_SNAKE_CASE , "utils" , "tf_ops" , "onnx.json" ) ) as f:
__lowercase = json.load(_SCREAMING_SNAKE_CASE )["opsets"]
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(_SCREAMING_SNAKE_CASE )] )
with open(_SCREAMING_SNAKE_CASE , "rb" ) as f:
saved_model.ParseFromString(f.read() )
__lowercase = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__lowercase = sorted(_SCREAMING_SNAKE_CASE )
__lowercase = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(_SCREAMING_SNAKE_CASE )
if strict and len(_SCREAMING_SNAKE_CASE ) > 0:
raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops )
elif len(_SCREAMING_SNAKE_CASE ) > 0:
print(F"""Found the following incompatible ops for the opset {opset}:""" )
print(*_SCREAMING_SNAKE_CASE , sep="\n" )
else:
print(F"""The saved model {saved_model_path} can properly be converted with ONNX.""" )
if __name__ == "__main__":
snake_case__ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
parser.add_argument(
"""--opset""", default=12, type=int, help="""The ONNX opset against which the model has to be tested."""
)
parser.add_argument(
"""--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
)
parser.add_argument(
"""--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
)
snake_case__ : Dict = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 655 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Any = logging.get_logger(__name__)
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
_snake_case : Dict = """maskformer-swin"""
_snake_case : List[str] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : List[str] , lowerCamelCase : Any=224 , lowerCamelCase : Optional[Any]=4 , lowerCamelCase : Dict=3 , lowerCamelCase : Tuple=96 , lowerCamelCase : str=[2, 2, 6, 2] , lowerCamelCase : Dict=[3, 6, 12, 24] , lowerCamelCase : Optional[Any]=7 , lowerCamelCase : Any=4.0 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[str]=0.0 , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : int="gelu" , lowerCamelCase : Optional[int]=False , lowerCamelCase : List[Any]=0.02 , lowerCamelCase : Tuple=1e-5 , lowerCamelCase : Dict=None , lowerCamelCase : Dict=None , **lowerCamelCase : int , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(lowerCamelCase )
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = layer_norm_eps
__lowercase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowercase = int(embed_dim * 2 ** (len(lowerCamelCase ) - 1) )
__lowercase = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(lowerCamelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase , out_indices=lowerCamelCase , stage_names=self.stage_names )
| 655 | 1 |
import operator as op
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
__lowercase = lambda _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int(x / y ) # noqa: E731 integer division operation
__lowercase = {
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8 ) , "Action".center(1_2 ) , "Stack" , sep=" | " )
print("-" * (3_0 + len(_SCREAMING_SNAKE_CASE )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(_SCREAMING_SNAKE_CASE ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("push(" + x + ")").ljust(1_2 ) , ",".join(_SCREAMING_SNAKE_CASE ) , sep=" | " )
else:
__lowercase = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + b + ")").ljust(1_2 ) , ",".join(_SCREAMING_SNAKE_CASE ) , sep=" | " )
__lowercase = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + a + ")").ljust(1_2 ) , ",".join(_SCREAMING_SNAKE_CASE ) , sep=" | " )
stack.append(
str(opr[x](int(_SCREAMING_SNAKE_CASE ) , int(_SCREAMING_SNAKE_CASE ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ("push(" + a + x + b + ")").ljust(1_2 ) , ",".join(_SCREAMING_SNAKE_CASE ) , sep=" | " , )
return int(stack[0] )
if __name__ == "__main__":
snake_case__ : Optional[Any] = input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
print("""\n\tResult = """, solve(Postfix))
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__lowercase = gray_code_sequence_string(_SCREAMING_SNAKE_CASE )
#
# convert them to integers
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = int(sequence[i] , 2 )
return sequence
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__lowercase = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__lowercase = gray_code_sequence_string(bit_count - 1 )
__lowercase = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__lowercase = "0" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__lowercase = "1" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
snake_case__ : List[Any] = [
"""python""",
"""tqdm""",
"""regex""",
"""requests""",
"""packaging""",
"""filelock""",
"""numpy""",
"""tokenizers""",
"""huggingface-hub""",
"""safetensors""",
"""accelerate""",
"""pyyaml""",
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
require_version(deps[pkg] , _SCREAMING_SNAKE_CASE )
| 655 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=True ):
model.train()
__lowercase = model(_SCREAMING_SNAKE_CASE )
__lowercase = F.mse_loss(_SCREAMING_SNAKE_CASE , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
set_seed(4_2 )
__lowercase = RegressionModel()
__lowercase = deepcopy(_SCREAMING_SNAKE_CASE )
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
model.to(accelerator.device )
if sched:
__lowercase = AdamW(params=model.parameters() , lr=1E-3 )
__lowercase = AdamW(params=ddp_model.parameters() , lr=1E-3 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
# Make a copy of `model`
if sched:
__lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test when on a single CPU or GPU that the context manager does nothing
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test on distributed setup that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(_SCREAMING_SNAKE_CASE ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
GradientState._reset_state()
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
__lowercase = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE ))
if accelerator.num_processes > 1:
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
GradientState._reset_state()
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase = RegressionDataset(length=9_6 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if iteration < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if batch_num < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = accelerator.state
if state.local_process_index == 0:
print("**Test `accumulate` gradient accumulation with dataloader break**" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("**Test NOOP `no_sync` context manager**" )
test_noop_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("**Test Distributed `no_sync` context manager**" )
test_distributed_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("<" , "2.0" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Any = logging.get_logger(__name__)
set_seed(7_70)
snake_case__ : Tuple = {
"""c_attn""": """att_proj""",
"""c_proj""": """out_proj""",
"""c_fc""": """in_proj""",
"""transformer.""": """""",
"""h.""": """layers.""",
"""ln_1""": """layernorm_1""",
"""ln_2""": """layernorm_2""",
"""ln_f""": """layernorm_final""",
"""wpe""": """position_embeds_layer""",
"""wte""": """input_embeds_layer""",
}
snake_case__ : Optional[int] = {
"""text_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """text.pt""",
},
"""coarse_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """coarse.pt""",
},
"""fine_small""": {
"""repo_id""": """suno/bark""",
"""file_name""": """fine.pt""",
},
"""text""": {
"""repo_id""": """suno/bark""",
"""file_name""": """text_2.pt""",
},
"""coarse""": {
"""repo_id""": """suno/bark""",
"""file_name""": """coarse_2.pt""",
},
"""fine""": {
"""repo_id""": """suno/bark""",
"""file_name""": """fine_2.pt""",
},
}
snake_case__ : int = os.path.dirname(os.path.abspath(__file__))
snake_case__ : List[str] = os.path.join(os.path.expanduser("""~"""), """.cache""")
snake_case__ : List[Any] = os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = model_type
if use_small:
key += "_small"
return os.path.join(_SCREAMING_SNAKE_CASE , REMOTE_MODEL_PATHS[key]["file_name"] )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
hf_hub_download(repo_id=_SCREAMING_SNAKE_CASE , filename=_SCREAMING_SNAKE_CASE , local_dir=_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="text" ):
if model_type == "text":
__lowercase = BarkSemanticModel
__lowercase = BarkSemanticConfig
__lowercase = BarkSemanticGenerationConfig
elif model_type == "coarse":
__lowercase = BarkCoarseModel
__lowercase = BarkCoarseConfig
__lowercase = BarkCoarseGenerationConfig
elif model_type == "fine":
__lowercase = BarkFineModel
__lowercase = BarkFineConfig
__lowercase = BarkFineGenerationConfig
else:
raise NotImplementedError()
__lowercase = F"""{model_type}_small""" if use_small else model_type
__lowercase = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(_SCREAMING_SNAKE_CASE ):
logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
_download(model_info["repo_id"] , model_info["file_name"] )
__lowercase = torch.load(_SCREAMING_SNAKE_CASE , map_location=_SCREAMING_SNAKE_CASE )
# this is a hack
__lowercase = checkpoint["model_args"]
if "input_vocab_size" not in model_args:
__lowercase = model_args["vocab_size"]
__lowercase = model_args["vocab_size"]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
__lowercase = model_args.pop("n_head" )
__lowercase = model_args.pop("n_embd" )
__lowercase = model_args.pop("n_layer" )
__lowercase = ConfigClass(**checkpoint["model_args"] )
__lowercase = ModelClass(config=_SCREAMING_SNAKE_CASE )
__lowercase = GenerationConfigClass()
__lowercase = model_generation_config
__lowercase = checkpoint["model"]
# fixup checkpoint
__lowercase = "_orig_mod."
for k, v in list(state_dict.items() ):
if k.startswith(_SCREAMING_SNAKE_CASE ):
# replace part of the key with corresponding layer name in HF implementation
__lowercase = k[len(_SCREAMING_SNAKE_CASE ) :]
for old_layer_name in new_layer_name_dict:
__lowercase = new_k.replace(_SCREAMING_SNAKE_CASE , new_layer_name_dict[old_layer_name] )
__lowercase = state_dict.pop(_SCREAMING_SNAKE_CASE )
__lowercase = set(state_dict.keys() ) - set(model.state_dict().keys() )
__lowercase = {k for k in extra_keys if not k.endswith(".attn.bias" )}
__lowercase = set(model.state_dict().keys() ) - set(state_dict.keys() )
__lowercase = {k for k in missing_keys if not k.endswith(".attn.bias" )}
if len(_SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(F"""extra keys found: {extra_keys}""" )
if len(_SCREAMING_SNAKE_CASE ) != 0:
raise ValueError(F"""missing keys: {missing_keys}""" )
model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
__lowercase = model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE )
__lowercase = checkpoint["best_val_loss"].item()
logger.info(F"""model loaded: {round(n_params/1E6 , 1 )}M params, {round(_SCREAMING_SNAKE_CASE , 3 )} loss""" )
model.eval()
model.to(_SCREAMING_SNAKE_CASE )
del checkpoint, state_dict
return model
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="text" ):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
__lowercase = "cpu" # do conversion on cpu
__lowercase = _get_ckpt_path(_SCREAMING_SNAKE_CASE , use_small=_SCREAMING_SNAKE_CASE )
__lowercase = _load_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , model_type=_SCREAMING_SNAKE_CASE , use_small=_SCREAMING_SNAKE_CASE )
# load bark initial model
__lowercase = _bark_load_model(_SCREAMING_SNAKE_CASE , "cpu" , model_type=_SCREAMING_SNAKE_CASE , use_small=_SCREAMING_SNAKE_CASE )
if model_type == "text":
__lowercase = bark_model["model"]
if model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE ) != bark_model.get_num_params():
raise ValueError("initial and new models don't have the same number of parameters" )
# check if same output as the bark model
__lowercase = 5
__lowercase = 1_0
if model_type in ["text", "coarse"]:
__lowercase = torch.randint(2_5_6 , (batch_size, sequence_length) , dtype=torch.int )
__lowercase = bark_model(_SCREAMING_SNAKE_CASE )[0]
__lowercase = model(_SCREAMING_SNAKE_CASE )
# take last logits
__lowercase = output_new_model_total.logits[:, [-1], :]
else:
__lowercase = 3
__lowercase = 8
__lowercase = torch.randint(2_5_6 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
__lowercase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = bark_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("initial and new outputs don't have the same shape" )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError("initial and new outputs are not equal" )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = BarkSemanticConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , "config.json" ) )
__lowercase = BarkCoarseConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , "config.json" ) )
__lowercase = BarkFineConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE , "config.json" ) )
__lowercase = EncodecConfig.from_pretrained("facebook/encodec_24khz" )
__lowercase = BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE )
__lowercase = BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE )
__lowercase = BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE )
__lowercase = EncodecModel.from_pretrained("facebook/encodec_24khz" )
__lowercase = BarkConfig.from_sub_model_configs(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
__lowercase = BarkModel(_SCREAMING_SNAKE_CASE )
__lowercase = semantic
__lowercase = coarseAcoustic
__lowercase = fineAcoustic
__lowercase = codec
__lowercase = bark_generation_config
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
bark.save_pretrained(_SCREAMING_SNAKE_CASE , repo_id=_SCREAMING_SNAKE_CASE , push_to_hub=_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")
snake_case__ : int = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 655 |
from ....utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : List[str] , lowerCamelCase : Any , lowerCamelCase : Dict=None , lowerCamelCase : Dict=2_048 ):
'''simple docstring'''
__lowercase = config.__dict__
__lowercase = modal_hidden_size
if num_labels:
__lowercase = num_labels
| 655 | 1 |
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ : List[str] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
snake_case__ : Optional[int] = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
__lowercase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(_SCREAMING_SNAKE_CASE ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(_SCREAMING_SNAKE_CASE ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(_SCREAMING_SNAKE_CASE ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
__lowercase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(_SCREAMING_SNAKE_CASE )
return next_generation
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = []
for _ in range(_SCREAMING_SNAKE_CASE ):
# Create output image
__lowercase = Image.new("RGB" , (len(cells[0] ), len(_SCREAMING_SNAKE_CASE )) )
__lowercase = img.load()
# Save cells to image
for x in range(len(_SCREAMING_SNAKE_CASE ) ):
for y in range(len(cells[0] ) ):
__lowercase = 2_5_5 - cells[y][x] * 2_5_5
__lowercase = (colour, colour, colour)
# Save image
images.append(_SCREAMING_SNAKE_CASE )
__lowercase = new_generation(_SCREAMING_SNAKE_CASE )
return images
if __name__ == "__main__":
snake_case__ : Optional[Any] = generate_images(GLIDER, 16)
images[0].save("""out.gif""", save_all=True, append_images=images[1:])
| 655 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class _A ( _lowercase , _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Dict = StableUnCLIPImgaImgPipeline
_snake_case : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
_snake_case : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_snake_case : int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_snake_case : int = frozenset([] )
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = 32
__lowercase = embedder_hidden_size
# image encoding components
__lowercase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
__lowercase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowerCamelCase , projection_dim=lowerCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__lowercase = StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase )
__lowercase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowercase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowercase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCamelCase , layers_per_block=1 , upcast_attention=lowerCamelCase , use_linear_projection=lowerCamelCase , )
torch.manual_seed(0 )
__lowercase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=lowerCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
__lowercase = AutoencoderKL()
__lowercase = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def _snake_case ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : Any=0 , lowerCamelCase : Union[str, Any]=True ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
__lowercase = torch.manual_seed(lowerCamelCase )
else:
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
if pil_image:
__lowercase = input_image * 0.5 + 0.5
__lowercase = input_image.clamp(0 , 1 )
__lowercase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowercase = DiffusionPipeline.numpy_to_pil(lowerCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableUnCLIPImgaImgPipeline(**lowerCamelCase )
__lowercase = sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
inputs.update({"image_embeds": None} )
__lowercase = sd_pipe(**lowerCamelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _snake_case ( self : str ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase )
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = pipe(
lowerCamelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 655 | 1 |
import numpy as np
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 1E-1_2 , _SCREAMING_SNAKE_CASE = 1_0_0 , ):
assert np.shape(_SCREAMING_SNAKE_CASE )[0] == np.shape(_SCREAMING_SNAKE_CASE )[1]
# Ensure proper dimensionality.
assert np.shape(_SCREAMING_SNAKE_CASE )[0] == np.shape(_SCREAMING_SNAKE_CASE )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(_SCREAMING_SNAKE_CASE ) == np.iscomplexobj(_SCREAMING_SNAKE_CASE )
__lowercase = np.iscomplexobj(_SCREAMING_SNAKE_CASE )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(_SCREAMING_SNAKE_CASE , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__lowercase = False
__lowercase = 0
__lowercase = 0
__lowercase = 1E1_2
while not convergence:
# Multiple matrix by the vector.
__lowercase = np.dot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Normalize the resulting output vector.
__lowercase = w / np.linalg.norm(_SCREAMING_SNAKE_CASE )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__lowercase = vector.conj().T if is_complex else vector.T
__lowercase = np.dot(_SCREAMING_SNAKE_CASE , np.dot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# Check convergence.
__lowercase = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__lowercase = True
__lowercase = lambda_
if is_complex:
__lowercase = np.real(lambda_ )
return lambda_, vector
def snake_case_ ( ):
__lowercase = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__lowercase = np.array([4_1, 4, 2_0] )
__lowercase = real_input_matrix.astype(np.complexaaa )
__lowercase = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__lowercase = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__lowercase = real_input_matrix
__lowercase = real_vector
elif problem_type == "complex":
__lowercase = complex_input_matrix
__lowercase = complex_vector
# Our implementation.
__lowercase , __lowercase = power_iteration(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__lowercase , __lowercase = np.linalg.eigh(_SCREAMING_SNAKE_CASE )
# Last eigenvalue is the maximum one.
__lowercase = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__lowercase = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(_SCREAMING_SNAKE_CASE ) - np.abs(_SCREAMING_SNAKE_CASE ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 655 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , *,
lowerCamelCase : int = 4 , lowerCamelCase : int = 768 , lowerCamelCase : int , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
super().__init__()
__lowercase = nn.Parameter(torch.zeros(lowerCamelCase ) )
# parameters for additional clip time embeddings
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
# parameters for encoder hidden states
__lowercase = clip_extra_context_tokens
__lowercase = nn.Linear(
lowerCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.LayerNorm(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , *, lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
__lowercase = image_embeddings.shape[0]
__lowercase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
__lowercase = classifier_free_guidance_embeddings.expand(
lowerCamelCase , -1 )
__lowercase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
__lowercase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
__lowercase = self.embedding_proj(lowerCamelCase )
__lowercase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase )
__lowercase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
__lowercase = self.clip_extra_context_tokens_proj(lowerCamelCase )
__lowercase = clip_extra_context_tokens.reshape(lowerCamelCase , -1 , self.clip_extra_context_tokens )
__lowercase = clip_extra_context_tokens.permute(0 , 2 , 1 )
__lowercase = self.encoder_hidden_states_proj(lowerCamelCase )
__lowercase = self.text_encoder_hidden_states_norm(lowerCamelCase )
__lowercase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 655 | 1 |
snake_case__ : Union[str, Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Make sure the supplied data is a bytes-like object
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(_SCREAMING_SNAKE_CASE )
__lowercase = "".join(bin(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for byte in data )
__lowercase = len(_SCREAMING_SNAKE_CASE ) % 6 != 0
if padding_needed:
# The padding that will be added later
__lowercase = b"=" * ((6 - len(_SCREAMING_SNAKE_CASE ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_SCREAMING_SNAKE_CASE ) % 6)
else:
__lowercase = b""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_SCREAMING_SNAKE_CASE ) , 6 ) ).encode()
+ padding
)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = (
"argument should be a bytes-like object or ASCII string, "
F"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(_SCREAMING_SNAKE_CASE )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
try:
__lowercase = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
__lowercase = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_SCREAMING_SNAKE_CASE ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
__lowercase = encoded_data[:-padding]
__lowercase = "".join(
bin(B64_CHARSET.index(_SCREAMING_SNAKE_CASE ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
__lowercase = "".join(
bin(B64_CHARSET.index(_SCREAMING_SNAKE_CASE ) )[2:].zfill(6 ) for char in encoded_data )
__lowercase = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_SCREAMING_SNAKE_CASE ) , 8 )
]
return bytes(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case__ : Union[str, Any] = TypeVar("""T""")
snake_case__ : Optional[int] = TypeVar("""U""")
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : T | None , lowerCamelCase : U | None ):
'''simple docstring'''
__lowercase = key
__lowercase = val
__lowercase = None
__lowercase = None
def __repr__( self : Any ):
'''simple docstring'''
return (
f"""Node: key: {self.key}, val: {self.val}, """
f"""has next: {bool(self.next )}, has prev: {bool(self.prev )}"""
)
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase , __lowercase = self.rear, self.head
def __repr__( self : Optional[Any] ):
'''simple docstring'''
__lowercase = ["DoubleLinkedList"]
__lowercase = self.head
while node.next is not None:
rep.append(str(lowerCamelCase ) )
__lowercase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
__lowercase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
__lowercase = node
__lowercase = previous
__lowercase = node
__lowercase = self.rear
def _snake_case ( self : Optional[int] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
if node.prev is None or node.next is None:
return None
__lowercase = node.next
__lowercase = node.prev
__lowercase = None
__lowercase = None
return node
class _A ( Generic[T, U] ):
'''simple docstring'''
_snake_case : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = DoubleLinkedList()
__lowercase = capacity
__lowercase = 0
__lowercase = 0
__lowercase = 0
__lowercase = {}
def __repr__( self : Optional[Any] ):
'''simple docstring'''
return (
f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
f"""capacity={self.capacity}, current size={self.num_keys})"""
)
def __contains__( self : Dict , lowerCamelCase : T ):
'''simple docstring'''
return key in self.cache
def _snake_case ( self : List[Any] , lowerCamelCase : T ):
'''simple docstring'''
if key in self.cache:
self.hits += 1
__lowercase = self.cache[key]
__lowercase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowerCamelCase )
return node.val
self.miss += 1
return None
def _snake_case ( self : Union[str, Any] , lowerCamelCase : T , lowerCamelCase : U ):
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
__lowercase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowerCamelCase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
__lowercase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
__lowercase = value
self.list.add(lowerCamelCase )
@classmethod
def _snake_case ( cls : Union[str, Any] , lowerCamelCase : int = 128 ):
'''simple docstring'''
def cache_decorator_inner(lowerCamelCase : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*lowerCamelCase : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
__lowercase = LRUCache(lowerCamelCase )
__lowercase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
__lowercase = func(*lowerCamelCase )
cls.decorator_function_to_instance_map[func].put(args[0] , lowerCamelCase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowerCamelCase , "cache_info" , lowerCamelCase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
snake_case__ : Optional[Any] = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling.....""")
snake_case__ : Optional[Any] = F'''https://www.google.com/search?q={query}&num=100'''
snake_case__ : str = requests.get(
url,
headers={"""User-Agent""": str(UserAgent().random)},
)
try:
snake_case__ : str = (
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """yuRUbf"""})
.find("""a""")
.get("""href""")
)
except AttributeError:
snake_case__ : Any = parse_qs(
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """kCrYT"""})
.find("""a""")
.get("""href""")
)["""url"""][0]
webbrowser.open(link)
| 655 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
snake_case__ : Optional[Any] = logging.getLogger()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = "\n".join(_SCREAMING_SNAKE_CASE )
Path(_SCREAMING_SNAKE_CASE ).open("w" ).writelines(_SCREAMING_SNAKE_CASE )
snake_case__ : List[str] = """patrickvonplaten/t5-tiny-random"""
snake_case__ : int = """sshleifer/bart-tiny-random"""
snake_case__ : Union[str, Any] = """sshleifer/tiny-mbart"""
snake_case__ : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : str , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(lowerCamelCase , lowerCamelCase )
__lowercase = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_generate()
assert Path(lowerCamelCase ).exists()
# os.remove(Path(output_file_name))
def _snake_case ( self : Dict ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
__lowercase = Path(self.get_auto_remove_tmp_dir() )
__lowercase = str(tmp_dir / "scores.json" )
__lowercase = str(tmp_dir / "val.target" )
_dump_articles(lowerCamelCase , text["en"] )
_dump_articles(lowerCamelCase , text["de"] )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{str(lowerCamelCase )}
{str(lowerCamelCase )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
with CaptureStdout() as cs:
run_search()
__lowercase = [" num_beams | length_penalty", model, "Best score args"]
__lowercase = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(lowerCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(lowerCamelCase ).exists()
os.remove(Path(lowerCamelCase ) )
| 655 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ : List[str] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
snake_case__ : Dict = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Optional[Any] = """mask2former"""
_snake_case : Union[str, Any] = ["""swin"""]
_snake_case : Union[str, Any] = {"""hidden_size""": """hidden_dim"""}
def __init__( self : Tuple , lowerCamelCase : Optional[Dict] = None , lowerCamelCase : int = 256 , lowerCamelCase : int = 256 , lowerCamelCase : int = 256 , lowerCamelCase : int = 1_024 , lowerCamelCase : str = "relu" , lowerCamelCase : int = 6 , lowerCamelCase : int = 10 , lowerCamelCase : int = 8 , lowerCamelCase : float = 0.0 , lowerCamelCase : int = 2_048 , lowerCamelCase : bool = False , lowerCamelCase : bool = False , lowerCamelCase : int = 4 , lowerCamelCase : int = 255 , lowerCamelCase : int = 100 , lowerCamelCase : float = 0.1 , lowerCamelCase : float = 2.0 , lowerCamelCase : float = 5.0 , lowerCamelCase : float = 5.0 , lowerCamelCase : int = 12_544 , lowerCamelCase : float = 3.0 , lowerCamelCase : float = 0.75 , lowerCamelCase : float = 0.02 , lowerCamelCase : float = 1.0 , lowerCamelCase : bool = True , lowerCamelCase : List[int] = [4, 8, 16, 32] , lowerCamelCase : bool = None , **lowerCamelCase : List[Any] , ):
'''simple docstring'''
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
__lowercase = CONFIG_MAPPING["swin"](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=lowerCamelCase , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = backbone_config.pop("model_type" )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowerCamelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {','.join(self.backbones_supported )}""" )
__lowercase = backbone_config
__lowercase = feature_size
__lowercase = mask_feature_size
__lowercase = hidden_dim
__lowercase = encoder_feedforward_dim
__lowercase = activation_function
__lowercase = encoder_layers
__lowercase = decoder_layers
__lowercase = num_attention_heads
__lowercase = dropout
__lowercase = dim_feedforward
__lowercase = pre_norm
__lowercase = enforce_input_projection
__lowercase = common_stride
__lowercase = ignore_value
__lowercase = num_queries
__lowercase = no_object_weight
__lowercase = class_weight
__lowercase = mask_weight
__lowercase = dice_weight
__lowercase = train_num_points
__lowercase = oversample_ratio
__lowercase = importance_sample_ratio
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = use_auxiliary_loss
__lowercase = feature_strides
__lowercase = output_auxiliary_logits
__lowercase = decoder_layers
super().__init__(**lowerCamelCase )
@classmethod
def _snake_case ( cls : Optional[int] , lowerCamelCase : PretrainedConfig , **lowerCamelCase : List[Any] ):
'''simple docstring'''
return cls(
backbone_config=lowerCamelCase , **lowerCamelCase , )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
| 655 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _A :
'''simple docstring'''
_snake_case : int
_snake_case : TreeNode | None = None
_snake_case : TreeNode | None = None
snake_case__ : Dict = namedtuple("""CoinsDistribResult""", """moves excess""")
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if root is None:
return 0
# Validation
def count_nodes(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_SCREAMING_SNAKE_CASE ) != count_coins(_SCREAMING_SNAKE_CASE ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(_SCREAMING_SNAKE_CASE ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
__lowercase , __lowercase = get_distrib(node.left )
__lowercase , __lowercase = get_distrib(node.right )
__lowercase = 1 - left_distrib_excess
__lowercase = 1 - right_distrib_excess
__lowercase = (
left_distrib_moves
+ right_distrib_moves
+ abs(_SCREAMING_SNAKE_CASE )
+ abs(_SCREAMING_SNAKE_CASE )
)
__lowercase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return get_distrib(_SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _A ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any]=7 , lowerCamelCase : Dict=3 , lowerCamelCase : Dict=18 , lowerCamelCase : Dict=30 , lowerCamelCase : Optional[Any]=400 , lowerCamelCase : Dict=True , lowerCamelCase : Any=None , lowerCamelCase : Optional[Any]=True , lowerCamelCase : str=None , lowerCamelCase : Any=True , lowerCamelCase : int=[0.5, 0.5, 0.5] , lowerCamelCase : Dict=[0.5, 0.5, 0.5] , ):
'''simple docstring'''
__lowercase = size if size is not None else {"shortest_edge": 18}
__lowercase = crop_size if crop_size is not None else {"height": 18, "width": 18}
__lowercase = parent
__lowercase = batch_size
__lowercase = num_channels
__lowercase = image_size
__lowercase = min_resolution
__lowercase = max_resolution
__lowercase = do_resize
__lowercase = size
__lowercase = do_center_crop
__lowercase = crop_size
__lowercase = do_normalize
__lowercase = image_mean
__lowercase = image_std
def _snake_case ( self : Dict ):
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _A ( _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Optional[int] = LevitImageProcessor if is_vision_available() else None
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = LevitImageProcessingTester(self )
@property
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , "image_mean" ) )
self.assertTrue(hasattr(lowerCamelCase , "image_std" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_normalize" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_resize" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_center_crop" ) )
self.assertTrue(hasattr(lowerCamelCase , "size" ) )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
__lowercase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
pass
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
__lowercase = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
__lowercase = image_processing(lowerCamelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
__lowercase = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
__lowercase = image_processing(lowerCamelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
__lowercase = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
__lowercase = image_processing(lowerCamelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 655 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = SwinvaConfig()
__lowercase = swinva_name.split("_" )
__lowercase = name_split[1]
if "to" in name_split[3]:
__lowercase = int(name_split[3][-3:] )
else:
__lowercase = int(name_split[3] )
if "to" in name_split[2]:
__lowercase = int(name_split[2][-2:] )
else:
__lowercase = int(name_split[2][6:] )
if model_size == "tiny":
__lowercase = 9_6
__lowercase = (2, 2, 6, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "small":
__lowercase = 9_6
__lowercase = (2, 2, 1_8, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "base":
__lowercase = 1_2_8
__lowercase = (2, 2, 1_8, 2)
__lowercase = (4, 8, 1_6, 3_2)
else:
__lowercase = 1_9_2
__lowercase = (2, 2, 1_8, 2)
__lowercase = (6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
__lowercase = (1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__lowercase = 2_1_8_4_1
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-22k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
else:
__lowercase = 1_0_0_0
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-1k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = img_size
__lowercase = num_classes
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
return config
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if "patch_embed.proj" in name:
__lowercase = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
__lowercase = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
__lowercase = "encoder." + name
if "attn.proj" in name:
__lowercase = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
__lowercase = name.replace("attn" , "attention.self" )
if "norm1" in name:
__lowercase = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
__lowercase = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
__lowercase = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
__lowercase = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
__lowercase = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
__lowercase = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
__lowercase = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
__lowercase = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
__lowercase = "layernorm.weight"
if name == "norm.bias":
__lowercase = "layernorm.bias"
if "head" in name:
__lowercase = name.replace("head" , "classifier" )
else:
__lowercase = "swinv2." + name
return name
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for key in orig_state_dict.copy().keys():
__lowercase = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
__lowercase = key.split("." )
__lowercase = int(key_split[1] )
__lowercase = int(key_split[3] )
__lowercase = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__lowercase = val[:dim, :]
__lowercase = val[dim : dim * 2, :]
__lowercase = val[-dim:, :]
else:
__lowercase = val[:dim]
__lowercase = val[
dim : dim * 2
]
__lowercase = val[-dim:]
else:
__lowercase = val
return orig_state_dict
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
__lowercase = get_swinva_config(_SCREAMING_SNAKE_CASE )
__lowercase = SwinvaForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
__lowercase = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
__lowercase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
__lowercase = timm_model(inputs["pixel_values"] )
__lowercase = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
model.push_to_hub(
repo_path_or_name=Path(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swinv2_name""",
default="""swinv2_tiny_patch4_window8_256""",
type=str,
help="""Name of the Swinv2 timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case__ : str = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 655 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def snake_case_ ( _SCREAMING_SNAKE_CASE = 1_0_0_0_0_0_0 , _SCREAMING_SNAKE_CASE = 1_0 ):
__lowercase = defaultdict(_SCREAMING_SNAKE_CASE )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
__lowercase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
__lowercase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(_SCREAMING_SNAKE_CASE , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 1_0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 655 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
snake_case__ : Optional[Any] = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
snake_case__ : List[str] = {
"""allenai/led-base-16384""": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def snake_case_ ( ):
__lowercase = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
__lowercase = bs[:]
__lowercase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
__lowercase = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
return pairs
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[str] = VOCAB_FILES_NAMES
_snake_case : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Optional[int]="replace" , lowerCamelCase : Dict="<s>" , lowerCamelCase : Dict="</s>" , lowerCamelCase : Optional[Any]="</s>" , lowerCamelCase : Any="<s>" , lowerCamelCase : List[str]="<unk>" , lowerCamelCase : Union[str, Any]="<pad>" , lowerCamelCase : Any="<mask>" , lowerCamelCase : str=False , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else bos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else eos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else sep_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else cls_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else unk_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else mask_token
super().__init__(
errors=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , add_prefix_space=lowerCamelCase , **lowerCamelCase , )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
__lowercase = json.load(lowerCamelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = errors # how to handle errors in decoding
__lowercase = bytes_to_unicode()
__lowercase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
__lowercase = merges_handle.read().split("\n" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in bpe_merges]
__lowercase = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__lowercase = {}
__lowercase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowercase = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return len(self.encoder )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : List[Any] , lowerCamelCase : str ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowercase = tuple(lowerCamelCase )
__lowercase = get_pairs(lowerCamelCase )
if not pairs:
return token
while True:
__lowercase = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(lowerCamelCase ):
try:
__lowercase = word.index(lowerCamelCase , lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase = j
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(lowerCamelCase )
__lowercase = new_word
if len(lowerCamelCase ) == 1:
break
else:
__lowercase = get_pairs(lowerCamelCase )
__lowercase = " ".join(lowerCamelCase )
__lowercase = word
return word
def _snake_case ( self : List[Any] , lowerCamelCase : Tuple ):
'''simple docstring'''
__lowercase = []
for token in re.findall(self.pat , lowerCamelCase ):
__lowercase = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : Dict , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : str , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.decoder.get(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = "".join(lowerCamelCase )
__lowercase = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
__lowercase = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
__lowercase = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : str , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase )) + [1]
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase )) + [1]
def _snake_case ( self : int , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [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 _snake_case ( self : Dict , lowerCamelCase : Any , lowerCamelCase : Tuple=False , **lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase ) > 0 and not text[0].isspace()):
__lowercase = " " + text
return (text, kwargs)
def _snake_case ( self : List[Any] , lowerCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCamelCase : Optional[int] = None , lowerCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[bool] = None , ):
'''simple docstring'''
__lowercase = super()._pad(
encoded_inputs=lowerCamelCase , max_length=lowerCamelCase , padding_strategy=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , )
# Load from model defaults
if return_attention_mask is None:
__lowercase = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__lowercase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__lowercase = len(encoded_inputs["global_attention_mask"] ) != len(lowerCamelCase )
if needs_to_be_padded:
__lowercase = len(lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__lowercase = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
__lowercase = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 655 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zero." )
if any(p < 0 for p in profit ):
raise ValueError("Profit can not be negative." )
if any(w < 0 for w in weight ):
raise ValueError("Weight can not be negative." )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
__lowercase = [p / w for p, w in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
# Creating a copy of the list and sorting profit/weight in ascending order
__lowercase = sorted(_SCREAMING_SNAKE_CASE )
# declaring useful variables
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = 0
__lowercase = 0
__lowercase = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
__lowercase = sorted_profit_by_weight[length - i - 1]
__lowercase = profit_by_weight.index(_SCREAMING_SNAKE_CASE )
__lowercase = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"""Input profits, weights, and then max_weight (all positive ints) separated by """
"""spaces."""
)
snake_case__ : str = [int(x) for x in input("""Input profits separated by spaces: """).split()]
snake_case__ : str = [int(x) for x in input("""Input weights separated by spaces: """).split()]
snake_case__ : Optional[Any] = int(input("""Max weight allowed: """))
# Function Call
calc_profit(profit, weight, max_weight)
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return round(float(moles / volume ) * nfactor )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return round(float((moles * 0.0_8_2_1 * temperature) / (volume) ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return round(float((moles * 0.0_8_2_1 * temperature) / (pressure) ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return round(float((pressure * volume) / (0.0_8_2_1 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
# 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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """openai/whisper-base"""
_snake_case : Union[str, Any] = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
_snake_case : Any = """transcriber"""
_snake_case : Any = WhisperProcessor
_snake_case : Optional[int] = WhisperForConditionalGeneration
_snake_case : str = ["""audio"""]
_snake_case : Optional[int] = ["""text"""]
def _snake_case ( self : List[str] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.pre_processor(lowerCamelCase , return_tensors="pt" ).input_features
def _snake_case ( self : str , lowerCamelCase : List[Any] ):
'''simple docstring'''
return self.model.generate(inputs=lowerCamelCase )
def _snake_case ( self : List[str] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(lowerCamelCase , skip_special_tokens=lowerCamelCase )[0]
| 655 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
snake_case__ : List[Any] = {
"""E""": 1_2.7_0,
"""T""": 9.0_6,
"""A""": 8.1_7,
"""O""": 7.5_1,
"""I""": 6.9_7,
"""N""": 6.7_5,
"""S""": 6.3_3,
"""H""": 6.0_9,
"""R""": 5.9_9,
"""D""": 4.2_5,
"""L""": 4.0_3,
"""C""": 2.7_8,
"""U""": 2.7_6,
"""M""": 2.4_1,
"""W""": 2.3_6,
"""F""": 2.2_3,
"""G""": 2.0_2,
"""Y""": 1.9_7,
"""P""": 1.9_3,
"""B""": 1.2_9,
"""V""": 0.9_8,
"""K""": 0.7_7,
"""J""": 0.1_5,
"""X""": 0.1_5,
"""Q""": 0.1_0,
"""Z""": 0.0_7,
}
snake_case__ : str = """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
snake_case__ : Optional[int] = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
return x[0]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = get_letter_count(_SCREAMING_SNAKE_CASE )
__lowercase = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(_SCREAMING_SNAKE_CASE )
__lowercase = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=_SCREAMING_SNAKE_CASE )
__lowercase = "".join(freq_to_letter[freq] )
__lowercase = list(freq_to_letter_str.items() )
freq_pairs.sort(key=_SCREAMING_SNAKE_CASE , reverse=_SCREAMING_SNAKE_CASE )
__lowercase = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = get_frequency_order(_SCREAMING_SNAKE_CASE )
__lowercase = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _A :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["prompt"]
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
if "image" in inputs:
__lowercase = inputs["image"]
else:
__lowercase = None
if "mask_image" in inputs:
__lowercase = inputs["mask_image"]
else:
__lowercase = None
if "original_image" in inputs:
__lowercase = inputs["original_image"]
else:
__lowercase = None
__lowercase , __lowercase = pipe.encode_prompt(lowerCamelCase )
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase , lowerCamelCase ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
| 655 | 1 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[str] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
snake_case__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import numpy as np
snake_case__ : Tuple = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class _A :
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase , __lowercase = np.where(letter == self.SQUARE )
__lowercase = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def _snake_case ( self : List[Any] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = self.SQUARE[indexa - 1, indexa - 1]
return letter
def _snake_case ( self : int , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
__lowercase = message.replace(" " , "" )
__lowercase = message.replace("j" , "i" )
__lowercase = np.empty((2, len(lowerCamelCase )) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape(2 * len(lowerCamelCase ) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[numbers_index * 2] )
__lowercase = int(second_step[(numbers_index * 2) + 1] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = encoded_message + letter
return encoded_message
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
message.replace(" " , "" )
__lowercase = np.empty(2 * len(lowerCamelCase ) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape((2, len(lowerCamelCase )) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[0, numbers_index] )
__lowercase = int(second_step[1, numbers_index] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = decoded_message + letter
return decoded_message
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Dict = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[str] = [
"""LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LukeForEntityClassification""",
"""LukeForEntityPairClassification""",
"""LukeForEntitySpanClassification""",
"""LukeForMultipleChoice""",
"""LukeForQuestionAnswering""",
"""LukeForSequenceClassification""",
"""LukeForTokenClassification""",
"""LukeForMaskedLM""",
"""LukeModel""",
"""LukePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
snake_case__ : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
'''simple docstring'''
_snake_case : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def snake_case_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 655 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 655 |
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__ : List[Any] = 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 ( _lowercase ):
'''simple docstring'''
_snake_case : List[Any] = """yolos"""
def __init__( self : Union[str, Any] , lowerCamelCase : Union[str, Any]=768 , lowerCamelCase : int=12 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Optional[Any]=3_072 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Dict=0.0 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Any=0.02 , lowerCamelCase : Optional[Any]=1e-12 , lowerCamelCase : Optional[Any]=[512, 864] , lowerCamelCase : str=16 , lowerCamelCase : Dict=3 , lowerCamelCase : str=True , lowerCamelCase : List[Any]=100 , lowerCamelCase : Dict=True , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=1 , lowerCamelCase : str=5 , lowerCamelCase : Any=2 , lowerCamelCase : str=5 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : List[Any]=0.1 , **lowerCamelCase : List[Any] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = num_detection_tokens
__lowercase = use_mid_position_embeddings
__lowercase = auxiliary_loss
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = version.parse("""1.11""" )
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : str ):
'''simple docstring'''
return 1e-4
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return 12
| 655 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class _A :
'''simple docstring'''
_snake_case : torch.Tensor # [batch_size x 3]
_snake_case : torch.Tensor # [batch_size x 3]
_snake_case : torch.Tensor # [batch_size x 3]
_snake_case : torch.Tensor # [batch_size x 3]
_snake_case : int
_snake_case : int
_snake_case : float
_snake_case : float
_snake_case : Tuple[int]
def _snake_case ( self : List[str] ):
'''simple docstring'''
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def _snake_case ( self : Any ):
'''simple docstring'''
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = torch.arange(self.height * self.width )
__lowercase = torch.stack(
[
pixel_indices % self.width,
torch.div(lowerCamelCase , self.width , rounding_mode="trunc" ),
] , axis=1 , )
return coords
@property
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase , *__lowercase = self.shape
__lowercase = int(np.prod(lowerCamelCase ) )
__lowercase = self.get_image_coords()
__lowercase = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
__lowercase = self.get_camera_rays(lowerCamelCase )
__lowercase = rays.view(lowerCamelCase , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def _snake_case ( self : Optional[int] , lowerCamelCase : torch.Tensor ):
'''simple docstring'''
__lowercase , *__lowercase , __lowercase = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
__lowercase = coords.view(lowerCamelCase , -1 , 2 )
__lowercase = self.resolution()
__lowercase = self.fov()
__lowercase = (flat.float() / (res - 1)) * 2 - 1
__lowercase = fracs * torch.tan(fov / 2 )
__lowercase = fracs.view(lowerCamelCase , -1 , 2 )
__lowercase = (
self.z.view(lowerCamelCase , 1 , 3 )
+ self.x.view(lowerCamelCase , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(lowerCamelCase , 1 , 3 ) * fracs[:, :, 1:]
)
__lowercase = directions / directions.norm(dim=-1 , keepdim=lowerCamelCase )
__lowercase = torch.stack(
[
torch.broadcast_to(self.origin.view(lowerCamelCase , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(lowerCamelCase , *lowerCamelCase , 2 , 3 )
def _snake_case ( self : Any , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=lowerCamelCase , height=lowerCamelCase , x_fov=self.x_fov , y_fov=self.y_fov , )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
__lowercase = []
__lowercase = []
__lowercase = []
for theta in np.linspace(0 , 2 * np.pi , num=2_0 ):
__lowercase = np.array([np.sin(_SCREAMING_SNAKE_CASE ), np.cos(_SCREAMING_SNAKE_CASE ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
__lowercase = -z * 4
__lowercase = np.array([np.cos(_SCREAMING_SNAKE_CASE ), -np.sin(_SCREAMING_SNAKE_CASE ), 0.0] )
__lowercase = np.cross(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
origins.append(_SCREAMING_SNAKE_CASE )
xs.append(_SCREAMING_SNAKE_CASE )
ys.append(_SCREAMING_SNAKE_CASE )
zs.append(_SCREAMING_SNAKE_CASE )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE , axis=0 ) ).float() , x=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE , axis=0 ) ).float() , y=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE , axis=0 ) ).float() , z=torch.from_numpy(np.stack(_SCREAMING_SNAKE_CASE , axis=0 ) ).float() , width=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , x_fov=0.7 , y_fov=0.7 , shape=(1, len(_SCREAMING_SNAKE_CASE )) , )
| 655 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Optional[int] = logging.get_logger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = MobileNetVaConfig(layer_norm_eps=0.0_0_1 )
if "_quant" in model_name:
raise ValueError("Quantized models are not supported." )
__lowercase = re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" , _SCREAMING_SNAKE_CASE )
if matches:
__lowercase = float(matches[1] )
__lowercase = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
__lowercase = 1_0_0_1
__lowercase = "imagenet-1k-id2label.json"
__lowercase = "huggingface/label-files"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ) + 1: v for k, v in idalabel.items()}
__lowercase = "background"
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ( ):
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = get_mobilenet_va_config(_SCREAMING_SNAKE_CASE )
# Load 🤗 model
__lowercase = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
__lowercase = MobileNetVaImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size} , size={"shortest_edge": config.image_size + 3_2} , )
__lowercase = image_processor(images=prepare_img() , return_tensors="pt" )
__lowercase = model(**_SCREAMING_SNAKE_CASE )
__lowercase = outputs.logits
assert logits.shape == (1, 1_0_0_1)
if model_name == "mobilenet_v1_1.0_224":
__lowercase = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] )
elif model_name == "mobilenet_v1_0.75_192":
__lowercase = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3] )
else:
__lowercase = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("Pushing to the hub..." )
__lowercase = "google/" + model_name
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""mobilenet_v1_1.0_224""",
type=str,
help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt 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."""
)
snake_case__ : Dict = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 655 | 1 |
from __future__ import annotations
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = len(_SCREAMING_SNAKE_CASE )
# We need to create solution object to save path.
__lowercase = [[0 for _ in range(_SCREAMING_SNAKE_CASE )] for _ in range(_SCREAMING_SNAKE_CASE )]
__lowercase = run_maze(_SCREAMING_SNAKE_CASE , 0 , 0 , _SCREAMING_SNAKE_CASE )
if solved:
print("\n".join(str(_SCREAMING_SNAKE_CASE ) for row in solutions ) )
else:
print("No solution exists!" )
return solved
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = len(_SCREAMING_SNAKE_CASE )
# Final check point.
if i == j == (size - 1):
__lowercase = 1
return True
__lowercase = (not i < 0) and (not j < 0) # Check lower bounds
__lowercase = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
__lowercase = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
__lowercase = 1
# check for directions
if (
run_maze(_SCREAMING_SNAKE_CASE , i + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
or run_maze(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , j + 1 , _SCREAMING_SNAKE_CASE )
or run_maze(_SCREAMING_SNAKE_CASE , i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
or run_maze(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , j - 1 , _SCREAMING_SNAKE_CASE )
):
return True
__lowercase = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
from __future__ import annotations
from typing import Any
class _A :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = num_of_nodes
__lowercase = []
__lowercase = {}
def _snake_case ( self : Dict , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def _snake_case ( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
__lowercase = self.find_component(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : list[int] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
__lowercase = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__lowercase = self.find_component(lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCamelCase )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = []
__lowercase = 0
__lowercase = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__lowercase = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__lowercase = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
__lowercase = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def snake_case_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from sklearn.metrics import recall_score
import datasets
snake_case__ : Optional[int] = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""
snake_case__ : int = """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{'recall': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric('recall')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{'recall': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric('recall')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{'recall': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric('recall')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
>>> print(results)
{'recall': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'recall': array([1., 0., 0.])}
"""
snake_case__ : int = """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _A ( datasets.Metric ):
'''simple docstring'''
def _snake_case ( self : Tuple ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"] , )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Tuple , lowerCamelCase : List[Any] , lowerCamelCase : Optional[int]=None , lowerCamelCase : List[Any]=1 , lowerCamelCase : List[str]="binary" , lowerCamelCase : Tuple=None , lowerCamelCase : Tuple="warn" , ):
'''simple docstring'''
__lowercase = recall_score(
lowerCamelCase , lowerCamelCase , labels=lowerCamelCase , pos_label=lowerCamelCase , average=lowerCamelCase , sample_weight=lowerCamelCase , zero_division=lowerCamelCase , )
return {"recall": float(lowerCamelCase ) if score.size == 1 else score}
| 655 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[str] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
snake_case__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _A ( _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : List[str] = RoCBertTokenizer
_snake_case : Optional[Any] = None
_snake_case : int = False
_snake_case : Optional[int] = True
_snake_case : Optional[Any] = filter_non_english
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
super().setUp()
__lowercase = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
__lowercase = {}
__lowercase = {}
for i, value in enumerate(lowerCamelCase ):
__lowercase = i
__lowercase = i
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_shape_file"] )
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["word_pronunciation_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
with open(self.word_shape_file , "w" , encoding="utf-8" ) as word_shape_writer:
json.dump(lowerCamelCase , lowerCamelCase , ensure_ascii=lowerCamelCase )
with open(self.word_pronunciation_file , "w" , encoding="utf-8" ) as word_pronunciation_writer:
json.dump(lowerCamelCase , lowerCamelCase , ensure_ascii=lowerCamelCase )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__lowercase = tokenizer.tokenize("你好[SEP]你是谁" )
self.assertListEqual(lowerCamelCase , ["你", "好", "[SEP]", "你", "是", "谁"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] )
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
__lowercase = {}
for i, token in enumerate(lowerCamelCase ):
__lowercase = i
__lowercase = RoCBertWordpieceTokenizer(vocab=lowerCamelCase , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def _snake_case ( self : int ):
'''simple docstring'''
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def _snake_case ( self : Tuple ):
'''simple docstring'''
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def _snake_case ( self : Any ):
'''simple docstring'''
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCamelCase ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
if self.test_rust_tokenizer:
__lowercase = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCamelCase ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__lowercase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
__lowercase = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
__lowercase = tokenizer_r.encode_plus(
lowerCamelCase , return_attention_mask=lowerCamelCase , return_token_type_ids=lowerCamelCase , return_offsets_mapping=lowerCamelCase , add_special_tokens=lowerCamelCase , )
__lowercase = tokenizer_r.do_lower_case if hasattr(lowerCamelCase , "do_lower_case" ) else False
__lowercase = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = ["的", "人", "有"]
__lowercase = "".join(lowerCamelCase )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__lowercase = True
__lowercase = self.tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
__lowercase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
__lowercase = tokenizer_p.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
__lowercase = tokenizer_r.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
__lowercase = tokenizer_r.convert_ids_to_tokens(lowerCamelCase )
__lowercase = tokenizer_p.convert_ids_to_tokens(lowerCamelCase )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCamelCase , lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
__lowercase = False
__lowercase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
__lowercase = self.tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase )
__lowercase = tokenizer_r.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
__lowercase = tokenizer_p.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
__lowercase = tokenizer_r.convert_ids_to_tokens(lowerCamelCase )
__lowercase = tokenizer_p.convert_ids_to_tokens(lowerCamelCase )
# it is expected that only the first Chinese character is not preceded by "##".
__lowercase = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowerCamelCase )
]
self.assertListEqual(lowerCamelCase , lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
@slow
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__lowercase = tokenizer.encode("你好" , add_special_tokens=lowerCamelCase )
__lowercase = tokenizer.encode("你是谁" , add_special_tokens=lowerCamelCase )
__lowercase = tokenizer.build_inputs_with_special_tokens(lowerCamelCase )
__lowercase = tokenizer.build_inputs_with_special_tokens(lowerCamelCase , lowerCamelCase )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = self.get_tokenizers(do_lower_case=lowerCamelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
__lowercase = "你好，你是谁"
__lowercase = tokenizer.tokenize(lowerCamelCase )
__lowercase = tokenizer.convert_tokens_to_ids(lowerCamelCase )
__lowercase = tokenizer.convert_tokens_to_shape_ids(lowerCamelCase )
__lowercase = tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase )
__lowercase = tokenizer.prepare_for_model(
lowerCamelCase , lowerCamelCase , lowerCamelCase , add_special_tokens=lowerCamelCase )
__lowercase = tokenizer.encode_plus(lowerCamelCase , add_special_tokens=lowerCamelCase )
self.assertEqual(lowerCamelCase , lowerCamelCase )
| 655 |
from __future__ import annotations
import bisect
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = 0
__lowercase = len(_SCREAMING_SNAKE_CASE ) - 1
while left <= right:
__lowercase = left + (right - left) // 2
__lowercase = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__lowercase = midpoint - 1
else:
__lowercase = midpoint + 1
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = bisect.bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
return index
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if right < left:
return None
__lowercase = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint - 1 )
else:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint + 1 , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Optional[Any] = input("""Enter numbers separated by comma:\n""").strip()
snake_case__ : Any = sorted(int(item) for item in user_input.split(""","""))
snake_case__ : Any = int(input("""Enter a single number to be found in the list:\n"""))
snake_case__ : List[Any] = binary_search(collection, target)
if result is None:
print(F'''{target} was not found in {collection}.''')
else:
print(F'''{target} was found at position {result} in {collection}.''')
| 655 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
snake_case__ : Optional[Any] = logging.getLogger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : str = """token-classification"""
def __init__( self : Optional[Any] , lowerCamelCase : Dict ):
'''simple docstring'''
if type(lowerCamelCase ) == dict:
__lowercase = Namespace(**lowerCamelCase )
__lowercase = import_module("tasks" )
try:
__lowercase = getattr(lowerCamelCase , hparams.task_type )
__lowercase = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
__lowercase = self.token_classification_task.get_labels(hparams.labels )
__lowercase = CrossEntropyLoss().ignore_index
super().__init__(lowerCamelCase , len(self.labels ) , self.mode )
def _snake_case ( self : Union[str, Any] , **lowerCamelCase : str ):
'''simple docstring'''
return self.model(**lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Any , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowercase = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type != "distilbert":
__lowercase = (
batch[2] if self.config.model_type in ["bert", "xlnet"] else None
) # XLM and RoBERTa don"t use token_type_ids
__lowercase = self(**lowerCamelCase )
__lowercase = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.hparams
for mode in ["train", "dev", "test"]:
__lowercase = self._feature_file(lowerCamelCase )
if os.path.exists(lowerCamelCase ) and not args.overwrite_cache:
logger.info("Loading features from cached file %s" , lowerCamelCase )
__lowercase = torch.load(lowerCamelCase )
else:
logger.info("Creating features from dataset file at %s" , args.data_dir )
__lowercase = self.token_classification_task.read_examples_from_file(args.data_dir , lowerCamelCase )
__lowercase = self.token_classification_task.convert_examples_to_features(
lowerCamelCase , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["xlnet"] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=lowerCamelCase , pad_on_left=bool(self.config.model_type in ["xlnet"] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("Saving features into cached file %s" , lowerCamelCase )
torch.save(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : bool = False ):
'''simple docstring'''
__lowercase = self._feature_file(lowerCamelCase )
logger.info("Loading features from cached file %s" , lowerCamelCase )
__lowercase = torch.load(lowerCamelCase )
__lowercase = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
__lowercase = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
__lowercase = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
__lowercase = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
__lowercase = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) , batch_size=lowerCamelCase )
def _snake_case ( self : List[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Any ):
'''simple docstring'''
"""Compute validation""" ""
__lowercase = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if self.config.model_type != "distilbert":
__lowercase = (
batch[2] if self.config.model_type in ["bert", "xlnet"] else None
) # XLM and RoBERTa don"t use token_type_ids
__lowercase = self(**lowerCamelCase )
__lowercase , __lowercase = outputs[:2]
__lowercase = logits.detach().cpu().numpy()
__lowercase = inputs["labels"].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _snake_case ( self : Tuple , lowerCamelCase : List[Any] ):
'''simple docstring'''
__lowercase = torch.stack([x["val_loss"] for x in outputs] ).mean()
__lowercase = np.concatenate([x["pred"] for x in outputs] , axis=0 )
__lowercase = np.argmax(lowerCamelCase , axis=2 )
__lowercase = np.concatenate([x["target"] for x in outputs] , axis=0 )
__lowercase = dict(enumerate(self.labels ) )
__lowercase = [[] for _ in range(out_label_ids.shape[0] )]
__lowercase = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
__lowercase = {
"val_loss": val_loss_mean,
"accuracy_score": accuracy_score(lowerCamelCase , lowerCamelCase ),
"precision": precision_score(lowerCamelCase , lowerCamelCase ),
"recall": recall_score(lowerCamelCase , lowerCamelCase ),
"f1": fa_score(lowerCamelCase , lowerCamelCase ),
}
__lowercase = dict(results.items() )
__lowercase = results
return ret, preds_list, out_label_list
def _snake_case ( self : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
__lowercase , __lowercase , __lowercase = self._eval_end(lowerCamelCase )
__lowercase = ret["log"]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _snake_case ( self : Optional[Any] , lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
__lowercase , __lowercase , __lowercase = self._eval_end(lowerCamelCase )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
__lowercase = ret["log"]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _snake_case ( lowerCamelCase : List[Any] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
BaseTransformer.add_model_specific_args(lowerCamelCase , lowerCamelCase )
parser.add_argument(
"--task_type" , default="NER" , type=lowerCamelCase , help="Task type to fine tune in training (e.g. NER, POS, etc)" )
parser.add_argument(
"--max_seq_length" , default=128 , type=lowerCamelCase , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--labels" , default="" , type=lowerCamelCase , help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used." , )
parser.add_argument(
"--gpus" , default=0 , type=lowerCamelCase , help="The number of GPUs allocated for this, it is by default 0 meaning none" , )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
return parser
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
snake_case__ : int = NERTransformer.add_model_specific_args(parser, os.getcwd())
snake_case__ : int = parser.parse_args()
snake_case__ : Dict = NERTransformer(args)
snake_case__ : List[Any] = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
snake_case__ : Optional[int] = sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
snake_case__ : Dict = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 655 |
import copy
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
from ..auto import CONFIG_MAPPING
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """conditional_detr"""
_snake_case : Union[str, Any] = ["""past_key_values"""]
_snake_case : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Optional[Any] , lowerCamelCase : int=True , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[int]=3 , lowerCamelCase : Optional[int]=300 , lowerCamelCase : List[Any]=6 , lowerCamelCase : str=2_048 , lowerCamelCase : Any=8 , lowerCamelCase : List[str]=6 , lowerCamelCase : Any=2_048 , lowerCamelCase : List[Any]=8 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : List[str]=0.0 , lowerCamelCase : List[Any]=True , lowerCamelCase : str="relu" , lowerCamelCase : int=256 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Dict=0.0 , lowerCamelCase : Tuple=0.02 , lowerCamelCase : int=1.0 , lowerCamelCase : Tuple=False , lowerCamelCase : List[str]="sine" , lowerCamelCase : List[Any]="resnet50" , lowerCamelCase : Any=True , lowerCamelCase : Any=False , lowerCamelCase : List[Any]=2 , lowerCamelCase : List[Any]=5 , lowerCamelCase : str=2 , lowerCamelCase : Dict=1 , lowerCamelCase : List[str]=1 , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Dict=5 , lowerCamelCase : List[Any]=2 , lowerCamelCase : Tuple=0.25 , **lowerCamelCase : List[str] , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
__lowercase = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = backbone_config.get("model_type" )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowerCamelCase )
__lowercase = use_timm_backbone
__lowercase = backbone_config
__lowercase = num_channels
__lowercase = num_queries
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = encoder_layers
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
__lowercase = backbone
__lowercase = use_pretrained_backbone
__lowercase = dilation
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = cls_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase , **lowerCamelCase )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _snake_case ( self : str ):
'''simple docstring'''
return self.d_model
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Any = version.parse("""1.11""" )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _snake_case ( self : Any ):
'''simple docstring'''
return 1e-5
@property
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
return 12
| 655 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[str] = """AutoTokenizer"""
_snake_case : List[Any] = ["""tokenizer"""]
_snake_case : Optional[Any] = {
"""semantic_prompt""": 1,
"""coarse_prompt""": 2,
"""fine_prompt""": 2,
}
def __init__( self : Tuple , lowerCamelCase : List[str] , lowerCamelCase : str=None ):
'''simple docstring'''
super().__init__(lowerCamelCase )
__lowercase = speaker_embeddings
@classmethod
def _snake_case ( cls : Optional[Any] , lowerCamelCase : Tuple , lowerCamelCase : List[Any]="speaker_embeddings_path.json" , **lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
__lowercase = get_file_from_repo(
lowerCamelCase , lowerCamelCase , subfolder=kwargs.pop("subfolder" , lowerCamelCase ) , cache_dir=kwargs.pop("cache_dir" , lowerCamelCase ) , force_download=kwargs.pop("force_download" , lowerCamelCase ) , proxies=kwargs.pop("proxies" , lowerCamelCase ) , resume_download=kwargs.pop("resume_download" , lowerCamelCase ) , local_files_only=kwargs.pop("local_files_only" , lowerCamelCase ) , use_auth_token=kwargs.pop("use_auth_token" , lowerCamelCase ) , revision=kwargs.pop("revision" , lowerCamelCase ) , )
if speaker_embeddings_path is None:
logger.warning(
f"""`{os.path.join(lowerCamelCase , lowerCamelCase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.""" )
__lowercase = None
else:
with open(lowerCamelCase ) as speaker_embeddings_json:
__lowercase = json.load(lowerCamelCase )
else:
__lowercase = None
__lowercase = AutoTokenizer.from_pretrained(lowerCamelCase , **lowerCamelCase )
return cls(tokenizer=lowerCamelCase , speaker_embeddings=lowerCamelCase )
def _snake_case ( self : Optional[int] , lowerCamelCase : Tuple , lowerCamelCase : str="speaker_embeddings_path.json" , lowerCamelCase : Optional[int]="speaker_embeddings" , lowerCamelCase : bool = False , **lowerCamelCase : Optional[int] , ):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(lowerCamelCase , lowerCamelCase , "v2" ) , exist_ok=lowerCamelCase )
__lowercase = {}
__lowercase = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
__lowercase = self._load_voice_preset(lowerCamelCase )
__lowercase = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , lowerCamelCase , f"""{prompt_key}_{key}""" ) , voice_preset[key] , allow_pickle=lowerCamelCase , )
__lowercase = os.path.join(lowerCamelCase , f"""{prompt_key}_{key}.npy""" )
__lowercase = tmp_dict
with open(os.path.join(lowerCamelCase , lowerCamelCase ) , "w" ) as fp:
json.dump(lowerCamelCase , lowerCamelCase )
super().save_pretrained(lowerCamelCase , lowerCamelCase , **lowerCamelCase )
def _snake_case ( self : Any , lowerCamelCase : str = None , **lowerCamelCase : str ):
'''simple docstring'''
__lowercase = self.speaker_embeddings[voice_preset]
__lowercase = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].""" )
__lowercase = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , lowerCamelCase ) , cache_dir=kwargs.pop("cache_dir" , lowerCamelCase ) , force_download=kwargs.pop("force_download" , lowerCamelCase ) , proxies=kwargs.pop("proxies" , lowerCamelCase ) , resume_download=kwargs.pop("resume_download" , lowerCamelCase ) , local_files_only=kwargs.pop("local_files_only" , lowerCamelCase ) , use_auth_token=kwargs.pop("use_auth_token" , lowerCamelCase ) , revision=kwargs.pop("revision" , lowerCamelCase ) , )
if path is None:
raise ValueError(
f"""`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.""" )
__lowercase = np.load(lowerCamelCase )
return voice_preset_dict
def _snake_case ( self : Dict , lowerCamelCase : Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f"""Voice preset unrecognized, missing {key} as a key.""" )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
def __call__( self : Optional[Any] , lowerCamelCase : int=None , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[Any]="pt" , lowerCamelCase : Optional[int]=256 , lowerCamelCase : Any=False , lowerCamelCase : List[Any]=True , lowerCamelCase : str=False , **lowerCamelCase : Tuple , ):
'''simple docstring'''
if voice_preset is not None and not isinstance(lowerCamelCase , lowerCamelCase ):
if (
isinstance(lowerCamelCase , lowerCamelCase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
__lowercase = self._load_voice_preset(lowerCamelCase )
else:
if isinstance(lowerCamelCase , lowerCamelCase ) and not voice_preset.endswith(".npz" ):
__lowercase = voice_preset + ".npz"
__lowercase = np.load(lowerCamelCase )
if voice_preset is not None:
self._validate_voice_preset_dict(lowerCamelCase , **lowerCamelCase )
__lowercase = BatchFeature(data=lowerCamelCase , tensor_type=lowerCamelCase )
__lowercase = self.tokenizer(
lowerCamelCase , return_tensors=lowerCamelCase , padding="max_length" , max_length=lowerCamelCase , return_attention_mask=lowerCamelCase , return_token_type_ids=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
if voice_preset is not None:
__lowercase = voice_preset
return encoded_text
| 655 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Any = logging.get_logger(__name__)
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
_snake_case : Dict = """maskformer-swin"""
_snake_case : List[str] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : List[str] , lowerCamelCase : Any=224 , lowerCamelCase : Optional[Any]=4 , lowerCamelCase : Dict=3 , lowerCamelCase : Tuple=96 , lowerCamelCase : str=[2, 2, 6, 2] , lowerCamelCase : Dict=[3, 6, 12, 24] , lowerCamelCase : Optional[Any]=7 , lowerCamelCase : Any=4.0 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[str]=0.0 , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : int="gelu" , lowerCamelCase : Optional[int]=False , lowerCamelCase : List[Any]=0.02 , lowerCamelCase : Tuple=1e-5 , lowerCamelCase : Dict=None , lowerCamelCase : Dict=None , **lowerCamelCase : int , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(lowerCamelCase )
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = layer_norm_eps
__lowercase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowercase = int(embed_dim * 2 ** (len(lowerCamelCase ) - 1) )
__lowercase = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(lowerCamelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase , out_indices=lowerCamelCase , stage_names=self.stage_names )
| 655 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
'''simple docstring'''
_snake_case : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def snake_case_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__lowercase = gray_code_sequence_string(_SCREAMING_SNAKE_CASE )
#
# convert them to integers
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = int(sequence[i] , 2 )
return sequence
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__lowercase = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__lowercase = gray_code_sequence_string(bit_count - 1 )
__lowercase = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__lowercase = "0" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__lowercase = "1" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
snake_case__ : List[Any] = logging.getLogger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : str , lowerCamelCase : Optional[int] , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple=None ):
'''simple docstring'''
super().__init__(
lowerCamelCase , question_encoder_tokenizer=lowerCamelCase , generator_tokenizer=lowerCamelCase , index=lowerCamelCase , init_retrieval=lowerCamelCase , )
__lowercase = None
def _snake_case ( self : Optional[Any] , lowerCamelCase : int ):
'''simple docstring'''
logger.info("initializing retrieval" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("dist initialized" )
# needs to be set manually
__lowercase = self._infer_socket_ifname()
# avoid clash with the NCCL port
__lowercase = str(distributed_port + 1 )
__lowercase = dist.new_group(ranks=lowerCamelCase , backend="gloo" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("dist not initialized / main" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _snake_case ( self : Dict ):
'''simple docstring'''
return dist.get_rank(group=self.process_group ) == 0
def _snake_case ( self : int , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : List[Any]=torch.floataa ):
'''simple docstring'''
__lowercase = torch.empty(lowerCamelCase , dtype=lowerCamelCase )
dist.scatter(lowerCamelCase , src=0 , scatter_list=lowerCamelCase , group=self.process_group )
return target_tensor
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__lowercase = next((addr for addr in addrs if addr.startswith("e" )) , lowerCamelCase )
return ifname
def _snake_case ( self : List[str] , lowerCamelCase : np.ndarray , lowerCamelCase : int ):
'''simple docstring'''
if not dist.is_initialized():
__lowercase , __lowercase = self._main_retrieve(lowerCamelCase , lowerCamelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase )
# distributed training
__lowercase = dist.get_world_size(group=self.process_group )
# gather logic
__lowercase = None
if self._is_main():
__lowercase = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(lowerCamelCase )]
dist.gather(torch.tensor(lowerCamelCase ) , dst=0 , gather_list=lowerCamelCase , group=self.process_group )
# scatter logic
__lowercase = question_hidden_states.shape[0]
__lowercase = []
__lowercase = []
if self._is_main():
assert len(lowerCamelCase ) == world_size
__lowercase , __lowercase = self._main_retrieve(torch.cat(lowerCamelCase ).numpy() , lowerCamelCase )
__lowercase , __lowercase = torch.tensor(lowerCamelCase ), torch.tensor(lowerCamelCase )
__lowercase = self._chunk_tensor(lowerCamelCase , lowerCamelCase )
__lowercase = self._chunk_tensor(lowerCamelCase , lowerCamelCase )
__lowercase = self._scattered(lowerCamelCase , [n_queries, n_docs] , target_type=torch.intaa )
__lowercase = self._scattered(lowerCamelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(lowerCamelCase )
| 655 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=True ):
model.train()
__lowercase = model(_SCREAMING_SNAKE_CASE )
__lowercase = F.mse_loss(_SCREAMING_SNAKE_CASE , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
set_seed(4_2 )
__lowercase = RegressionModel()
__lowercase = deepcopy(_SCREAMING_SNAKE_CASE )
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
model.to(accelerator.device )
if sched:
__lowercase = AdamW(params=model.parameters() , lr=1E-3 )
__lowercase = AdamW(params=ddp_model.parameters() , lr=1E-3 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
# Make a copy of `model`
if sched:
__lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test when on a single CPU or GPU that the context manager does nothing
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test on distributed setup that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(_SCREAMING_SNAKE_CASE ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
GradientState._reset_state()
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
__lowercase = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE ))
if accelerator.num_processes > 1:
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
GradientState._reset_state()
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase = RegressionDataset(length=9_6 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if iteration < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if batch_num < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = accelerator.state
if state.local_process_index == 0:
print("**Test `accumulate` gradient accumulation with dataloader break**" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("**Test NOOP `no_sync` context manager**" )
test_noop_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("**Test Distributed `no_sync` context manager**" )
test_distributed_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("<" , "2.0" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 | 1 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def snake_case_ ( _SCREAMING_SNAKE_CASE="" ):
__lowercase = tempfile.mkdtemp()
return os.path.join(_SCREAMING_SNAKE_CASE , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = torch.rand(12 , dtype=torch.floataa ) - 0.5
__lowercase = AgentAudio(lowerCamelCase )
__lowercase = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowerCamelCase , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(lowerCamelCase ) )
# Ensure that the file contains the same value as the original tensor
__lowercase , __lowercase = sf.read(lowerCamelCase )
self.assertTrue(torch.allclose(lowerCamelCase , torch.tensor(lowerCamelCase ) , atol=1e-4 ) )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = torch.rand(12 , dtype=torch.floataa ) - 0.5
__lowercase = get_new_path(suffix=".wav" )
sf.write(lowerCamelCase , lowerCamelCase , 16_000 )
__lowercase = AgentAudio(lowerCamelCase )
self.assertTrue(torch.allclose(lowerCamelCase , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , lowerCamelCase )
@require_vision
@require_torch
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = torch.randint(0 , 256 , (64, 64, 3) )
__lowercase = AgentImage(lowerCamelCase )
__lowercase = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(lowerCamelCase , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCamelCase ) )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
__lowercase = Image.open(lowerCamelCase )
__lowercase = AgentImage(lowerCamelCase )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCamelCase ) )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
__lowercase = Image.open(lowerCamelCase )
__lowercase = AgentImage(lowerCamelCase )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(lowerCamelCase ) )
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = "Hey!"
__lowercase = AgentText(lowerCamelCase )
self.assertEqual(lowerCamelCase , agent_type.to_string() )
self.assertEqual(lowerCamelCase , agent_type.to_raw() )
self.assertEqual(lowerCamelCase , lowerCamelCase )
| 655 |
from ....utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : List[str] , lowerCamelCase : Any , lowerCamelCase : Dict=None , lowerCamelCase : Dict=2_048 ):
'''simple docstring'''
__lowercase = config.__dict__
__lowercase = modal_hidden_size
if num_labels:
__lowercase = num_labels
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return 1 if input_a == input_a else 0
def snake_case_ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 655 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class _A ( _lowercase , _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Dict = StableUnCLIPImgaImgPipeline
_snake_case : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
_snake_case : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_snake_case : int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_snake_case : int = frozenset([] )
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = 32
__lowercase = embedder_hidden_size
# image encoding components
__lowercase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
__lowercase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowerCamelCase , projection_dim=lowerCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__lowercase = StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase )
__lowercase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowercase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowercase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCamelCase , layers_per_block=1 , upcast_attention=lowerCamelCase , use_linear_projection=lowerCamelCase , )
torch.manual_seed(0 )
__lowercase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=lowerCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
__lowercase = AutoencoderKL()
__lowercase = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def _snake_case ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : Any=0 , lowerCamelCase : Union[str, Any]=True ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
__lowercase = torch.manual_seed(lowerCamelCase )
else:
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
if pil_image:
__lowercase = input_image * 0.5 + 0.5
__lowercase = input_image.clamp(0 , 1 )
__lowercase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowercase = DiffusionPipeline.numpy_to_pil(lowerCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableUnCLIPImgaImgPipeline(**lowerCamelCase )
__lowercase = sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
inputs.update({"image_embeds": None} )
__lowercase = sd_pipe(**lowerCamelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _snake_case ( self : str ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase )
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = pipe(
lowerCamelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 655 | 1 |
from __future__ import annotations
snake_case__ : Tuple = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
__lowercase = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the reference grid
__lowercase = 1
__lowercase = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the action grid
__lowercase = init[0]
__lowercase = init[1]
__lowercase = 0
__lowercase = g + heuristic[x][y] # cost from starting cell to destination cell
__lowercase = [[f, g, x, y]]
__lowercase = False # flag that is set when search is complete
__lowercase = False # flag set if we can't find expand
while not found and not resign:
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("Algorithm is unable to find solution" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
__lowercase = cell.pop()
__lowercase = next_cell[2]
__lowercase = next_cell[3]
__lowercase = next_cell[1]
if x == goal[0] and y == goal[1]:
__lowercase = True
else:
for i in range(len(_SCREAMING_SNAKE_CASE ) ): # to try out different valid actions
__lowercase = x + DIRECTIONS[i][0]
__lowercase = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
__lowercase = g + cost
__lowercase = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
__lowercase = 1
__lowercase = i
__lowercase = []
__lowercase = goal[0]
__lowercase = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
__lowercase = x - DIRECTIONS[action[x][y]][0]
__lowercase = y - DIRECTIONS[action[x][y]][1]
__lowercase = xa
__lowercase = ya
invpath.append([x, y] )
__lowercase = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
path.append(invpath[len(_SCREAMING_SNAKE_CASE ) - 1 - i] )
return path, action
if __name__ == "__main__":
snake_case__ : List[str] = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
snake_case__ : Optional[int] = [0, 0]
# all coordinates are given in format [y,x]
snake_case__ : Union[str, Any] = [len(grid) - 1, len(grid[0]) - 1]
snake_case__ : Dict = 1
# the cost map which pushes the path closer to the goal
snake_case__ : Tuple = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
snake_case__ : Optional[int] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
snake_case__ : List[Any] = 99
snake_case__ , snake_case__ : Union[str, Any] = search(grid, init, goal, cost, heuristic)
print("""ACTION MAP""")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 655 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , *,
lowerCamelCase : int = 4 , lowerCamelCase : int = 768 , lowerCamelCase : int , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
super().__init__()
__lowercase = nn.Parameter(torch.zeros(lowerCamelCase ) )
# parameters for additional clip time embeddings
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
# parameters for encoder hidden states
__lowercase = clip_extra_context_tokens
__lowercase = nn.Linear(
lowerCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.LayerNorm(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , *, lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
__lowercase = image_embeddings.shape[0]
__lowercase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
__lowercase = classifier_free_guidance_embeddings.expand(
lowerCamelCase , -1 )
__lowercase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
__lowercase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
__lowercase = self.embedding_proj(lowerCamelCase )
__lowercase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase )
__lowercase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
__lowercase = self.clip_extra_context_tokens_proj(lowerCamelCase )
__lowercase = clip_extra_context_tokens.reshape(lowerCamelCase , -1 , self.clip_extra_context_tokens )
__lowercase = clip_extra_context_tokens.permute(0 , 2 , 1 )
__lowercase = self.encoder_hidden_states_proj(lowerCamelCase )
__lowercase = self.text_encoder_hidden_states_norm(lowerCamelCase )
__lowercase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 655 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
snake_case__ : int = 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""")
snake_case__ : str = parser.parse_args()
snake_case__ : int = """cpu"""
snake_case__ : Optional[int] = """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""
snake_case__ : Union[str, Any] = """path-to-your-trained-model"""
snake_case__ : str = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
snake_case__ : int = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
snake_case__ : Union[str, Any] = pipe.to(device)
# to channels last
snake_case__ : Any = pipe.unet.to(memory_format=torch.channels_last)
snake_case__ : Any = pipe.vae.to(memory_format=torch.channels_last)
snake_case__ : int = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
snake_case__ : List[str] = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
snake_case__ : List[Any] = torch.randn(2, 4, 64, 64)
snake_case__ : Optional[int] = torch.rand(1) * 9_99
snake_case__ : str = torch.randn(2, 77, 7_68)
snake_case__ : List[Any] = (sample, timestep, encoder_hidden_status)
try:
snake_case__ : List[str] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
snake_case__ : Optional[int] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
snake_case__ : Dict = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
snake_case__ : str = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
snake_case__ : Union[str, Any] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
snake_case__ : Dict = 6_66
snake_case__ : List[str] = torch.Generator(device).manual_seed(seed)
snake_case__ : int = {"""generator""": generator}
if args.steps is not None:
snake_case__ : Optional[int] = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
snake_case__ : Tuple = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save("""generated.png""")
| 655 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case__ : Union[str, Any] = TypeVar("""T""")
snake_case__ : Optional[int] = TypeVar("""U""")
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : T | None , lowerCamelCase : U | None ):
'''simple docstring'''
__lowercase = key
__lowercase = val
__lowercase = None
__lowercase = None
def __repr__( self : Any ):
'''simple docstring'''
return (
f"""Node: key: {self.key}, val: {self.val}, """
f"""has next: {bool(self.next )}, has prev: {bool(self.prev )}"""
)
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase , __lowercase = self.rear, self.head
def __repr__( self : Optional[Any] ):
'''simple docstring'''
__lowercase = ["DoubleLinkedList"]
__lowercase = self.head
while node.next is not None:
rep.append(str(lowerCamelCase ) )
__lowercase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
__lowercase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
__lowercase = node
__lowercase = previous
__lowercase = node
__lowercase = self.rear
def _snake_case ( self : Optional[int] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
if node.prev is None or node.next is None:
return None
__lowercase = node.next
__lowercase = node.prev
__lowercase = None
__lowercase = None
return node
class _A ( Generic[T, U] ):
'''simple docstring'''
_snake_case : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = DoubleLinkedList()
__lowercase = capacity
__lowercase = 0
__lowercase = 0
__lowercase = 0
__lowercase = {}
def __repr__( self : Optional[Any] ):
'''simple docstring'''
return (
f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
f"""capacity={self.capacity}, current size={self.num_keys})"""
)
def __contains__( self : Dict , lowerCamelCase : T ):
'''simple docstring'''
return key in self.cache
def _snake_case ( self : List[Any] , lowerCamelCase : T ):
'''simple docstring'''
if key in self.cache:
self.hits += 1
__lowercase = self.cache[key]
__lowercase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowerCamelCase )
return node.val
self.miss += 1
return None
def _snake_case ( self : Union[str, Any] , lowerCamelCase : T , lowerCamelCase : U ):
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
__lowercase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowerCamelCase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
__lowercase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
__lowercase = value
self.list.add(lowerCamelCase )
@classmethod
def _snake_case ( cls : Union[str, Any] , lowerCamelCase : int = 128 ):
'''simple docstring'''
def cache_decorator_inner(lowerCamelCase : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*lowerCamelCase : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
__lowercase = LRUCache(lowerCamelCase )
__lowercase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
__lowercase = func(*lowerCamelCase )
cls.decorator_function_to_instance_map[func].put(args[0] , lowerCamelCase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowerCamelCase , "cache_info" , lowerCamelCase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Union[str, Any] = logging.get_logger(__name__)
snake_case__ : List[str] = {
"""google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
_snake_case : Optional[int] = """bit"""
_snake_case : Dict = ["""preactivation""", """bottleneck"""]
_snake_case : Any = ["""SAME""", """VALID"""]
def __init__( self : int , lowerCamelCase : Any=3 , lowerCamelCase : Dict=64 , lowerCamelCase : List[Any]=[256, 512, 1_024, 2_048] , lowerCamelCase : Dict=[3, 4, 6, 3] , lowerCamelCase : Tuple="preactivation" , lowerCamelCase : Tuple="relu" , lowerCamelCase : Tuple=None , lowerCamelCase : Dict=32 , lowerCamelCase : Dict=0.0 , lowerCamelCase : Dict=False , lowerCamelCase : Union[str, Any]=32 , lowerCamelCase : List[Any]=1 , lowerCamelCase : List[Any]=None , lowerCamelCase : Dict=None , **lowerCamelCase : List[str] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
__lowercase = global_padding.upper()
else:
raise ValueError(f"""Padding strategy {global_padding} not supported""" )
__lowercase = num_channels
__lowercase = embedding_size
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = layer_type
__lowercase = hidden_act
__lowercase = global_padding
__lowercase = num_groups
__lowercase = drop_path_rate
__lowercase = embedding_dynamic_padding
__lowercase = output_stride
__lowercase = width_factor
__lowercase = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(lowerCamelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase , out_indices=lowerCamelCase , stage_names=self.stage_names )
| 655 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
snake_case__ : Optional[Any] = logging.getLogger()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = "\n".join(_SCREAMING_SNAKE_CASE )
Path(_SCREAMING_SNAKE_CASE ).open("w" ).writelines(_SCREAMING_SNAKE_CASE )
snake_case__ : List[str] = """patrickvonplaten/t5-tiny-random"""
snake_case__ : int = """sshleifer/bart-tiny-random"""
snake_case__ : Union[str, Any] = """sshleifer/tiny-mbart"""
snake_case__ : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : str , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(lowerCamelCase , lowerCamelCase )
__lowercase = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_generate()
assert Path(lowerCamelCase ).exists()
# os.remove(Path(output_file_name))
def _snake_case ( self : Dict ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
__lowercase = Path(self.get_auto_remove_tmp_dir() )
__lowercase = str(tmp_dir / "scores.json" )
__lowercase = str(tmp_dir / "val.target" )
_dump_articles(lowerCamelCase , text["en"] )
_dump_articles(lowerCamelCase , text["de"] )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{str(lowerCamelCase )}
{str(lowerCamelCase )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
with CaptureStdout() as cs:
run_search()
__lowercase = [" num_beams | length_penalty", model, "Best score args"]
__lowercase = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(lowerCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(lowerCamelCase ).exists()
os.remove(Path(lowerCamelCase ) )
| 655 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _A ( enum.Enum ):
'''simple docstring'''
_snake_case : str = 0
_snake_case : Any = 1
_snake_case : Optional[Any] = 2
@add_end_docstrings(_lowercase )
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Optional[Any] = """
In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
begging for his blessing. <eod> </s> <eos>
"""
def __init__( self : List[str] , *lowerCamelCase : List[str] , **lowerCamelCase : Optional[int] ):
'''simple docstring'''
super().__init__(*lowerCamelCase , **lowerCamelCase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
__lowercase = None
if self.model.config.prefix is not None:
__lowercase = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
__lowercase = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
__lowercase , __lowercase , __lowercase = self._sanitize_parameters(prefix=lowerCamelCase , **self._forward_params )
__lowercase = {**self._preprocess_params, **preprocess_params}
__lowercase = {**self._forward_params, **forward_params}
def _snake_case ( self : Any , lowerCamelCase : Optional[int]=None , lowerCamelCase : Any=None , lowerCamelCase : Optional[int]=None , lowerCamelCase : List[str]=None , lowerCamelCase : int=None , lowerCamelCase : Any=None , lowerCamelCase : List[str]=None , lowerCamelCase : Union[str, Any]=None , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = {}
if prefix is not None:
__lowercase = prefix
if prefix:
__lowercase = self.tokenizer(
lowerCamelCase , padding=lowerCamelCase , add_special_tokens=lowerCamelCase , return_tensors=self.framework )
__lowercase = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
" [None, 'hole']" )
__lowercase = handle_long_generation
preprocess_params.update(lowerCamelCase )
__lowercase = generate_kwargs
__lowercase = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
__lowercase = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
__lowercase = ReturnType.TENSORS
if return_type is not None:
__lowercase = return_type
if clean_up_tokenization_spaces is not None:
__lowercase = clean_up_tokenization_spaces
if stop_sequence is not None:
__lowercase = self.tokenizer.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
if len(lowerCamelCase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
__lowercase = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def _snake_case ( self : int , *lowerCamelCase : Optional[int] , **lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*lowerCamelCase , **lowerCamelCase )
def __call__( self : Tuple , lowerCamelCase : Union[str, Any] , **lowerCamelCase : str ):
'''simple docstring'''
return super().__call__(lowerCamelCase , **lowerCamelCase )
def _snake_case ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Dict="" , lowerCamelCase : List[Any]=None , **lowerCamelCase : List[str] ):
'''simple docstring'''
__lowercase = self.tokenizer(
prefix + prompt_text , padding=lowerCamelCase , add_special_tokens=lowerCamelCase , return_tensors=self.framework )
__lowercase = prompt_text
if handle_long_generation == "hole":
__lowercase = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
__lowercase = generate_kwargs["max_new_tokens"]
else:
__lowercase = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
__lowercase = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
__lowercase = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
__lowercase = inputs["attention_mask"][:, -keep_length:]
return inputs
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Optional[int] , **lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowercase = model_inputs["input_ids"]
__lowercase = model_inputs.get("attention_mask" , lowerCamelCase )
# Allow empty prompts
if input_ids.shape[1] == 0:
__lowercase = None
__lowercase = None
__lowercase = 1
else:
__lowercase = input_ids.shape[0]
__lowercase = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
__lowercase = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
__lowercase = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
__lowercase = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
__lowercase = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
__lowercase = self.model.generate(input_ids=lowerCamelCase , attention_mask=lowerCamelCase , **lowerCamelCase )
__lowercase = generated_sequence.shape[0]
if self.framework == "pt":
__lowercase = generated_sequence.reshape(lowerCamelCase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
__lowercase = tf.reshape(lowerCamelCase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def _snake_case ( self : Tuple , lowerCamelCase : str , lowerCamelCase : Optional[int]=ReturnType.FULL_TEXT , lowerCamelCase : List[str]=True ):
'''simple docstring'''
__lowercase = model_outputs["generated_sequence"][0]
__lowercase = model_outputs["input_ids"]
__lowercase = model_outputs["prompt_text"]
__lowercase = generated_sequence.numpy().tolist()
__lowercase = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
__lowercase = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
__lowercase = self.tokenizer.decode(
lowerCamelCase , skip_special_tokens=lowerCamelCase , clean_up_tokenization_spaces=lowerCamelCase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
__lowercase = 0
else:
__lowercase = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=lowerCamelCase , clean_up_tokenization_spaces=lowerCamelCase , ) )
if return_type == ReturnType.FULL_TEXT:
__lowercase = prompt_text + text[prompt_length:]
else:
__lowercase = text[prompt_length:]
__lowercase = {"generated_text": all_text}
records.append(lowerCamelCase )
return records
| 655 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _A :
'''simple docstring'''
_snake_case : int
_snake_case : TreeNode | None = None
_snake_case : TreeNode | None = None
snake_case__ : Dict = namedtuple("""CoinsDistribResult""", """moves excess""")
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if root is None:
return 0
# Validation
def count_nodes(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_SCREAMING_SNAKE_CASE ) != count_coins(_SCREAMING_SNAKE_CASE ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(_SCREAMING_SNAKE_CASE ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
__lowercase , __lowercase = get_distrib(node.left )
__lowercase , __lowercase = get_distrib(node.right )
__lowercase = 1 - left_distrib_excess
__lowercase = 1 - right_distrib_excess
__lowercase = (
left_distrib_moves
+ right_distrib_moves
+ abs(_SCREAMING_SNAKE_CASE )
+ abs(_SCREAMING_SNAKE_CASE )
)
__lowercase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return get_distrib(_SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
snake_case__ : Optional[int] = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
snake_case__ : str = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=_SCREAMING_SNAKE_CASE )[0]
@deprecated(_SCREAMING_SNAKE_CASE , "Please use tf.data to implement this functionality." )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE ) as bytestream:
__lowercase = _readaa(_SCREAMING_SNAKE_CASE )
if magic != 2_0_5_1:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
__lowercase = _readaa(_SCREAMING_SNAKE_CASE )
__lowercase = _readaa(_SCREAMING_SNAKE_CASE )
__lowercase = _readaa(_SCREAMING_SNAKE_CASE )
__lowercase = bytestream.read(rows * cols * num_images )
__lowercase = numpy.frombuffer(_SCREAMING_SNAKE_CASE , dtype=numpy.uinta )
__lowercase = data.reshape(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 1 )
return data
@deprecated(_SCREAMING_SNAKE_CASE , "Please use tf.one_hot on tensors." )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = labels_dense.shape[0]
__lowercase = numpy.arange(_SCREAMING_SNAKE_CASE ) * num_classes
__lowercase = numpy.zeros((num_labels, num_classes) )
__lowercase = 1
return labels_one_hot
@deprecated(_SCREAMING_SNAKE_CASE , "Please use tf.data to implement this functionality." )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=1_0 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE ) as bytestream:
__lowercase = _readaa(_SCREAMING_SNAKE_CASE )
if magic != 2_0_4_9:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
__lowercase = _readaa(_SCREAMING_SNAKE_CASE )
__lowercase = bytestream.read(_SCREAMING_SNAKE_CASE )
__lowercase = numpy.frombuffer(_SCREAMING_SNAKE_CASE , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return labels
class _A :
'''simple docstring'''
@deprecated(
lowerCamelCase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any]=False , lowerCamelCase : str=False , lowerCamelCase : str=dtypes.floataa , lowerCamelCase : Optional[int]=True , lowerCamelCase : int=None , ):
'''simple docstring'''
__lowercase , __lowercase = random_seed.get_seed(lowerCamelCase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
__lowercase = dtypes.as_dtype(lowerCamelCase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
__lowercase = 10_000
__lowercase = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
__lowercase = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
__lowercase = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
__lowercase = images.astype(numpy.floataa )
__lowercase = numpy.multiply(lowerCamelCase , 1.0 / 255.0 )
__lowercase = images
__lowercase = labels
__lowercase = 0
__lowercase = 0
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return self._images
@property
def _snake_case ( self : List[str] ):
'''simple docstring'''
return self._labels
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self._num_examples
@property
def _snake_case ( self : List[str] ):
'''simple docstring'''
return self._epochs_completed
def _snake_case ( self : List[str] , lowerCamelCase : Optional[int] , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=True ):
'''simple docstring'''
if fake_data:
__lowercase = [1] * 784
__lowercase = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(lowerCamelCase )],
[fake_label for _ in range(lowerCamelCase )],
)
__lowercase = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
__lowercase = numpy.arange(self._num_examples )
numpy.random.shuffle(lowerCamelCase )
__lowercase = self.images[perma]
__lowercase = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
__lowercase = self._num_examples - start
__lowercase = self._images[start : self._num_examples]
__lowercase = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
__lowercase = numpy.arange(self._num_examples )
numpy.random.shuffle(lowerCamelCase )
__lowercase = self.images[perm]
__lowercase = self.labels[perm]
# Start next epoch
__lowercase = 0
__lowercase = batch_size - rest_num_examples
__lowercase = self._index_in_epoch
__lowercase = self._images[start:end]
__lowercase = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
__lowercase = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(_SCREAMING_SNAKE_CASE , "Please write your own downloading logic." )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if not gfile.Exists(_SCREAMING_SNAKE_CASE ):
gfile.MakeDirs(_SCREAMING_SNAKE_CASE )
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if not gfile.Exists(_SCREAMING_SNAKE_CASE ):
urllib.request.urlretrieve(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # noqa: S310
with gfile.GFile(_SCREAMING_SNAKE_CASE ) as f:
__lowercase = f.size()
print("Successfully downloaded" , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , "bytes." )
return filepath
@deprecated(
_SCREAMING_SNAKE_CASE , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=dtypes.floataa , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=5_0_0_0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=_SCREAMING_SNAKE_CASE , one_hot=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE , seed=_SCREAMING_SNAKE_CASE )
__lowercase = fake()
__lowercase = fake()
__lowercase = fake()
return _Datasets(train=_SCREAMING_SNAKE_CASE , validation=_SCREAMING_SNAKE_CASE , test=_SCREAMING_SNAKE_CASE )
if not source_url: # empty string check
__lowercase = DEFAULT_SOURCE_URL
__lowercase = "train-images-idx3-ubyte.gz"
__lowercase = "train-labels-idx1-ubyte.gz"
__lowercase = "t10k-images-idx3-ubyte.gz"
__lowercase = "t10k-labels-idx1-ubyte.gz"
__lowercase = _maybe_download(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , source_url + train_images_file )
with gfile.Open(_SCREAMING_SNAKE_CASE , "rb" ) as f:
__lowercase = _extract_images(_SCREAMING_SNAKE_CASE )
__lowercase = _maybe_download(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , source_url + train_labels_file )
with gfile.Open(_SCREAMING_SNAKE_CASE , "rb" ) as f:
__lowercase = _extract_labels(_SCREAMING_SNAKE_CASE , one_hot=_SCREAMING_SNAKE_CASE )
__lowercase = _maybe_download(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , source_url + test_images_file )
with gfile.Open(_SCREAMING_SNAKE_CASE , "rb" ) as f:
__lowercase = _extract_images(_SCREAMING_SNAKE_CASE )
__lowercase = _maybe_download(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , source_url + test_labels_file )
with gfile.Open(_SCREAMING_SNAKE_CASE , "rb" ) as f:
__lowercase = _extract_labels(_SCREAMING_SNAKE_CASE , one_hot=_SCREAMING_SNAKE_CASE )
if not 0 <= validation_size <= len(_SCREAMING_SNAKE_CASE ):
__lowercase = (
"Validation size should be between 0 and "
F"""{len(_SCREAMING_SNAKE_CASE )}. Received: {validation_size}."""
)
raise ValueError(_SCREAMING_SNAKE_CASE )
__lowercase = train_images[:validation_size]
__lowercase = train_labels[:validation_size]
__lowercase = train_images[validation_size:]
__lowercase = train_labels[validation_size:]
__lowercase = {"dtype": dtype, "reshape": reshape, "seed": seed}
__lowercase = _DataSet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
__lowercase = _DataSet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
__lowercase = _DataSet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
return _Datasets(train=_SCREAMING_SNAKE_CASE , validation=_SCREAMING_SNAKE_CASE , test=_SCREAMING_SNAKE_CASE )
| 655 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = SwinvaConfig()
__lowercase = swinva_name.split("_" )
__lowercase = name_split[1]
if "to" in name_split[3]:
__lowercase = int(name_split[3][-3:] )
else:
__lowercase = int(name_split[3] )
if "to" in name_split[2]:
__lowercase = int(name_split[2][-2:] )
else:
__lowercase = int(name_split[2][6:] )
if model_size == "tiny":
__lowercase = 9_6
__lowercase = (2, 2, 6, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "small":
__lowercase = 9_6
__lowercase = (2, 2, 1_8, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "base":
__lowercase = 1_2_8
__lowercase = (2, 2, 1_8, 2)
__lowercase = (4, 8, 1_6, 3_2)
else:
__lowercase = 1_9_2
__lowercase = (2, 2, 1_8, 2)
__lowercase = (6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
__lowercase = (1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__lowercase = 2_1_8_4_1
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-22k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
else:
__lowercase = 1_0_0_0
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-1k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = img_size
__lowercase = num_classes
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
return config
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if "patch_embed.proj" in name:
__lowercase = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
__lowercase = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
__lowercase = "encoder." + name
if "attn.proj" in name:
__lowercase = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
__lowercase = name.replace("attn" , "attention.self" )
if "norm1" in name:
__lowercase = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
__lowercase = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
__lowercase = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
__lowercase = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
__lowercase = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
__lowercase = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
__lowercase = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
__lowercase = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
__lowercase = "layernorm.weight"
if name == "norm.bias":
__lowercase = "layernorm.bias"
if "head" in name:
__lowercase = name.replace("head" , "classifier" )
else:
__lowercase = "swinv2." + name
return name
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for key in orig_state_dict.copy().keys():
__lowercase = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
__lowercase = key.split("." )
__lowercase = int(key_split[1] )
__lowercase = int(key_split[3] )
__lowercase = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__lowercase = val[:dim, :]
__lowercase = val[dim : dim * 2, :]
__lowercase = val[-dim:, :]
else:
__lowercase = val[:dim]
__lowercase = val[
dim : dim * 2
]
__lowercase = val[-dim:]
else:
__lowercase = val
return orig_state_dict
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
__lowercase = get_swinva_config(_SCREAMING_SNAKE_CASE )
__lowercase = SwinvaForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
__lowercase = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
__lowercase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
__lowercase = timm_model(inputs["pixel_values"] )
__lowercase = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
model.push_to_hub(
repo_path_or_name=Path(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swinv2_name""",
default="""swinv2_tiny_patch4_window8_256""",
type=str,
help="""Name of the Swinv2 timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case__ : str = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 655 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
snake_case__ : Dict = 1_00
snake_case__ : Tuple = set(range(3, NUM_PRIMES, 2))
primes.add(2)
snake_case__ : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0 )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
__lowercase = set()
__lowercase = 42
__lowercase = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def snake_case_ ( _SCREAMING_SNAKE_CASE = 5_0_0_0 ):
for number_to_partition in range(1 , _SCREAMING_SNAKE_CASE ):
if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'''{solution() = }''')
| 655 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
snake_case__ : Optional[Any] = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
snake_case__ : List[str] = {
"""allenai/led-base-16384""": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def snake_case_ ( ):
__lowercase = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
__lowercase = bs[:]
__lowercase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
__lowercase = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
return pairs
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[str] = VOCAB_FILES_NAMES
_snake_case : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Optional[int]="replace" , lowerCamelCase : Dict="<s>" , lowerCamelCase : Dict="</s>" , lowerCamelCase : Optional[Any]="</s>" , lowerCamelCase : Any="<s>" , lowerCamelCase : List[str]="<unk>" , lowerCamelCase : Union[str, Any]="<pad>" , lowerCamelCase : Any="<mask>" , lowerCamelCase : str=False , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else bos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else eos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else sep_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else cls_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else unk_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else mask_token
super().__init__(
errors=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , add_prefix_space=lowerCamelCase , **lowerCamelCase , )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
__lowercase = json.load(lowerCamelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = errors # how to handle errors in decoding
__lowercase = bytes_to_unicode()
__lowercase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
__lowercase = merges_handle.read().split("\n" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in bpe_merges]
__lowercase = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__lowercase = {}
__lowercase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowercase = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return len(self.encoder )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : List[Any] , lowerCamelCase : str ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowercase = tuple(lowerCamelCase )
__lowercase = get_pairs(lowerCamelCase )
if not pairs:
return token
while True:
__lowercase = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(lowerCamelCase ):
try:
__lowercase = word.index(lowerCamelCase , lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase = j
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(lowerCamelCase )
__lowercase = new_word
if len(lowerCamelCase ) == 1:
break
else:
__lowercase = get_pairs(lowerCamelCase )
__lowercase = " ".join(lowerCamelCase )
__lowercase = word
return word
def _snake_case ( self : List[Any] , lowerCamelCase : Tuple ):
'''simple docstring'''
__lowercase = []
for token in re.findall(self.pat , lowerCamelCase ):
__lowercase = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : Dict , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : str , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.decoder.get(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = "".join(lowerCamelCase )
__lowercase = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
__lowercase = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
__lowercase = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : str , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase )) + [1]
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase )) + [1]
def _snake_case ( self : int , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [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 _snake_case ( self : Dict , lowerCamelCase : Any , lowerCamelCase : Tuple=False , **lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase ) > 0 and not text[0].isspace()):
__lowercase = " " + text
return (text, kwargs)
def _snake_case ( self : List[Any] , lowerCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCamelCase : Optional[int] = None , lowerCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[bool] = None , ):
'''simple docstring'''
__lowercase = super()._pad(
encoded_inputs=lowerCamelCase , max_length=lowerCamelCase , padding_strategy=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , )
# Load from model defaults
if return_attention_mask is None:
__lowercase = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__lowercase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__lowercase = len(encoded_inputs["global_attention_mask"] ) != len(lowerCamelCase )
if needs_to_be_padded:
__lowercase = len(lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__lowercase = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
__lowercase = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zero." )
if any(p < 0 for p in profit ):
raise ValueError("Profit can not be negative." )
if any(w < 0 for w in weight ):
raise ValueError("Weight can not be negative." )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
__lowercase = [p / w for p, w in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
# Creating a copy of the list and sorting profit/weight in ascending order
__lowercase = sorted(_SCREAMING_SNAKE_CASE )
# declaring useful variables
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = 0
__lowercase = 0
__lowercase = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
__lowercase = sorted_profit_by_weight[length - i - 1]
__lowercase = profit_by_weight.index(_SCREAMING_SNAKE_CASE )
__lowercase = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"""Input profits, weights, and then max_weight (all positive ints) separated by """
"""spaces."""
)
snake_case__ : str = [int(x) for x in input("""Input profits separated by spaces: """).split()]
snake_case__ : str = [int(x) for x in input("""Input weights separated by spaces: """).split()]
snake_case__ : Optional[Any] = int(input("""Max weight allowed: """))
# Function Call
calc_profit(profit, weight, max_weight)
| 655 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class _A ( _lowercase , _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Dict = StableUnCLIPImgaImgPipeline
_snake_case : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
_snake_case : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_snake_case : int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_snake_case : int = frozenset([] )
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = 32
__lowercase = embedder_hidden_size
# image encoding components
__lowercase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
__lowercase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowerCamelCase , projection_dim=lowerCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__lowercase = StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase )
__lowercase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowercase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowercase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCamelCase , layers_per_block=1 , upcast_attention=lowerCamelCase , use_linear_projection=lowerCamelCase , )
torch.manual_seed(0 )
__lowercase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=lowerCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
__lowercase = AutoencoderKL()
__lowercase = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def _snake_case ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : Any=0 , lowerCamelCase : Union[str, Any]=True ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
__lowercase = torch.manual_seed(lowerCamelCase )
else:
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
if pil_image:
__lowercase = input_image * 0.5 + 0.5
__lowercase = input_image.clamp(0 , 1 )
__lowercase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowercase = DiffusionPipeline.numpy_to_pil(lowerCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableUnCLIPImgaImgPipeline(**lowerCamelCase )
__lowercase = sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
inputs.update({"image_embeds": None} )
__lowercase = sd_pipe(**lowerCamelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _snake_case ( self : str ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase )
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = pipe(
lowerCamelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 655 |
# 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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """openai/whisper-base"""
_snake_case : Union[str, Any] = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
_snake_case : Any = """transcriber"""
_snake_case : Any = WhisperProcessor
_snake_case : Optional[int] = WhisperForConditionalGeneration
_snake_case : str = ["""audio"""]
_snake_case : Optional[int] = ["""text"""]
def _snake_case ( self : List[str] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.pre_processor(lowerCamelCase , return_tensors="pt" ).input_features
def _snake_case ( self : str , lowerCamelCase : List[Any] ):
'''simple docstring'''
return self.model.generate(inputs=lowerCamelCase )
def _snake_case ( self : List[str] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(lowerCamelCase , skip_special_tokens=lowerCamelCase )[0]
| 655 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = 1.5
__lowercase = int(factor * num_class_images )
__lowercase = ClipClient(
url="https://knn.laion.ai/knn-service" , indice_name="laion_400m" , num_images=_SCREAMING_SNAKE_CASE , aesthetic_weight=0.1 )
os.makedirs(F"""{class_data_dir}/images""" , exist_ok=_SCREAMING_SNAKE_CASE )
if len(list(Path(F"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
__lowercase = client.query(text=_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) >= factor * num_class_images or num_images > 1E4:
break
else:
__lowercase = int(factor * num_images )
__lowercase = ClipClient(
url="https://knn.laion.ai/knn-service" , indice_name="laion_400m" , num_images=_SCREAMING_SNAKE_CASE , aesthetic_weight=0.1 , )
__lowercase = 0
__lowercase = 0
__lowercase = tqdm(desc="downloading real regularization images" , total=_SCREAMING_SNAKE_CASE )
with open(F"""{class_data_dir}/caption.txt""" , "w" ) as fa, open(F"""{class_data_dir}/urls.txt""" , "w" ) as fa, open(
F"""{class_data_dir}/images.txt""" , "w" ) as fa:
while total < num_class_images:
__lowercase = class_images[count]
count += 1
try:
__lowercase = requests.get(images["url"] )
if img.status_code == 2_0_0:
__lowercase = Image.open(BytesIO(img.content ) )
with open(F"""{class_data_dir}/images/{total}.jpg""" , "wb" ) as f:
f.write(img.content )
fa.write(images["caption"] + "\n" )
fa.write(images["url"] + "\n" )
fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def snake_case_ ( ):
__lowercase = argparse.ArgumentParser("" , add_help=_SCREAMING_SNAKE_CASE )
parser.add_argument("--class_prompt" , help="text prompt to retrieve images" , required=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE )
parser.add_argument("--class_data_dir" , help="path to save images" , required=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE )
parser.add_argument("--num_class_images" , help="number of images to download" , default=2_0_0 , type=_SCREAMING_SNAKE_CASE )
return parser.parse_args()
if __name__ == "__main__":
snake_case__ : Dict = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _A :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["prompt"]
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
if "image" in inputs:
__lowercase = inputs["image"]
else:
__lowercase = None
if "mask_image" in inputs:
__lowercase = inputs["mask_image"]
else:
__lowercase = None
if "original_image" in inputs:
__lowercase = inputs["original_image"]
else:
__lowercase = None
__lowercase , __lowercase = pipe.encode_prompt(lowerCamelCase )
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase , lowerCamelCase ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE = 2_0_0 ):
__lowercase = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
__lowercase = [0] * (pence + 1)
__lowercase = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(_SCREAMING_SNAKE_CASE , pence + 1 , 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(2_00) == 7_36_82
| 655 |
import numpy as np
snake_case__ : Tuple = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class _A :
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase , __lowercase = np.where(letter == self.SQUARE )
__lowercase = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def _snake_case ( self : List[Any] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = self.SQUARE[indexa - 1, indexa - 1]
return letter
def _snake_case ( self : int , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
__lowercase = message.replace(" " , "" )
__lowercase = message.replace("j" , "i" )
__lowercase = np.empty((2, len(lowerCamelCase )) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape(2 * len(lowerCamelCase ) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[numbers_index * 2] )
__lowercase = int(second_step[(numbers_index * 2) + 1] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = encoded_message + letter
return encoded_message
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
message.replace(" " , "" )
__lowercase = np.empty(2 * len(lowerCamelCase ) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape((2, len(lowerCamelCase )) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[0, numbers_index] )
__lowercase = int(second_step[1, numbers_index] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = decoded_message + letter
return decoded_message
| 655 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = ["""image_processor""", """tokenizer"""]
_snake_case : Dict = """BlipImageProcessor"""
_snake_case : Tuple = """AutoTokenizer"""
def __init__( self : int , lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
__lowercase = False
super().__init__(lowerCamelCase , lowerCamelCase )
__lowercase = self.image_processor
def __call__( self : Optional[int] , lowerCamelCase : ImageInput = None , lowerCamelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowerCamelCase : bool = True , lowerCamelCase : Union[bool, str, PaddingStrategy] = False , lowerCamelCase : Union[bool, str, TruncationStrategy] = None , lowerCamelCase : Optional[int] = None , lowerCamelCase : int = 0 , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[bool] = None , lowerCamelCase : bool = False , lowerCamelCase : bool = False , lowerCamelCase : bool = False , lowerCamelCase : bool = False , lowerCamelCase : bool = False , lowerCamelCase : bool = True , lowerCamelCase : Optional[Union[str, TensorType]] = None , **lowerCamelCase : List[str] , ):
'''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:
__lowercase = self.tokenizer
__lowercase = self.tokenizer(
text=lowerCamelCase , add_special_tokens=lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase , max_length=lowerCamelCase , stride=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , return_overflowing_tokens=lowerCamelCase , return_special_tokens_mask=lowerCamelCase , return_offsets_mapping=lowerCamelCase , return_token_type_ids=lowerCamelCase , return_length=lowerCamelCase , verbose=lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase , )
return text_encoding
# add pixel_values
__lowercase = self.image_processor(lowerCamelCase , return_tensors=lowerCamelCase )
if text is not None:
__lowercase = self.tokenizer(
text=lowerCamelCase , add_special_tokens=lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase , max_length=lowerCamelCase , stride=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , return_overflowing_tokens=lowerCamelCase , return_special_tokens_mask=lowerCamelCase , return_offsets_mapping=lowerCamelCase , return_token_type_ids=lowerCamelCase , return_length=lowerCamelCase , verbose=lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase , )
else:
__lowercase = None
if text_encoding is not None:
encoding_image_processor.update(lowerCamelCase )
return encoding_image_processor
def _snake_case ( self : Union[str, Any] , *lowerCamelCase : Tuple , **lowerCamelCase : Tuple ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase , **lowerCamelCase )
def _snake_case ( self : Optional[Any] , *lowerCamelCase : Tuple , **lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase , **lowerCamelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = self.tokenizer.model_input_names
__lowercase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 655 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
'''simple docstring'''
_snake_case : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def snake_case_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 655 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
snake_case__ : List[str] = pytest.mark.integration
snake_case__ : int = {"""comet"""}
snake_case__ : Optional[int] = importlib.util.find_spec("""fairseq""") is not None
snake_case__ : List[Any] = {"""code_eval"""}
snake_case__ : str = os.name == """nt"""
snake_case__ : Dict = {"""bertscore""", """frugalscore""", """perplexity"""}
snake_case__ : int = importlib.util.find_spec("""transformers""") is not None
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
@wraps(_SCREAMING_SNAKE_CASE )
def wrapper(self , _SCREAMING_SNAKE_CASE ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("\"test requires Fairseq\"" )
else:
test_case(self , _SCREAMING_SNAKE_CASE )
return wrapper
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
@wraps(_SCREAMING_SNAKE_CASE )
def wrapper(self , _SCREAMING_SNAKE_CASE ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("\"test requires transformers\"" )
else:
test_case(self , _SCREAMING_SNAKE_CASE )
return wrapper
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
@wraps(_SCREAMING_SNAKE_CASE )
def wrapper(self , _SCREAMING_SNAKE_CASE ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("\"test not supported on Windows\"" )
else:
test_case(self , _SCREAMING_SNAKE_CASE )
return wrapper
def snake_case_ ( ):
__lowercase = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
_lowercase , _lowercase , _lowercase )
@local
class _A ( parameterized.TestCase ):
'''simple docstring'''
_snake_case : Optional[Any] = {}
_snake_case : List[Any] = None
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
def _snake_case ( self : str , lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = "[...]"
__lowercase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , lowerCamelCase ) ).module_path )
__lowercase = datasets.load.import_main_class(metric_module.__name__ , dataset=lowerCamelCase )
# check parameters
__lowercase = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(lowerCamelCase , metric_module.__name__ ):
with self.use_local_metrics():
try:
__lowercase = doctest.testmod(lowerCamelCase , verbose=lowerCamelCase , raise_on_error=lowerCamelCase )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def _snake_case ( self : List[str] , lowerCamelCase : List[str] ):
'''simple docstring'''
__lowercase = "[...]"
__lowercase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , lowerCamelCase ) ).module_path )
# run doctest
with self.use_local_metrics():
__lowercase = doctest.testmod(lowerCamelCase , verbose=lowerCamelCase , raise_on_error=lowerCamelCase )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def _snake_case ( self : List[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : List[str] ):
'''simple docstring'''
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase ):
yield
else:
yield
@contextmanager
def _snake_case ( self : List[Any] ):
'''simple docstring'''
def load_local_metric(lowerCamelCase : Optional[int] , *lowerCamelCase : Any , **lowerCamelCase : Dict ):
return load_metric(os.path.join("metrics" , lowerCamelCase ) , *lowerCamelCase , **lowerCamelCase )
with patch("datasets.load_metric" ) as mock_load_metric:
__lowercase = load_local_metric
yield
@classmethod
def _snake_case ( cls : Union[str, Any] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
def wrapper(lowerCamelCase : Union[str, Any] ):
__lowercase = contextmanager(lowerCamelCase )
__lowercase = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("sv" , "" , "" ) # handle pytest cli flags
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : Any , lowerCamelCase : int ):
'''simple docstring'''
assert len(input_dict["input_ids"] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
__lowercase = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
import torch
def bert_cos_score_idf(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(_SCREAMING_SNAKE_CASE ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("bert_score.scorer.get_model" ), patch(
"bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
__lowercase = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("comet" )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
def load_from_checkpoint(_SCREAMING_SNAKE_CASE ):
class _A :
'''simple docstring'''
def _snake_case ( self : str , lowerCamelCase : Any , *lowerCamelCase : List[str] , **lowerCamelCase : List[str] ):
'''simple docstring'''
assert len(lowerCamelCase ) == 2
__lowercase = [0.19, 0.92]
return scores, sum(lowerCamelCase ) / len(lowerCamelCase )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("comet.download_model" ) as mock_download_model:
__lowercase = None
with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
__lowercase = load_from_checkpoint
yield
def snake_case_ ( ):
__lowercase = load_metric(os.path.join("metrics" , "seqeval" ) )
__lowercase = "ERROR"
__lowercase = F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
with pytest.raises(_SCREAMING_SNAKE_CASE , match=re.escape(_SCREAMING_SNAKE_CASE ) ):
metric.compute(predictions=[] , references=[] , scheme=_SCREAMING_SNAKE_CASE )
| 655 |
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__ : List[Any] = 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 ( _lowercase ):
'''simple docstring'''
_snake_case : List[Any] = """yolos"""
def __init__( self : Union[str, Any] , lowerCamelCase : Union[str, Any]=768 , lowerCamelCase : int=12 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Optional[Any]=3_072 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Dict=0.0 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Any=0.02 , lowerCamelCase : Optional[Any]=1e-12 , lowerCamelCase : Optional[Any]=[512, 864] , lowerCamelCase : str=16 , lowerCamelCase : Dict=3 , lowerCamelCase : str=True , lowerCamelCase : List[Any]=100 , lowerCamelCase : Dict=True , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=1 , lowerCamelCase : str=5 , lowerCamelCase : Any=2 , lowerCamelCase : str=5 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : List[Any]=0.1 , **lowerCamelCase : List[Any] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = num_detection_tokens
__lowercase = use_mid_position_embeddings
__lowercase = auxiliary_loss
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = version.parse("""1.11""" )
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : str ):
'''simple docstring'''
return 1e-4
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return 12
| 655 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
snake_case__ : int = random.Random()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ):
if rng is None:
__lowercase = global_rng
__lowercase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class _A ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str]=7 , lowerCamelCase : Optional[int]=400 , lowerCamelCase : Union[str, Any]=2_000 , lowerCamelCase : Union[str, Any]=10 , lowerCamelCase : Optional[int]=160 , lowerCamelCase : Union[str, Any]=8 , lowerCamelCase : str=0.0 , lowerCamelCase : Dict=4_000 , lowerCamelCase : Optional[Any]=False , lowerCamelCase : int=True , ):
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = min_seq_length
__lowercase = max_seq_length
__lowercase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__lowercase = padding_value
__lowercase = sampling_rate
__lowercase = return_attention_mask
__lowercase = do_normalize
__lowercase = feature_size
__lowercase = chunk_length
__lowercase = hop_length
def _snake_case ( self : str ):
'''simple docstring'''
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int=False , lowerCamelCase : Union[str, Any]=False ):
'''simple docstring'''
def _flatten(lowerCamelCase : Any ):
return list(itertools.chain(*lowerCamelCase ) )
if equal_length:
__lowercase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
__lowercase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__lowercase = [np.asarray(lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _A ( _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Dict = WhisperFeatureExtractor if is_speech_available() else None
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = WhisperFeatureExtractionTester(self )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = feat_extract_first.save_pretrained(lowerCamelCase )[0]
check_json_file_has_correct_format(lowerCamelCase )
__lowercase = self.feature_extraction_class.from_pretrained(lowerCamelCase )
__lowercase = feat_extract_first.to_dict()
__lowercase = feat_extract_second.to_dict()
__lowercase = feat_extract_first.mel_filters
__lowercase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase ) )
self.assertEqual(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowercase = os.path.join(lowerCamelCase , "feat_extract.json" )
feat_extract_first.to_json_file(lowerCamelCase )
__lowercase = self.feature_extraction_class.from_json_file(lowerCamelCase )
__lowercase = feat_extract_first.to_dict()
__lowercase = feat_extract_second.to_dict()
__lowercase = feat_extract_first.mel_filters
__lowercase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase ) )
self.assertEqual(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__lowercase = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__lowercase = [np.asarray(lowerCamelCase ) for speech_input in speech_inputs]
# Test feature size
__lowercase = feature_extractor(lowerCamelCase , padding="max_length" , return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
__lowercase = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features
__lowercase = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1e-3 ) )
# Test batched
__lowercase = feature_extractor(lowerCamelCase , return_tensors="np" ).input_features
__lowercase = feature_extractor(lowerCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
__lowercase = [floats_list((1, x) )[0] for x in (800, 800, 800)]
__lowercase = np.asarray(lowerCamelCase )
__lowercase = feature_extractor(lowerCamelCase , return_tensors="np" ).input_features
__lowercase = feature_extractor(lowerCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1e-3 ) )
# Test truncation required
__lowercase = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
__lowercase = [np.asarray(lowerCamelCase ) for speech_input in speech_inputs]
__lowercase = [x[: feature_extractor.n_samples] for x in speech_inputs]
__lowercase = [np.asarray(lowerCamelCase ) for speech_input in speech_inputs_truncated]
__lowercase = feature_extractor(lowerCamelCase , return_tensors="np" ).input_features
__lowercase = feature_extractor(lowerCamelCase , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1e-3 ) )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
import torch
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowercase = np.random.rand(100 , 32 ).astype(np.floataa )
__lowercase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__lowercase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
__lowercase = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def _snake_case ( self : str , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowercase = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
__lowercase = ds.sort("id" ).select(range(lowerCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
__lowercase = self._load_datasamples(1 )
__lowercase = WhisperFeatureExtractor()
__lowercase = feature_extractor(lowerCamelCase , return_tensors="pt" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , lowerCamelCase , atol=1e-4 ) )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowercase = self._load_datasamples(1 )[0]
__lowercase = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
__lowercase = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=lowerCamelCase )[0]
self.assertTrue(np.all(np.mean(lowerCamelCase ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCamelCase ) - 1 ) < 1e-3 ) )
| 655 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Optional[int] = logging.get_logger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = MobileNetVaConfig(layer_norm_eps=0.0_0_1 )
if "_quant" in model_name:
raise ValueError("Quantized models are not supported." )
__lowercase = re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" , _SCREAMING_SNAKE_CASE )
if matches:
__lowercase = float(matches[1] )
__lowercase = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
__lowercase = 1_0_0_1
__lowercase = "imagenet-1k-id2label.json"
__lowercase = "huggingface/label-files"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ) + 1: v for k, v in idalabel.items()}
__lowercase = "background"
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ( ):
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = get_mobilenet_va_config(_SCREAMING_SNAKE_CASE )
# Load 🤗 model
__lowercase = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
__lowercase = MobileNetVaImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size} , size={"shortest_edge": config.image_size + 3_2} , )
__lowercase = image_processor(images=prepare_img() , return_tensors="pt" )
__lowercase = model(**_SCREAMING_SNAKE_CASE )
__lowercase = outputs.logits
assert logits.shape == (1, 1_0_0_1)
if model_name == "mobilenet_v1_1.0_224":
__lowercase = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] )
elif model_name == "mobilenet_v1_0.75_192":
__lowercase = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3] )
else:
__lowercase = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("Pushing to the hub..." )
__lowercase = "google/" + model_name
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""mobilenet_v1_1.0_224""",
type=str,
help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt 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."""
)
snake_case__ : Dict = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 655 | 1 |
import os
import sys
import unittest
snake_case__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
snake_case__ : Optional[int] = os.path.join(git_repo_path, """src""", """transformers""")
snake_case__ : Tuple = """
{0} = None
"""
snake_case__ : List[str] = """
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
"""
snake_case__ : Dict = """
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = find_backend(" _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")" )
self.assertIsNone(lowerCamelCase )
__lowercase = find_backend(" if not is_tokenizers_available():" )
self.assertEqual(lowerCamelCase , "tokenizers" )
__lowercase = find_backend(" if not is_tensorflow_text_available():" )
self.assertEqual(lowerCamelCase , "tensorflow_text" )
__lowercase = find_backend(" if not (is_sentencepiece_available() and is_tokenizers_available()):" )
self.assertEqual(lowerCamelCase , "sentencepiece_and_tokenizers" )
__lowercase = find_backend(
" if not (is_sentencepiece_available() and is_tensorflow_text_available()):" )
self.assertEqual(lowerCamelCase , "sentencepiece_and_tensorflow_text" )
__lowercase = find_backend(
" if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):" )
self.assertEqual(lowerCamelCase , "sentencepiece_and_tokenizers_and_vision" )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowercase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" , lowerCamelCase )
self.assertIn("tensorflow_text" , lowerCamelCase )
self.assertIn("sentencepiece_and_tokenizers" , lowerCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn("BertModel" , objects["torch"] )
self.assertIn("TFBertModel" , objects["tf"] )
self.assertIn("FlaxBertModel" , objects["flax"] )
self.assertIn("BertModel" , objects["torch"] )
self.assertIn("TFBertTokenizer" , objects["tensorflow_text"] )
self.assertIn("convert_slow_tokenizer" , objects["sentencepiece_and_tokenizers"] )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowercase = create_dummy_object("CONSTANT" , "'torch'" )
self.assertEqual(lowerCamelCase , "\nCONSTANT = None\n" )
__lowercase = create_dummy_object("function" , "'torch'" )
self.assertEqual(
lowerCamelCase , "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
__lowercase = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n"
__lowercase = create_dummy_object("FakeClass" , "'torch'" )
self.assertEqual(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n"
__lowercase = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] , lowerCamelCase )
| 655 |
from __future__ import annotations
from typing import Any
class _A :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = num_of_nodes
__lowercase = []
__lowercase = {}
def _snake_case ( self : Dict , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def _snake_case ( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
__lowercase = self.find_component(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : list[int] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
__lowercase = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__lowercase = self.find_component(lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCamelCase )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = []
__lowercase = 0
__lowercase = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__lowercase = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__lowercase = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
__lowercase = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def snake_case_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("Input list must be a non empty list" )
if len(_SCREAMING_SNAKE_CASE ) == 1:
return True
__lowercase = series[1] - series[0]
for index in range(len(_SCREAMING_SNAKE_CASE ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("Input list must be a non empty list" )
__lowercase = 0
for val in series:
answer += val
return answer / len(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[str] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
snake_case__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = SwinvaConfig()
__lowercase = swinva_name.split("_" )
__lowercase = name_split[1]
if "to" in name_split[3]:
__lowercase = int(name_split[3][-3:] )
else:
__lowercase = int(name_split[3] )
if "to" in name_split[2]:
__lowercase = int(name_split[2][-2:] )
else:
__lowercase = int(name_split[2][6:] )
if model_size == "tiny":
__lowercase = 9_6
__lowercase = (2, 2, 6, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "small":
__lowercase = 9_6
__lowercase = (2, 2, 1_8, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "base":
__lowercase = 1_2_8
__lowercase = (2, 2, 1_8, 2)
__lowercase = (4, 8, 1_6, 3_2)
else:
__lowercase = 1_9_2
__lowercase = (2, 2, 1_8, 2)
__lowercase = (6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
__lowercase = (1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__lowercase = 2_1_8_4_1
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-22k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
else:
__lowercase = 1_0_0_0
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-1k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = img_size
__lowercase = num_classes
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
return config
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if "patch_embed.proj" in name:
__lowercase = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
__lowercase = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
__lowercase = "encoder." + name
if "attn.proj" in name:
__lowercase = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
__lowercase = name.replace("attn" , "attention.self" )
if "norm1" in name:
__lowercase = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
__lowercase = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
__lowercase = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
__lowercase = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
__lowercase = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
__lowercase = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
__lowercase = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
__lowercase = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
__lowercase = "layernorm.weight"
if name == "norm.bias":
__lowercase = "layernorm.bias"
if "head" in name:
__lowercase = name.replace("head" , "classifier" )
else:
__lowercase = "swinv2." + name
return name
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for key in orig_state_dict.copy().keys():
__lowercase = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
__lowercase = key.split("." )
__lowercase = int(key_split[1] )
__lowercase = int(key_split[3] )
__lowercase = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__lowercase = val[:dim, :]
__lowercase = val[dim : dim * 2, :]
__lowercase = val[-dim:, :]
else:
__lowercase = val[:dim]
__lowercase = val[
dim : dim * 2
]
__lowercase = val[-dim:]
else:
__lowercase = val
return orig_state_dict
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
__lowercase = get_swinva_config(_SCREAMING_SNAKE_CASE )
__lowercase = SwinvaForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
__lowercase = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
__lowercase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
__lowercase = timm_model(inputs["pixel_values"] )
__lowercase = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
model.push_to_hub(
repo_path_or_name=Path(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swinv2_name""",
default="""swinv2_tiny_patch4_window8_256""",
type=str,
help="""Name of the Swinv2 timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case__ : str = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 655 |
from __future__ import annotations
import bisect
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = 0
__lowercase = len(_SCREAMING_SNAKE_CASE ) - 1
while left <= right:
__lowercase = left + (right - left) // 2
__lowercase = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__lowercase = midpoint - 1
else:
__lowercase = midpoint + 1
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = bisect.bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
return index
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if right < left:
return None
__lowercase = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint - 1 )
else:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint + 1 , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Optional[Any] = input("""Enter numbers separated by comma:\n""").strip()
snake_case__ : Any = sorted(int(item) for item in user_input.split(""","""))
snake_case__ : Any = int(input("""Enter a single number to be found in the list:\n"""))
snake_case__ : List[Any] = binary_search(collection, target)
if result is None:
print(F'''{target} was not found in {collection}.''')
else:
print(F'''{target} was found at position {result} in {collection}.''')
| 655 | 1 |
from __future__ import annotations
class _A :
'''simple docstring'''
def __init__( self : List[str] , lowerCamelCase : str=None ):
'''simple docstring'''
__lowercase = data
__lowercase = None
def __repr__( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = []
__lowercase = self
while temp:
string_rep.append(f"""{temp.data}""" )
__lowercase = temp.next
return "->".join(lowerCamelCase )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if not elements_list:
raise Exception("The Elements List is empty" )
__lowercase = __lowercase = Node(elements_list[0] )
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = Node(elements_list[i] )
__lowercase = current.next
return head
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
print_reverse(head_node.next )
print(head_node.data )
def snake_case_ ( ):
from doctest import testmod
testmod()
__lowercase = make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
print("Linked List:" )
print(_SCREAMING_SNAKE_CASE )
print("Elements in Reverse:" )
print_reverse(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 655 |
import copy
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
from ..auto import CONFIG_MAPPING
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """conditional_detr"""
_snake_case : Union[str, Any] = ["""past_key_values"""]
_snake_case : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Optional[Any] , lowerCamelCase : int=True , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[int]=3 , lowerCamelCase : Optional[int]=300 , lowerCamelCase : List[Any]=6 , lowerCamelCase : str=2_048 , lowerCamelCase : Any=8 , lowerCamelCase : List[str]=6 , lowerCamelCase : Any=2_048 , lowerCamelCase : List[Any]=8 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : List[str]=0.0 , lowerCamelCase : List[Any]=True , lowerCamelCase : str="relu" , lowerCamelCase : int=256 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Dict=0.0 , lowerCamelCase : Tuple=0.02 , lowerCamelCase : int=1.0 , lowerCamelCase : Tuple=False , lowerCamelCase : List[str]="sine" , lowerCamelCase : List[Any]="resnet50" , lowerCamelCase : Any=True , lowerCamelCase : Any=False , lowerCamelCase : List[Any]=2 , lowerCamelCase : List[Any]=5 , lowerCamelCase : str=2 , lowerCamelCase : Dict=1 , lowerCamelCase : List[str]=1 , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Dict=5 , lowerCamelCase : List[Any]=2 , lowerCamelCase : Tuple=0.25 , **lowerCamelCase : List[str] , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
__lowercase = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = backbone_config.get("model_type" )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowerCamelCase )
__lowercase = use_timm_backbone
__lowercase = backbone_config
__lowercase = num_channels
__lowercase = num_queries
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = encoder_layers
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
__lowercase = backbone
__lowercase = use_pretrained_backbone
__lowercase = dilation
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = cls_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase , **lowerCamelCase )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _snake_case ( self : str ):
'''simple docstring'''
return self.d_model
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Any = version.parse("""1.11""" )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _snake_case ( self : Any ):
'''simple docstring'''
return 1e-5
@property
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
return 12
| 655 | 1 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
snake_case__ : int = """CompVis/stable-diffusion-v1-1"""
snake_case__ : Dict = """CompVis/stable-diffusion-v1-2"""
snake_case__ : Dict = """CompVis/stable-diffusion-v1-3"""
snake_case__ : Optional[Any] = """CompVis/stable-diffusion-v1-4"""
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : Tuple , lowerCamelCase : AutoencoderKL , lowerCamelCase : CLIPTextModel , lowerCamelCase : CLIPTokenizer , lowerCamelCase : UNetaDConditionModel , lowerCamelCase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowerCamelCase : StableDiffusionSafetyChecker , lowerCamelCase : CLIPImageProcessor , lowerCamelCase : bool = True , ):
'''simple docstring'''
super()._init_()
__lowercase = StableDiffusionPipeline.from_pretrained(lowerCamelCase )
__lowercase = StableDiffusionPipeline.from_pretrained(lowerCamelCase )
__lowercase = StableDiffusionPipeline.from_pretrained(lowerCamelCase )
__lowercase = StableDiffusionPipeline(
vae=lowerCamelCase , text_encoder=lowerCamelCase , tokenizer=lowerCamelCase , unet=lowerCamelCase , scheduler=lowerCamelCase , safety_checker=lowerCamelCase , feature_extractor=lowerCamelCase , requires_safety_checker=lowerCamelCase , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return {k: getattr(self , lowerCamelCase ) for k in self.config.keys() if not k.startswith("_" )}
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[Union[str, int]] = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__lowercase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase )
def _snake_case ( self : Tuple ):
'''simple docstring'''
self.enable_attention_slicing(lowerCamelCase )
@torch.no_grad()
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Union[str, List[str]] , lowerCamelCase : int = 512 , lowerCamelCase : int = 512 , lowerCamelCase : int = 50 , lowerCamelCase : float = 7.5 , lowerCamelCase : Optional[Union[str, List[str]]] = None , lowerCamelCase : Optional[int] = 1 , lowerCamelCase : float = 0.0 , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : Optional[torch.FloatTensor] = None , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , lowerCamelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase : int = 1 , **lowerCamelCase : Tuple , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
@torch.no_grad()
def _snake_case ( self : int , lowerCamelCase : Union[str, List[str]] , lowerCamelCase : int = 512 , lowerCamelCase : int = 512 , lowerCamelCase : int = 50 , lowerCamelCase : float = 7.5 , lowerCamelCase : Optional[Union[str, List[str]]] = None , lowerCamelCase : Optional[int] = 1 , lowerCamelCase : float = 0.0 , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : Optional[torch.FloatTensor] = None , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , lowerCamelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase : int = 1 , **lowerCamelCase : Any , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
@torch.no_grad()
def _snake_case ( self : List[Any] , lowerCamelCase : Union[str, List[str]] , lowerCamelCase : int = 512 , lowerCamelCase : int = 512 , lowerCamelCase : int = 50 , lowerCamelCase : float = 7.5 , lowerCamelCase : Optional[Union[str, List[str]]] = None , lowerCamelCase : Optional[int] = 1 , lowerCamelCase : float = 0.0 , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : Optional[torch.FloatTensor] = None , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , lowerCamelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase : int = 1 , **lowerCamelCase : Union[str, Any] , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
@torch.no_grad()
def _snake_case ( self : Any , lowerCamelCase : Union[str, List[str]] , lowerCamelCase : int = 512 , lowerCamelCase : int = 512 , lowerCamelCase : int = 50 , lowerCamelCase : float = 7.5 , lowerCamelCase : Optional[Union[str, List[str]]] = None , lowerCamelCase : Optional[int] = 1 , lowerCamelCase : float = 0.0 , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : Optional[torch.FloatTensor] = None , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , lowerCamelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase : int = 1 , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
return self.pipea(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
@torch.no_grad()
def _snake_case ( self : Optional[Any] , lowerCamelCase : Union[str, List[str]] , lowerCamelCase : int = 512 , lowerCamelCase : int = 512 , lowerCamelCase : int = 50 , lowerCamelCase : float = 7.5 , lowerCamelCase : Optional[Union[str, List[str]]] = None , lowerCamelCase : Optional[int] = 1 , lowerCamelCase : float = 0.0 , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : Optional[torch.FloatTensor] = None , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , lowerCamelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowerCamelCase : int = 1 , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = "cuda" if torch.cuda.is_available() else "cpu"
self.to(lowerCamelCase )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"""`height` and `width` must be divisible by 8 but are {height} and {width}.""" )
# Get first result from Stable Diffusion Checkpoint v1.1
__lowercase = self.textaimg_sda_a(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
# Get first result from Stable Diffusion Checkpoint v1.2
__lowercase = self.textaimg_sda_a(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
# Get first result from Stable Diffusion Checkpoint v1.3
__lowercase = self.textaimg_sda_a(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
# Get first result from Stable Diffusion Checkpoint v1.4
__lowercase = self.textaimg_sda_a(
prompt=lowerCamelCase , height=lowerCamelCase , width=lowerCamelCase , num_inference_steps=lowerCamelCase , guidance_scale=lowerCamelCase , negative_prompt=lowerCamelCase , num_images_per_prompt=lowerCamelCase , eta=lowerCamelCase , generator=lowerCamelCase , latents=lowerCamelCase , output_type=lowerCamelCase , return_dict=lowerCamelCase , callback=lowerCamelCase , callback_steps=lowerCamelCase , **lowerCamelCase , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 655 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Any = logging.get_logger(__name__)
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
_snake_case : Dict = """maskformer-swin"""
_snake_case : List[str] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : List[str] , lowerCamelCase : Any=224 , lowerCamelCase : Optional[Any]=4 , lowerCamelCase : Dict=3 , lowerCamelCase : Tuple=96 , lowerCamelCase : str=[2, 2, 6, 2] , lowerCamelCase : Dict=[3, 6, 12, 24] , lowerCamelCase : Optional[Any]=7 , lowerCamelCase : Any=4.0 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[str]=0.0 , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : int="gelu" , lowerCamelCase : Optional[int]=False , lowerCamelCase : List[Any]=0.02 , lowerCamelCase : Tuple=1e-5 , lowerCamelCase : Dict=None , lowerCamelCase : Dict=None , **lowerCamelCase : int , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(lowerCamelCase )
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = layer_norm_eps
__lowercase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowercase = int(embed_dim * 2 ** (len(lowerCamelCase ) - 1) )
__lowercase = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(lowerCamelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase , out_indices=lowerCamelCase , stage_names=self.stage_names )
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case__ : Optional[Any] = {
"""configuration_layoutlmv3""": [
"""LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LayoutLMv3Config""",
"""LayoutLMv3OnnxConfig""",
],
"""processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
"""tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Any = ["""LayoutLMv3TokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[str] = [
"""LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LayoutLMv3ForQuestionAnswering""",
"""LayoutLMv3ForSequenceClassification""",
"""LayoutLMv3ForTokenClassification""",
"""LayoutLMv3Model""",
"""LayoutLMv3PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[str] = [
"""TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLayoutLMv3ForQuestionAnswering""",
"""TFLayoutLMv3ForSequenceClassification""",
"""TFLayoutLMv3ForTokenClassification""",
"""TFLayoutLMv3Model""",
"""TFLayoutLMv3PreTrainedModel""",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[Any] = ["""LayoutLMv3FeatureExtractor"""]
snake_case__ : List[str] = ["""LayoutLMv3ImageProcessor"""]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
snake_case__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__lowercase = gray_code_sequence_string(_SCREAMING_SNAKE_CASE )
#
# convert them to integers
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = int(sequence[i] , 2 )
return sequence
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__lowercase = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__lowercase = gray_code_sequence_string(bit_count - 1 )
__lowercase = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__lowercase = "0" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__lowercase = "1" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = set()
# edges = list of graph's edges
__lowercase = get_edges(_SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__lowercase , __lowercase = edges.pop()
chosen_vertices.add(_SCREAMING_SNAKE_CASE )
chosen_vertices.add(_SCREAMING_SNAKE_CASE )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_SCREAMING_SNAKE_CASE )
return chosen_vertices
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 655 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=True ):
model.train()
__lowercase = model(_SCREAMING_SNAKE_CASE )
__lowercase = F.mse_loss(_SCREAMING_SNAKE_CASE , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
set_seed(4_2 )
__lowercase = RegressionModel()
__lowercase = deepcopy(_SCREAMING_SNAKE_CASE )
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
model.to(accelerator.device )
if sched:
__lowercase = AdamW(params=model.parameters() , lr=1E-3 )
__lowercase = AdamW(params=ddp_model.parameters() , lr=1E-3 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
# Make a copy of `model`
if sched:
__lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test when on a single CPU or GPU that the context manager does nothing
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test on distributed setup that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(_SCREAMING_SNAKE_CASE ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
GradientState._reset_state()
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
__lowercase = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE ))
if accelerator.num_processes > 1:
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
GradientState._reset_state()
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase = RegressionDataset(length=9_6 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if iteration < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if batch_num < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = accelerator.state
if state.local_process_index == 0:
print("**Test `accumulate` gradient accumulation with dataloader break**" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("**Test NOOP `no_sync` context manager**" )
test_noop_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("**Test Distributed `no_sync` context manager**" )
test_distributed_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("<" , "2.0" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 | 1 |
# Copyright 2021 The HuggingFace 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 pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
snake_case__ : List[str] = """Create a default config file for Accelerate with only a few flags set."""
def snake_case_ ( _SCREAMING_SNAKE_CASE="no" , _SCREAMING_SNAKE_CASE = default_json_config_file , _SCREAMING_SNAKE_CASE = False ):
__lowercase = Path(_SCREAMING_SNAKE_CASE )
path.parent.mkdir(parents=_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
if path.exists():
print(
F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
__lowercase = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
__lowercase = {
"compute_environment": "LOCAL_MACHINE",
"mixed_precision": mixed_precision,
}
if torch.cuda.is_available():
__lowercase = torch.cuda.device_count()
__lowercase = num_gpus
__lowercase = False
if num_gpus > 1:
__lowercase = "MULTI_GPU"
else:
__lowercase = "NO"
elif is_xpu_available() and use_xpu:
__lowercase = torch.xpu.device_count()
__lowercase = num_xpus
__lowercase = False
if num_xpus > 1:
__lowercase = "MULTI_XPU"
else:
__lowercase = "NO"
elif is_npu_available():
__lowercase = torch.npu.device_count()
__lowercase = num_npus
__lowercase = False
if num_npus > 1:
__lowercase = "MULTI_NPU"
else:
__lowercase = "NO"
else:
__lowercase = 0
__lowercase = True
__lowercase = 1
__lowercase = "NO"
__lowercase = ClusterConfig(**_SCREAMING_SNAKE_CASE )
config.to_json_file(_SCREAMING_SNAKE_CASE )
return path
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = parser.add_parser("default" , parents=_SCREAMING_SNAKE_CASE , help=_SCREAMING_SNAKE_CASE , formatter_class=_SCREAMING_SNAKE_CASE )
parser.add_argument(
"--config_file" , default=_SCREAMING_SNAKE_CASE , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , dest="save_location" , )
parser.add_argument(
"--mixed_precision" , choices=["no", "fp16", "bf16"] , type=_SCREAMING_SNAKE_CASE , help="Whether or not to use mixed precision training. "
"Choose between FP16 and BF16 (bfloat16) training. "
"BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later." , default="no" , )
parser.set_defaults(func=_SCREAMING_SNAKE_CASE )
return parser
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F"""accelerate configuration saved at {config_file}""" )
| 655 |
from ....utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : List[str] , lowerCamelCase : Any , lowerCamelCase : Dict=None , lowerCamelCase : Dict=2_048 ):
'''simple docstring'''
__lowercase = config.__dict__
__lowercase = modal_hidden_size
if num_labels:
__lowercase = num_labels
| 655 | 1 |
import baseaa
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
return baseaa.baaencode(string.encode("utf-8" ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
return baseaa.baadecode(_SCREAMING_SNAKE_CASE ).decode("utf-8" )
if __name__ == "__main__":
snake_case__ : int = """Hello World!"""
snake_case__ : Union[str, Any] = baseaa_encode(test)
print(encoded)
snake_case__ : Optional[Any] = baseaa_decode(encoded)
print(decoded)
| 655 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class _A ( _lowercase , _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Dict = StableUnCLIPImgaImgPipeline
_snake_case : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
_snake_case : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_snake_case : int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_snake_case : int = frozenset([] )
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = 32
__lowercase = embedder_hidden_size
# image encoding components
__lowercase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
__lowercase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowerCamelCase , projection_dim=lowerCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__lowercase = StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase )
__lowercase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowercase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowercase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCamelCase , layers_per_block=1 , upcast_attention=lowerCamelCase , use_linear_projection=lowerCamelCase , )
torch.manual_seed(0 )
__lowercase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=lowerCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
__lowercase = AutoencoderKL()
__lowercase = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def _snake_case ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : Any=0 , lowerCamelCase : Union[str, Any]=True ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
__lowercase = torch.manual_seed(lowerCamelCase )
else:
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
if pil_image:
__lowercase = input_image * 0.5 + 0.5
__lowercase = input_image.clamp(0 , 1 )
__lowercase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowercase = DiffusionPipeline.numpy_to_pil(lowerCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableUnCLIPImgaImgPipeline(**lowerCamelCase )
__lowercase = sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
inputs.update({"image_embeds": None} )
__lowercase = sd_pipe(**lowerCamelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _snake_case ( self : str ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase )
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = pipe(
lowerCamelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 655 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
snake_case__ : Optional[Any] = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
snake_case__ : List[str] = {
"""allenai/led-base-16384""": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def snake_case_ ( ):
__lowercase = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
__lowercase = bs[:]
__lowercase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
__lowercase = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
return pairs
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[str] = VOCAB_FILES_NAMES
_snake_case : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Optional[int]="replace" , lowerCamelCase : Dict="<s>" , lowerCamelCase : Dict="</s>" , lowerCamelCase : Optional[Any]="</s>" , lowerCamelCase : Any="<s>" , lowerCamelCase : List[str]="<unk>" , lowerCamelCase : Union[str, Any]="<pad>" , lowerCamelCase : Any="<mask>" , lowerCamelCase : str=False , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else bos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else eos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else sep_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else cls_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else unk_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else mask_token
super().__init__(
errors=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , add_prefix_space=lowerCamelCase , **lowerCamelCase , )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
__lowercase = json.load(lowerCamelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = errors # how to handle errors in decoding
__lowercase = bytes_to_unicode()
__lowercase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
__lowercase = merges_handle.read().split("\n" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in bpe_merges]
__lowercase = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__lowercase = {}
__lowercase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowercase = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return len(self.encoder )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : List[Any] , lowerCamelCase : str ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowercase = tuple(lowerCamelCase )
__lowercase = get_pairs(lowerCamelCase )
if not pairs:
return token
while True:
__lowercase = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(lowerCamelCase ):
try:
__lowercase = word.index(lowerCamelCase , lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase = j
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(lowerCamelCase )
__lowercase = new_word
if len(lowerCamelCase ) == 1:
break
else:
__lowercase = get_pairs(lowerCamelCase )
__lowercase = " ".join(lowerCamelCase )
__lowercase = word
return word
def _snake_case ( self : List[Any] , lowerCamelCase : Tuple ):
'''simple docstring'''
__lowercase = []
for token in re.findall(self.pat , lowerCamelCase ):
__lowercase = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : Dict , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : str , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.decoder.get(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = "".join(lowerCamelCase )
__lowercase = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
__lowercase = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
__lowercase = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : str , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase )) + [1]
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase )) + [1]
def _snake_case ( self : int , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [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 _snake_case ( self : Dict , lowerCamelCase : Any , lowerCamelCase : Tuple=False , **lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase ) > 0 and not text[0].isspace()):
__lowercase = " " + text
return (text, kwargs)
def _snake_case ( self : List[Any] , lowerCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCamelCase : Optional[int] = None , lowerCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[bool] = None , ):
'''simple docstring'''
__lowercase = super()._pad(
encoded_inputs=lowerCamelCase , max_length=lowerCamelCase , padding_strategy=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , )
# Load from model defaults
if return_attention_mask is None:
__lowercase = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__lowercase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__lowercase = len(encoded_inputs["global_attention_mask"] ) != len(lowerCamelCase )
if needs_to_be_padded:
__lowercase = len(lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__lowercase = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
__lowercase = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 655 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , *,
lowerCamelCase : int = 4 , lowerCamelCase : int = 768 , lowerCamelCase : int , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
super().__init__()
__lowercase = nn.Parameter(torch.zeros(lowerCamelCase ) )
# parameters for additional clip time embeddings
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
# parameters for encoder hidden states
__lowercase = clip_extra_context_tokens
__lowercase = nn.Linear(
lowerCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.LayerNorm(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , *, lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
__lowercase = image_embeddings.shape[0]
__lowercase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
__lowercase = classifier_free_guidance_embeddings.expand(
lowerCamelCase , -1 )
__lowercase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
__lowercase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
__lowercase = self.embedding_proj(lowerCamelCase )
__lowercase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase )
__lowercase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
__lowercase = self.clip_extra_context_tokens_proj(lowerCamelCase )
__lowercase = clip_extra_context_tokens.reshape(lowerCamelCase , -1 , self.clip_extra_context_tokens )
__lowercase = clip_extra_context_tokens.permute(0 , 2 , 1 )
__lowercase = self.encoder_hidden_states_proj(lowerCamelCase )
__lowercase = self.text_encoder_hidden_states_norm(lowerCamelCase )
__lowercase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 655 | 1 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if is_torch_version("<" , "2.0.0" ) or not hasattr(_SCREAMING_SNAKE_CASE , "_dynamo" ):
return False
return isinstance(_SCREAMING_SNAKE_CASE , torch._dynamo.eval_frame.OptimizedModule )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True ):
__lowercase = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__lowercase = is_compiled_module(_SCREAMING_SNAKE_CASE )
if is_compiled:
__lowercase = model
__lowercase = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = model.module
if not keep_fpaa_wrapper:
__lowercase = getattr(_SCREAMING_SNAKE_CASE , "forward" )
__lowercase = model.__dict__.pop("_original_forward" , _SCREAMING_SNAKE_CASE )
if original_forward is not None:
while hasattr(_SCREAMING_SNAKE_CASE , "__wrapped__" ):
__lowercase = forward.__wrapped__
if forward == original_forward:
break
__lowercase = forward
if getattr(_SCREAMING_SNAKE_CASE , "_converted_to_transformer_engine" , _SCREAMING_SNAKE_CASE ):
convert_model(_SCREAMING_SNAKE_CASE , to_transformer_engine=_SCREAMING_SNAKE_CASE )
if is_compiled:
__lowercase = model
__lowercase = compiled_model
return model
def snake_case_ ( ):
PartialState().wait_for_everyone()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif PartialState().local_process_index == 0:
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@contextmanager
def snake_case_ ( **_SCREAMING_SNAKE_CASE ):
for key, value in kwargs.items():
__lowercase = str(_SCREAMING_SNAKE_CASE )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if not hasattr(_SCREAMING_SNAKE_CASE , "__qualname__" ) and not hasattr(_SCREAMING_SNAKE_CASE , "__name__" ):
__lowercase = getattr(_SCREAMING_SNAKE_CASE , "__class__" , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , "__qualname__" ):
return obj.__qualname__
if hasattr(_SCREAMING_SNAKE_CASE , "__name__" ):
return obj.__name__
return str(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for key, value in source.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = destination.setdefault(_SCREAMING_SNAKE_CASE , {} )
merge_dicts(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__lowercase = value
return destination
def snake_case_ ( _SCREAMING_SNAKE_CASE = None ):
if port is None:
__lowercase = 2_9_5_0_0
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 655 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case__ : Union[str, Any] = TypeVar("""T""")
snake_case__ : Optional[int] = TypeVar("""U""")
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : T | None , lowerCamelCase : U | None ):
'''simple docstring'''
__lowercase = key
__lowercase = val
__lowercase = None
__lowercase = None
def __repr__( self : Any ):
'''simple docstring'''
return (
f"""Node: key: {self.key}, val: {self.val}, """
f"""has next: {bool(self.next )}, has prev: {bool(self.prev )}"""
)
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase , __lowercase = self.rear, self.head
def __repr__( self : Optional[Any] ):
'''simple docstring'''
__lowercase = ["DoubleLinkedList"]
__lowercase = self.head
while node.next is not None:
rep.append(str(lowerCamelCase ) )
__lowercase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
__lowercase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
__lowercase = node
__lowercase = previous
__lowercase = node
__lowercase = self.rear
def _snake_case ( self : Optional[int] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
if node.prev is None or node.next is None:
return None
__lowercase = node.next
__lowercase = node.prev
__lowercase = None
__lowercase = None
return node
class _A ( Generic[T, U] ):
'''simple docstring'''
_snake_case : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = DoubleLinkedList()
__lowercase = capacity
__lowercase = 0
__lowercase = 0
__lowercase = 0
__lowercase = {}
def __repr__( self : Optional[Any] ):
'''simple docstring'''
return (
f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
f"""capacity={self.capacity}, current size={self.num_keys})"""
)
def __contains__( self : Dict , lowerCamelCase : T ):
'''simple docstring'''
return key in self.cache
def _snake_case ( self : List[Any] , lowerCamelCase : T ):
'''simple docstring'''
if key in self.cache:
self.hits += 1
__lowercase = self.cache[key]
__lowercase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowerCamelCase )
return node.val
self.miss += 1
return None
def _snake_case ( self : Union[str, Any] , lowerCamelCase : T , lowerCamelCase : U ):
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
__lowercase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowerCamelCase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
__lowercase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
__lowercase = value
self.list.add(lowerCamelCase )
@classmethod
def _snake_case ( cls : Union[str, Any] , lowerCamelCase : int = 128 ):
'''simple docstring'''
def cache_decorator_inner(lowerCamelCase : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*lowerCamelCase : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
__lowercase = LRUCache(lowerCamelCase )
__lowercase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
__lowercase = func(*lowerCamelCase )
cls.decorator_function_to_instance_map[func].put(args[0] , lowerCamelCase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowerCamelCase , "cache_info" , lowerCamelCase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
snake_case__ : Optional[Any] = logging.getLogger()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = "\n".join(_SCREAMING_SNAKE_CASE )
Path(_SCREAMING_SNAKE_CASE ).open("w" ).writelines(_SCREAMING_SNAKE_CASE )
snake_case__ : List[str] = """patrickvonplaten/t5-tiny-random"""
snake_case__ : int = """sshleifer/bart-tiny-random"""
snake_case__ : Union[str, Any] = """sshleifer/tiny-mbart"""
snake_case__ : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : str , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(lowerCamelCase , lowerCamelCase )
__lowercase = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_generate()
assert Path(lowerCamelCase ).exists()
# os.remove(Path(output_file_name))
def _snake_case ( self : Dict ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
__lowercase = Path(self.get_auto_remove_tmp_dir() )
__lowercase = str(tmp_dir / "scores.json" )
__lowercase = str(tmp_dir / "val.target" )
_dump_articles(lowerCamelCase , text["en"] )
_dump_articles(lowerCamelCase , text["de"] )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{str(lowerCamelCase )}
{str(lowerCamelCase )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
with CaptureStdout() as cs:
run_search()
__lowercase = [" num_beams | length_penalty", model, "Best score args"]
__lowercase = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(lowerCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(lowerCamelCase ).exists()
os.remove(Path(lowerCamelCase ) )
| 655 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
snake_case__ : Optional[Any] = logging.getLogger()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = "\n".join(_SCREAMING_SNAKE_CASE )
Path(_SCREAMING_SNAKE_CASE ).open("w" ).writelines(_SCREAMING_SNAKE_CASE )
snake_case__ : List[str] = """patrickvonplaten/t5-tiny-random"""
snake_case__ : int = """sshleifer/bart-tiny-random"""
snake_case__ : Union[str, Any] = """sshleifer/tiny-mbart"""
snake_case__ : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : str , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(lowerCamelCase , lowerCamelCase )
__lowercase = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_generate()
assert Path(lowerCamelCase ).exists()
# os.remove(Path(output_file_name))
def _snake_case ( self : Dict ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
__lowercase = Path(self.get_auto_remove_tmp_dir() )
__lowercase = str(tmp_dir / "scores.json" )
__lowercase = str(tmp_dir / "val.target" )
_dump_articles(lowerCamelCase , text["en"] )
_dump_articles(lowerCamelCase , text["de"] )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{str(lowerCamelCase )}
{str(lowerCamelCase )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
with CaptureStdout() as cs:
run_search()
__lowercase = [" num_beams | length_penalty", model, "Best score args"]
__lowercase = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(lowerCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(lowerCamelCase ).exists()
os.remove(Path(lowerCamelCase ) )
| 655 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def snake_case_ ( _SCREAMING_SNAKE_CASE="ro" , _SCREAMING_SNAKE_CASE="en" , _SCREAMING_SNAKE_CASE="wmt16" , _SCREAMING_SNAKE_CASE=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install datasets" )
__lowercase = F"""{src_lang}-{tgt_lang}"""
print(F"""Converting {dataset}-{pair}""" )
__lowercase = datasets.load_dataset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if save_dir is None:
__lowercase = F"""{dataset}-{pair}"""
__lowercase = Path(_SCREAMING_SNAKE_CASE )
save_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
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
__lowercase = "val" if split == "validation" else split
__lowercase = save_dir.joinpath(F"""{fn}.source""" )
__lowercase = save_dir.joinpath(F"""{fn}.target""" )
__lowercase = src_path.open("w+" )
__lowercase = 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] ):
__lowercase = 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)
| 655 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _A :
'''simple docstring'''
_snake_case : int
_snake_case : TreeNode | None = None
_snake_case : TreeNode | None = None
snake_case__ : Dict = namedtuple("""CoinsDistribResult""", """moves excess""")
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if root is None:
return 0
# Validation
def count_nodes(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_SCREAMING_SNAKE_CASE ) != count_coins(_SCREAMING_SNAKE_CASE ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(_SCREAMING_SNAKE_CASE ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
__lowercase , __lowercase = get_distrib(node.left )
__lowercase , __lowercase = get_distrib(node.right )
__lowercase = 1 - left_distrib_excess
__lowercase = 1 - right_distrib_excess
__lowercase = (
left_distrib_moves
+ right_distrib_moves
+ abs(_SCREAMING_SNAKE_CASE )
+ abs(_SCREAMING_SNAKE_CASE )
)
__lowercase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return get_distrib(_SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
snake_case__ : Dict = 20_48
snake_case__ : str = 40_96
snake_case__ : List[str] = 42
snake_case__ : Dict = os.environ.pop("""PROCESS_TRAIN""", """false""")
snake_case__ : Optional[Any] = {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
def choose_first(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) == 1:
__lowercase = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
__lowercase = {k: [a[k]] for k in a}
if len(a["start_token"] ) > 0:
break
return a
__lowercase = {"id": example["id"]}
__lowercase = example["annotations"]
__lowercase = annotation["yes_no_answer"]
if 0 in yes_no_answer or 1 in yes_no_answer:
__lowercase = ["yes"] if 1 in yes_no_answer else ["no"]
__lowercase = __lowercase = []
__lowercase = __lowercase = []
__lowercase = ["<cls>"]
else:
__lowercase = ["short"]
__lowercase = choose_first(annotation["short_answers"] )
if len(out["start_token"] ) == 0:
# answer will be long if short is not available
__lowercase = ["long"]
__lowercase = choose_first(annotation["long_answer"] , is_long_answer=_SCREAMING_SNAKE_CASE )
__lowercase = []
answer.update(_SCREAMING_SNAKE_CASE )
# disregard some samples
if len(answer["start_token"] ) > 1 or answer["start_token"] == answer["end_token"]:
__lowercase = True
else:
__lowercase = False
__lowercase = ["start_token", "end_token", "start_byte", "end_byte", "text"]
if not all(isinstance(answer[k] , _SCREAMING_SNAKE_CASE ) for k in cols ):
raise ValueError("Issue in ID" , example["id"] )
return answer
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = _get_single_answer(_SCREAMING_SNAKE_CASE )
# 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
__lowercase = example["document"]["tokens"]
__lowercase = []
for i in range(len(doc["token"] ) ):
if not doc["is_html"][i]:
context.append(doc["token"][i] )
return {
"context": " ".join(_SCREAMING_SNAKE_CASE ),
"answer": {
"start_token": -1_0_0, # ignore index in cross-entropy
"end_token": -1_0_0, # 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
__lowercase = ["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
__lowercase = example["document"]["tokens"]
__lowercase = answer["start_token"]
__lowercase = answer["end_token"]
__lowercase = []
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
__lowercase = " ".join(context[start_token:end_token] )
# checking above code
if assertion:
__lowercase = doc["is_html"][answer["start_token"] : answer["end_token"]]
__lowercase = doc["token"][answer["start_token"] : answer["end_token"]]
__lowercase = " ".join([old[i] for i in range(len(_SCREAMING_SNAKE_CASE ) ) if not is_html[i]] )
if new != old:
print("ID:" , example["id"] )
print("New:" , _SCREAMING_SNAKE_CASE , end="\n" )
print("Old:" , _SCREAMING_SNAKE_CASE , end="\n\n" )
return {
"context": " ".join(_SCREAMING_SNAKE_CASE ),
"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 snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=2_0_4_8 , _SCREAMING_SNAKE_CASE=4_0_9_6 , _SCREAMING_SNAKE_CASE=True ):
# overlap will be of doc_stride - q_len
__lowercase = get_context_and_ans(_SCREAMING_SNAKE_CASE , assertion=_SCREAMING_SNAKE_CASE )
__lowercase = 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"],
},
}
__lowercase = tokenizer(example["question"]["text"] , out["context"] ).input_ids
__lowercase = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
__lowercase = []
__lowercase = []
__lowercase = input_ids[:q_len]
__lowercase = range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) , max_length - doc_stride )
for i in doc_start_indices:
__lowercase = i + max_length - q_len
__lowercase = 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": [-1_0_0] * len(_SCREAMING_SNAKE_CASE ),
"end_token": [-1_0_0] * len(_SCREAMING_SNAKE_CASE ),
"category": category,
},
}
__lowercase = out["context"].split()
__lowercase = splitted_context[answer["end_token"]]
__lowercase = len(
tokenizer(
" ".join(splitted_context[: answer["start_token"]] ) , add_special_tokens=_SCREAMING_SNAKE_CASE , ).input_ids )
__lowercase = len(
tokenizer(" ".join(splitted_context[: answer["end_token"]] ) , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
__lowercase = len(tokenizer(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
__lowercase = input_ids[answer["start_token"] : answer["end_token"] + 1] # right & left are inclusive
__lowercase = answer["start_token"]
__lowercase = answer["end_token"]
if assertion:
__lowercase = tokenizer.decode(_SCREAMING_SNAKE_CASE )
if answer["span"] != new:
print("ISSUE IN TOKENIZATION" )
print("OLD:" , answer["span"] )
print("NEW:" , _SCREAMING_SNAKE_CASE , end="\n\n" )
if len(_SCREAMING_SNAKE_CASE ) <= 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"],
},
}
__lowercase = input_ids[:q_len]
__lowercase = range(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) , max_length - doc_stride )
__lowercase = []
__lowercase = []
__lowercase = []
__lowercase = [] # null, yes, no, long, short
for i in doc_start_indices:
__lowercase = i + max_length - q_len
__lowercase = 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:
__lowercase = start_token - i + q_len
__lowercase = end_token - i + q_len
answers_category.append(answer["category"][0] ) # ["short"] -> "short"
else:
__lowercase = -1_0_0
__lowercase = -1_0_0
answers_category.append("null" )
__lowercase = inputs[-1][start_token : end_token + 1]
answers_start_token.append(_SCREAMING_SNAKE_CASE )
answers_end_token.append(_SCREAMING_SNAKE_CASE )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("ISSUE in strided for ID:" , example["id"] )
print("New:" , tokenizer.decode(_SCREAMING_SNAKE_CASE ) )
print("Old:" , tokenizer.decode(_SCREAMING_SNAKE_CASE ) , 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 snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=2_0_4_8 , _SCREAMING_SNAKE_CASE=4_0_9_6 , _SCREAMING_SNAKE_CASE=False ):
__lowercase = get_strided_contexts_and_ans(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , doc_stride=_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , assertion=_SCREAMING_SNAKE_CASE , )
return example
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
with jsonlines.open(_SCREAMING_SNAKE_CASE , "a" ) as writer:
for example in tqdm(_SCREAMING_SNAKE_CASE , total=len(_SCREAMING_SNAKE_CASE ) , desc="Saving samples ... " ):
__lowercase = 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
snake_case__ : Union[str, Any] = load_dataset("""natural_questions""")
snake_case__ : Union[str, Any] = BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")
snake_case__ : List[Any] = data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]
snake_case__ : List[str] = {
"""tokenizer""": tokenizer,
"""doc_stride""": DOC_STRIDE,
"""max_length""": MAX_LENGTH,
"""assertion""": False,
}
snake_case__ : str = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
snake_case__ : Dict = data.remove_columns(["""annotations""", """document""", """id""", """question"""])
print(data)
np.random.seed(SEED)
snake_case__ : str = """nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
save_to_disk(data, file_name=cache_file_name)
| 655 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = SwinvaConfig()
__lowercase = swinva_name.split("_" )
__lowercase = name_split[1]
if "to" in name_split[3]:
__lowercase = int(name_split[3][-3:] )
else:
__lowercase = int(name_split[3] )
if "to" in name_split[2]:
__lowercase = int(name_split[2][-2:] )
else:
__lowercase = int(name_split[2][6:] )
if model_size == "tiny":
__lowercase = 9_6
__lowercase = (2, 2, 6, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "small":
__lowercase = 9_6
__lowercase = (2, 2, 1_8, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "base":
__lowercase = 1_2_8
__lowercase = (2, 2, 1_8, 2)
__lowercase = (4, 8, 1_6, 3_2)
else:
__lowercase = 1_9_2
__lowercase = (2, 2, 1_8, 2)
__lowercase = (6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
__lowercase = (1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__lowercase = 2_1_8_4_1
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-22k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
else:
__lowercase = 1_0_0_0
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-1k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = img_size
__lowercase = num_classes
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
return config
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if "patch_embed.proj" in name:
__lowercase = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
__lowercase = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
__lowercase = "encoder." + name
if "attn.proj" in name:
__lowercase = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
__lowercase = name.replace("attn" , "attention.self" )
if "norm1" in name:
__lowercase = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
__lowercase = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
__lowercase = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
__lowercase = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
__lowercase = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
__lowercase = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
__lowercase = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
__lowercase = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
__lowercase = "layernorm.weight"
if name == "norm.bias":
__lowercase = "layernorm.bias"
if "head" in name:
__lowercase = name.replace("head" , "classifier" )
else:
__lowercase = "swinv2." + name
return name
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for key in orig_state_dict.copy().keys():
__lowercase = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
__lowercase = key.split("." )
__lowercase = int(key_split[1] )
__lowercase = int(key_split[3] )
__lowercase = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__lowercase = val[:dim, :]
__lowercase = val[dim : dim * 2, :]
__lowercase = val[-dim:, :]
else:
__lowercase = val[:dim]
__lowercase = val[
dim : dim * 2
]
__lowercase = val[-dim:]
else:
__lowercase = val
return orig_state_dict
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
__lowercase = get_swinva_config(_SCREAMING_SNAKE_CASE )
__lowercase = SwinvaForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
__lowercase = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
__lowercase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
__lowercase = timm_model(inputs["pixel_values"] )
__lowercase = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
model.push_to_hub(
repo_path_or_name=Path(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swinv2_name""",
default="""swinv2_tiny_patch4_window8_256""",
type=str,
help="""Name of the Swinv2 timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case__ : str = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 655 | 1 |
from collections.abc import Generator
from math import sin
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) != 3_2:
raise ValueError("Input must be of length 32" )
__lowercase = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if i < 0:
raise ValueError("Input must be non-negative" )
__lowercase = format(_SCREAMING_SNAKE_CASE , "08x" )[-8:]
__lowercase = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = b""
for char in message:
bit_string += format(_SCREAMING_SNAKE_CASE , "08b" ).encode("utf-8" )
__lowercase = format(len(_SCREAMING_SNAKE_CASE ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(_SCREAMING_SNAKE_CASE ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) % 5_1_2 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(_SCREAMING_SNAKE_CASE ) , 5_1_2 ):
__lowercase = bit_string[pos : pos + 5_1_2]
__lowercase = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if i < 0:
raise ValueError("Input must be non-negative" )
__lowercase = format(_SCREAMING_SNAKE_CASE , "032b" )
__lowercase = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(_SCREAMING_SNAKE_CASE , 2 )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return (a + b) % 2**3_2
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = preprocess(_SCREAMING_SNAKE_CASE )
__lowercase = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
__lowercase = 0x6_7_4_5_2_3_0_1
__lowercase = 0xe_f_c_d_a_b_8_9
__lowercase = 0x9_8_b_a_d_c_f_e
__lowercase = 0x1_0_3_2_5_4_7_6
__lowercase = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(_SCREAMING_SNAKE_CASE ):
__lowercase = aa
__lowercase = ba
__lowercase = ca
__lowercase = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
__lowercase = d ^ (b & (c ^ d))
__lowercase = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
__lowercase = c ^ (d & (b ^ c))
__lowercase = (5 * i + 1) % 1_6
elif i <= 4_7:
__lowercase = b ^ c ^ d
__lowercase = (3 * i + 5) % 1_6
else:
__lowercase = c ^ (b | not_aa(_SCREAMING_SNAKE_CASE ))
__lowercase = (7 * i) % 1_6
__lowercase = (f + a + added_consts[i] + block_words[g]) % 2**3_2
__lowercase = d
__lowercase = c
__lowercase = b
__lowercase = sum_aa(_SCREAMING_SNAKE_CASE , left_rotate_aa(_SCREAMING_SNAKE_CASE , shift_amounts[i] ) )
# Add hashed chunk to running total
__lowercase = sum_aa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = sum_aa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = sum_aa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = sum_aa(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = reformat_hex(_SCREAMING_SNAKE_CASE ) + reformat_hex(_SCREAMING_SNAKE_CASE ) + reformat_hex(_SCREAMING_SNAKE_CASE ) + reformat_hex(_SCREAMING_SNAKE_CASE )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
snake_case__ : Optional[Any] = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
snake_case__ : List[str] = {
"""allenai/led-base-16384""": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def snake_case_ ( ):
__lowercase = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
__lowercase = bs[:]
__lowercase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
__lowercase = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
return pairs
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[str] = VOCAB_FILES_NAMES
_snake_case : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Optional[int]="replace" , lowerCamelCase : Dict="<s>" , lowerCamelCase : Dict="</s>" , lowerCamelCase : Optional[Any]="</s>" , lowerCamelCase : Any="<s>" , lowerCamelCase : List[str]="<unk>" , lowerCamelCase : Union[str, Any]="<pad>" , lowerCamelCase : Any="<mask>" , lowerCamelCase : str=False , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else bos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else eos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else sep_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else cls_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else unk_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else mask_token
super().__init__(
errors=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , add_prefix_space=lowerCamelCase , **lowerCamelCase , )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
__lowercase = json.load(lowerCamelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = errors # how to handle errors in decoding
__lowercase = bytes_to_unicode()
__lowercase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
__lowercase = merges_handle.read().split("\n" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in bpe_merges]
__lowercase = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__lowercase = {}
__lowercase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowercase = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return len(self.encoder )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : List[Any] , lowerCamelCase : str ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowercase = tuple(lowerCamelCase )
__lowercase = get_pairs(lowerCamelCase )
if not pairs:
return token
while True:
__lowercase = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(lowerCamelCase ):
try:
__lowercase = word.index(lowerCamelCase , lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase = j
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(lowerCamelCase )
__lowercase = new_word
if len(lowerCamelCase ) == 1:
break
else:
__lowercase = get_pairs(lowerCamelCase )
__lowercase = " ".join(lowerCamelCase )
__lowercase = word
return word
def _snake_case ( self : List[Any] , lowerCamelCase : Tuple ):
'''simple docstring'''
__lowercase = []
for token in re.findall(self.pat , lowerCamelCase ):
__lowercase = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : Dict , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : str , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.decoder.get(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = "".join(lowerCamelCase )
__lowercase = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
__lowercase = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
__lowercase = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : str , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase )) + [1]
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase )) + [1]
def _snake_case ( self : int , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [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 _snake_case ( self : Dict , lowerCamelCase : Any , lowerCamelCase : Tuple=False , **lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase ) > 0 and not text[0].isspace()):
__lowercase = " " + text
return (text, kwargs)
def _snake_case ( self : List[Any] , lowerCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCamelCase : Optional[int] = None , lowerCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[bool] = None , ):
'''simple docstring'''
__lowercase = super()._pad(
encoded_inputs=lowerCamelCase , max_length=lowerCamelCase , padding_strategy=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , )
# Load from model defaults
if return_attention_mask is None:
__lowercase = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__lowercase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__lowercase = len(encoded_inputs["global_attention_mask"] ) != len(lowerCamelCase )
if needs_to_be_padded:
__lowercase = len(lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__lowercase = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
__lowercase = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 655 | 1 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _A ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : List[Any] = IFImgaImgSuperResolutionPipeline
_snake_case : Union[str, Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
_snake_case : Any = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""} )
_snake_case : Any = PipelineTesterMixin.required_optional_params - {"""latents"""}
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
return self._get_superresolution_dummy_components()
def _snake_case ( self : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : str=0 ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
__lowercase = torch.manual_seed(lowerCamelCase )
else:
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
__lowercase = floats_tensor((1, 3, 16, 16) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
__lowercase = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _snake_case ( self : str ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _snake_case ( self : Tuple ):
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def _snake_case ( self : Dict ):
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _snake_case ( self : List[str] ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _snake_case ( self : int ):
'''simple docstring'''
self._test_save_load_local()
def _snake_case ( self : Dict ):
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zero." )
if any(p < 0 for p in profit ):
raise ValueError("Profit can not be negative." )
if any(w < 0 for w in weight ):
raise ValueError("Weight can not be negative." )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
__lowercase = [p / w for p, w in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
# Creating a copy of the list and sorting profit/weight in ascending order
__lowercase = sorted(_SCREAMING_SNAKE_CASE )
# declaring useful variables
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = 0
__lowercase = 0
__lowercase = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
__lowercase = sorted_profit_by_weight[length - i - 1]
__lowercase = profit_by_weight.index(_SCREAMING_SNAKE_CASE )
__lowercase = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"""Input profits, weights, and then max_weight (all positive ints) separated by """
"""spaces."""
)
snake_case__ : str = [int(x) for x in input("""Input profits separated by spaces: """).split()]
snake_case__ : str = [int(x) for x in input("""Input weights separated by spaces: """).split()]
snake_case__ : Optional[Any] = int(input("""Max weight allowed: """))
# Function Call
calc_profit(profit, weight, max_weight)
| 655 | 1 |
from __future__ import annotations
import bisect
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = 0
__lowercase = len(_SCREAMING_SNAKE_CASE ) - 1
while left <= right:
__lowercase = left + (right - left) // 2
__lowercase = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__lowercase = midpoint - 1
else:
__lowercase = midpoint + 1
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = bisect.bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
return index
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if right < left:
return None
__lowercase = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint - 1 )
else:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint + 1 , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Optional[Any] = input("""Enter numbers separated by comma:\n""").strip()
snake_case__ : Any = sorted(int(item) for item in user_input.split(""","""))
snake_case__ : Any = int(input("""Enter a single number to be found in the list:\n"""))
snake_case__ : List[Any] = binary_search(collection, target)
if result is None:
print(F'''{target} was not found in {collection}.''')
else:
print(F'''{target} was found at position {result} in {collection}.''')
| 655 |
# 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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """openai/whisper-base"""
_snake_case : Union[str, Any] = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
_snake_case : Any = """transcriber"""
_snake_case : Any = WhisperProcessor
_snake_case : Optional[int] = WhisperForConditionalGeneration
_snake_case : str = ["""audio"""]
_snake_case : Optional[int] = ["""text"""]
def _snake_case ( self : List[str] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.pre_processor(lowerCamelCase , return_tensors="pt" ).input_features
def _snake_case ( self : str , lowerCamelCase : List[Any] ):
'''simple docstring'''
return self.model.generate(inputs=lowerCamelCase )
def _snake_case ( self : List[str] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(lowerCamelCase , skip_special_tokens=lowerCamelCase )[0]
| 655 | 1 |
from math import sqrt
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
__lowercase = True
# 0 and 1 are none primes.
if number <= 1:
__lowercase = False
for divisor in range(2 , int(round(sqrt(_SCREAMING_SNAKE_CASE ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
__lowercase = False
break
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'status' must been from type bool"
return status
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
__lowercase = list(range(2 , n + 1 ) )
__lowercase = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
for j in range(i + 1 , len(_SCREAMING_SNAKE_CASE ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
__lowercase = 0
# filters actual prime numbers.
__lowercase = [x for x in begin_list if x != 0]
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'ans' must been from type list"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (n > 2), "'N' must been an int and > 2"
__lowercase = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(_SCREAMING_SNAKE_CASE ):
ans.append(_SCREAMING_SNAKE_CASE )
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'ans' must been from type list"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and number >= 0, "'number' must been an int and >= 0"
__lowercase = [] # this list will be returns of the function.
# potential prime number factors.
__lowercase = 2
__lowercase = number
if number == 0 or number == 1:
ans.append(_SCREAMING_SNAKE_CASE )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(_SCREAMING_SNAKE_CASE ):
while quotient != 1:
if is_prime(_SCREAMING_SNAKE_CASE ) and (quotient % factor == 0):
ans.append(_SCREAMING_SNAKE_CASE )
quotient /= factor
else:
factor += 1
else:
ans.append(_SCREAMING_SNAKE_CASE )
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'ans' must been from type list"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' bust been an int and >= 0"
__lowercase = 0
# prime factorization of 'number'
__lowercase = prime_factorization(_SCREAMING_SNAKE_CASE )
__lowercase = max(_SCREAMING_SNAKE_CASE )
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'ans' must been from type int"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' bust been an int and >= 0"
__lowercase = 0
# prime factorization of 'number'
__lowercase = prime_factorization(_SCREAMING_SNAKE_CASE )
__lowercase = min(_SCREAMING_SNAKE_CASE )
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'ans' must been from type int"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'number' must been an int"
assert isinstance(number % 2 == 0 , _SCREAMING_SNAKE_CASE ), "compare bust been from type bool"
return number % 2 == 0
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ), "'number' must been an int"
assert isinstance(number % 2 != 0 , _SCREAMING_SNAKE_CASE ), "compare bust been from type bool"
return number % 2 != 0
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (number > 2) and is_even(_SCREAMING_SNAKE_CASE )
), "'number' must been an int, even and > 2"
__lowercase = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
__lowercase = get_prime_numbers(_SCREAMING_SNAKE_CASE )
__lowercase = len(_SCREAMING_SNAKE_CASE )
# run variable for while-loops.
__lowercase = 0
__lowercase = None
# exit variable. for break up the loops
__lowercase = True
while i < len_pn and loop:
__lowercase = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
__lowercase = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and (len(_SCREAMING_SNAKE_CASE ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
__lowercase = 0
while numbera != 0:
__lowercase = numbera % numbera
__lowercase = numbera
__lowercase = rest
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
__lowercase = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
__lowercase = prime_factorization(_SCREAMING_SNAKE_CASE )
__lowercase = prime_factorization(_SCREAMING_SNAKE_CASE )
elif numbera == 1 or numbera == 1:
__lowercase = []
__lowercase = []
__lowercase = max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = 0
__lowercase = 0
__lowercase = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
__lowercase = prime_fac_a.count(_SCREAMING_SNAKE_CASE )
__lowercase = prime_fac_a.count(_SCREAMING_SNAKE_CASE )
for _ in range(max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ):
ans *= n
else:
__lowercase = prime_fac_a.count(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
ans *= n
done.append(_SCREAMING_SNAKE_CASE )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
__lowercase = prime_fac_a.count(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
ans *= n
done.append(_SCREAMING_SNAKE_CASE )
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (n >= 0), "'number' must been a positive int"
__lowercase = 0
__lowercase = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(_SCREAMING_SNAKE_CASE ):
ans += 1
# precondition
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and is_prime(
_SCREAMING_SNAKE_CASE ), "'ans' must been a prime number and from type int"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert (
is_prime(_SCREAMING_SNAKE_CASE ) and is_prime(_SCREAMING_SNAKE_CASE ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
__lowercase = p_number_a + 1 # jump to the next number
__lowercase = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(_SCREAMING_SNAKE_CASE ):
number += 1
while number < p_number_a:
ans.append(_SCREAMING_SNAKE_CASE )
number += 1
# fetch the next prime number.
while not is_prime(_SCREAMING_SNAKE_CASE ):
number += 1
# precondition
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and ans[0] != p_number_a
and ans[len(_SCREAMING_SNAKE_CASE ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (n >= 1), "'n' must been int and >= 1"
__lowercase = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(_SCREAMING_SNAKE_CASE )
# precondition
assert ans[0] == 1 and ans[len(_SCREAMING_SNAKE_CASE ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number > 1
), "'number' must been an int and >= 1"
__lowercase = get_divisors(_SCREAMING_SNAKE_CASE )
# precondition
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and (divisors[0] == 1)
and (divisors[len(_SCREAMING_SNAKE_CASE ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
__lowercase = gcd(abs(_SCREAMING_SNAKE_CASE ) , abs(_SCREAMING_SNAKE_CASE ) )
# precondition
assert (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (n >= 0), "'n' must been a int and >= 0"
__lowercase = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (n >= 0), "'n' must been an int and >= 0"
__lowercase = 0
__lowercase = 1
__lowercase = 1 # this will be return
for _ in range(n - 1 ):
__lowercase = ans
ans += fiba
__lowercase = tmp
return ans
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _A :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["prompt"]
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
if "image" in inputs:
__lowercase = inputs["image"]
else:
__lowercase = None
if "mask_image" in inputs:
__lowercase = inputs["mask_image"]
else:
__lowercase = None
if "original_image" in inputs:
__lowercase = inputs["original_image"]
else:
__lowercase = None
__lowercase , __lowercase = pipe.encode_prompt(lowerCamelCase )
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase , lowerCamelCase ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(1_00, 0.2_5) = }''')
print(F'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''')
| 655 |
import numpy as np
snake_case__ : Tuple = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class _A :
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase , __lowercase = np.where(letter == self.SQUARE )
__lowercase = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def _snake_case ( self : List[Any] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = self.SQUARE[indexa - 1, indexa - 1]
return letter
def _snake_case ( self : int , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
__lowercase = message.replace(" " , "" )
__lowercase = message.replace("j" , "i" )
__lowercase = np.empty((2, len(lowerCamelCase )) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape(2 * len(lowerCamelCase ) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[numbers_index * 2] )
__lowercase = int(second_step[(numbers_index * 2) + 1] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = encoded_message + letter
return encoded_message
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
message.replace(" " , "" )
__lowercase = np.empty(2 * len(lowerCamelCase ) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape((2, len(lowerCamelCase )) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[0, numbers_index] )
__lowercase = int(second_step[1, numbers_index] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = decoded_message + letter
return decoded_message
| 655 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
snake_case__ : List[Any] = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
"""
snake_case__ : Optional[int] = """\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""
snake_case__ : Optional[int] = """
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for 'record': list of question-answer dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'prediction_text': the predicted answer text
- for 'multirc': list of question-answer dictionaries with the following keys:
- 'idx': index of the question-answer pair as specified by the dataset
- 'prediction': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for 'record': list of question-answers dictionaries with the following keys:
- 'idx': index of the question as specified by the dataset
- 'answers': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for 'record':
- 'exact_match': Exact match between answer and gold answer
- 'f1': F1 score
- for 'multirc':
- 'exact_match': Exact match between answer and gold answer
- 'f1_m': Per-question macro-F1 score
- 'f1_a': Average F1 score over all answers
- for 'axb':
'matthews_correlation': Matthew Correlation
- for 'cb':
- 'accuracy': Accuracy
- 'f1': F1 score
- for all others:
- 'accuracy': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'record')
>>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
>>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
>>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}
>>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return float((preds == labels).mean() )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="binary" ):
__lowercase = simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = float(fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=_SCREAMING_SNAKE_CASE , average=_SCREAMING_SNAKE_CASE ) )
return {
"accuracy": acc,
"f1": fa,
}
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = {}
for id_pred, label in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
__lowercase = id_pred["prediction"]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
__lowercase = [(pred, label)]
__lowercase , __lowercase = [], []
for question, preds_labels in question_map.items():
__lowercase , __lowercase = zip(*_SCREAMING_SNAKE_CASE )
__lowercase = fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=_SCREAMING_SNAKE_CASE , average="macro" )
fas.append(_SCREAMING_SNAKE_CASE )
__lowercase = int(sum(pred == label for pred, label in preds_labels ) == len(_SCREAMING_SNAKE_CASE ) )
ems.append(_SCREAMING_SNAKE_CASE )
__lowercase = float(sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE ) )
__lowercase = sum(_SCREAMING_SNAKE_CASE ) / len(_SCREAMING_SNAKE_CASE )
__lowercase = float(fa_score(y_true=_SCREAMING_SNAKE_CASE , y_pred=[id_pred["prediction"] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _A ( datasets.Metric ):
'''simple docstring'''
def _snake_case ( self : Tuple ):
'''simple docstring'''
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None , )
def _snake_case ( self : Tuple ):
'''simple docstring'''
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"prediction_text": datasets.Value("string" ),
},
"references": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"answers": datasets.Sequence(datasets.Value("string" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("int64" ),
"paragraph": datasets.Value("int64" ),
"question": datasets.Value("int64" ),
},
"prediction": datasets.Value("int64" ),
},
"references": datasets.Value("int64" ),
}
else:
return {
"predictions": datasets.Value("int64" ),
"references": datasets.Value("int64" ),
}
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Dict ):
'''simple docstring'''
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowerCamelCase , lowerCamelCase )}
elif self.config_name == "cb":
return acc_and_fa(lowerCamelCase , lowerCamelCase , fa_avg="macro" )
elif self.config_name == "record":
__lowercase = [
{
"qas": [
{"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
for ref in references
]
}
]
__lowercase = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
return evaluate_record(lowerCamelCase , lowerCamelCase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowerCamelCase , lowerCamelCase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowerCamelCase , lowerCamelCase )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
| 655 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
'''simple docstring'''
_snake_case : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def snake_case_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 655 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _A ( _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : int = LayoutLMTokenizer
_snake_case : List[str] = LayoutLMTokenizerFast
_snake_case : List[str] = True
_snake_case : Tuple = True
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
__lowercase = [
"[UNK]",
"[CLS]",
"[SEP]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def _snake_case ( self : Dict , **lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def _snake_case ( self : Dict , lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = "UNwant\u00E9d,running"
__lowercase = "unwanted, running"
return input_text, output_text
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.tokenizer_class(self.vocab_file )
__lowercase = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(lowerCamelCase , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [7, 4, 5, 10, 8, 9] )
def _snake_case ( self : Dict ):
'''simple docstring'''
pass
| 655 |
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__ : List[Any] = 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 ( _lowercase ):
'''simple docstring'''
_snake_case : List[Any] = """yolos"""
def __init__( self : Union[str, Any] , lowerCamelCase : Union[str, Any]=768 , lowerCamelCase : int=12 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Optional[Any]=3_072 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Dict=0.0 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Any=0.02 , lowerCamelCase : Optional[Any]=1e-12 , lowerCamelCase : Optional[Any]=[512, 864] , lowerCamelCase : str=16 , lowerCamelCase : Dict=3 , lowerCamelCase : str=True , lowerCamelCase : List[Any]=100 , lowerCamelCase : Dict=True , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=1 , lowerCamelCase : str=5 , lowerCamelCase : Any=2 , lowerCamelCase : str=5 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : List[Any]=0.1 , **lowerCamelCase : List[Any] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = num_detection_tokens
__lowercase = use_mid_position_embeddings
__lowercase = auxiliary_loss
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = version.parse("""1.11""" )
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : str ):
'''simple docstring'''
return 1e-4
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return 12
| 655 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class _A :
'''simple docstring'''
def __init__( self : List[Any] , lowerCamelCase : List[str] , lowerCamelCase : List[Any]=2 , lowerCamelCase : Optional[Any]=32 , lowerCamelCase : Union[str, Any]=16 , lowerCamelCase : Union[str, Any]=3 , lowerCamelCase : Optional[Any]=True , lowerCamelCase : Optional[int]=True , lowerCamelCase : Dict=32 , lowerCamelCase : Any=4 , lowerCamelCase : List[Any]=[0, 1, 2, 3] , lowerCamelCase : Union[str, Any]=4 , lowerCamelCase : Dict=37 , lowerCamelCase : List[str]="gelu" , lowerCamelCase : Optional[int]=0.1 , lowerCamelCase : Tuple=0.1 , lowerCamelCase : Dict=0.02 , lowerCamelCase : str=3 , lowerCamelCase : int=[1, 384, 24, 24] , lowerCamelCase : Optional[Any]=True , lowerCamelCase : Union[str, Any]=None , ):
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = backbone_out_indices
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = backbone_featmap_shape
__lowercase = scope
__lowercase = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
__lowercase = (image_size // patch_size) ** 2
__lowercase = num_patches + 1
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowercase = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [96, 192, 384, 768],
"num_groups": 2,
}
return DPTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=lowerCamelCase , backbone_featmap_shape=self.backbone_featmap_shape , )
def _snake_case ( self : Tuple , lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Dict ):
'''simple docstring'''
__lowercase = DPTModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : List[Any] , lowerCamelCase : Any , lowerCamelCase : Optional[Any] , lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = self.num_labels
__lowercase = DPTForDepthEstimation(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def _snake_case ( self : str , lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict , lowerCamelCase : List[str] ):
'''simple docstring'''
__lowercase = self.num_labels
__lowercase = DPTForSemanticSegmentation(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _A ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Tuple = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
_snake_case : Dict = (
{
"""depth-estimation""": DPTForDepthEstimation,
"""feature-extraction""": DPTModel,
"""image-segmentation""": DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_snake_case : Tuple = False
_snake_case : Tuple = False
_snake_case : List[Any] = False
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = DPTModelTester(self )
__lowercase = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=37 )
def _snake_case ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="DPT does not use inputs_embeds" )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
pass
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase , nn.Linear ) )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowerCamelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase )
def _snake_case ( self : Dict ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = True
if model_class in get_values(lowerCamelCase ):
continue
__lowercase = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.train()
__lowercase = self._prepare_for_class(lowerCamelCase , lowerCamelCase , return_labels=lowerCamelCase )
__lowercase = model(**lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : int ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = False
__lowercase = True
if model_class in get_values(lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
__lowercase = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.gradient_checkpointing_enable()
model.train()
__lowercase = self._prepare_for_class(lowerCamelCase , lowerCamelCase , return_labels=lowerCamelCase )
__lowercase = model(**lowerCamelCase ).loss
loss.backward()
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = _config_zero_init(lowerCamelCase )
for model_class in self.all_model_classes:
__lowercase = model_class(config=lowerCamelCase )
# Skip the check for the backbone
__lowercase = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
__lowercase = [f"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : Tuple ):
'''simple docstring'''
pass
@slow
def _snake_case ( self : Dict ):
'''simple docstring'''
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
__lowercase = DPTModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = "add"
with self.assertRaises(lowerCamelCase ):
__lowercase = DPTForDepthEstimation(lowerCamelCase )
def snake_case_ ( ):
__lowercase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
@slow
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
__lowercase = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(lowerCamelCase )
__lowercase = prepare_img()
__lowercase = image_processor(images=lowerCamelCase , return_tensors="pt" ).to(lowerCamelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.predicted_depth
# verify the predicted depth
__lowercase = torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape , lowerCamelCase )
__lowercase = torch.tensor(
[[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , lowerCamelCase , atol=1e-4 ) )
| 655 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Optional[int] = logging.get_logger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = MobileNetVaConfig(layer_norm_eps=0.0_0_1 )
if "_quant" in model_name:
raise ValueError("Quantized models are not supported." )
__lowercase = re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" , _SCREAMING_SNAKE_CASE )
if matches:
__lowercase = float(matches[1] )
__lowercase = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
__lowercase = 1_0_0_1
__lowercase = "imagenet-1k-id2label.json"
__lowercase = "huggingface/label-files"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ) + 1: v for k, v in idalabel.items()}
__lowercase = "background"
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ( ):
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = get_mobilenet_va_config(_SCREAMING_SNAKE_CASE )
# Load 🤗 model
__lowercase = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
__lowercase = MobileNetVaImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size} , size={"shortest_edge": config.image_size + 3_2} , )
__lowercase = image_processor(images=prepare_img() , return_tensors="pt" )
__lowercase = model(**_SCREAMING_SNAKE_CASE )
__lowercase = outputs.logits
assert logits.shape == (1, 1_0_0_1)
if model_name == "mobilenet_v1_1.0_224":
__lowercase = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] )
elif model_name == "mobilenet_v1_0.75_192":
__lowercase = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3] )
else:
__lowercase = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("Pushing to the hub..." )
__lowercase = "google/" + model_name
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""mobilenet_v1_1.0_224""",
type=str,
help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt 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."""
)
snake_case__ : Dict = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 655 | 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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : List[Any] = logging.get_logger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-1k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
__lowercase = BitConfig(
conv_layer=_SCREAMING_SNAKE_CASE , num_labels=1_0_0_0 , idalabel=_SCREAMING_SNAKE_CASE , labelaid=_SCREAMING_SNAKE_CASE , )
return config
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if "stem.conv" in name:
__lowercase = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
__lowercase = name.replace("blocks" , "layers" )
if "head.fc" in name:
__lowercase = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
__lowercase = "bit." + name
if "bit" not in name and "classifier" not in name:
__lowercase = "bit.encoder." + name
return name
def snake_case_ ( ):
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = get_config(_SCREAMING_SNAKE_CASE )
# load original model from timm
__lowercase = create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
# load state_dict of original model
__lowercase = timm_model.state_dict()
for key in state_dict.copy().keys():
__lowercase = state_dict.pop(_SCREAMING_SNAKE_CASE )
__lowercase = val.squeeze() if "head" in key else val
# load HuggingFace model
__lowercase = BitForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# create image processor
__lowercase = create_transform(**resolve_data_config({} , model=_SCREAMING_SNAKE_CASE ) )
__lowercase = transform.transforms
__lowercase = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
__lowercase = BitImageProcessor(
do_resize=_SCREAMING_SNAKE_CASE , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_SCREAMING_SNAKE_CASE , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_SCREAMING_SNAKE_CASE , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
__lowercase = prepare_img()
__lowercase = transform(_SCREAMING_SNAKE_CASE ).unsqueeze(0 )
__lowercase = processor(_SCREAMING_SNAKE_CASE , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# verify logits
with torch.no_grad():
__lowercase = model(_SCREAMING_SNAKE_CASE )
__lowercase = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
__lowercase = timm_model(_SCREAMING_SNAKE_CASE )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_SCREAMING_SNAKE_CASE , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print(F"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(F"""ybelkada/{model_name}""" )
processor.push_to_hub(F"""ybelkada/{model_name}""" )
if __name__ == "__main__":
snake_case__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""resnetv2_50x1_bitm""",
type=str,
help="""Name of the BiT timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model to the hub.""",
)
snake_case__ : Tuple = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 |
from __future__ import annotations
from typing import Any
class _A :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = num_of_nodes
__lowercase = []
__lowercase = {}
def _snake_case ( self : Dict , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def _snake_case ( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
__lowercase = self.find_component(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : list[int] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
__lowercase = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__lowercase = self.find_component(lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCamelCase )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = []
__lowercase = 0
__lowercase = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__lowercase = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__lowercase = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
__lowercase = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def snake_case_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ : Union[str, Any] = """▁"""
snake_case__ : Dict = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
snake_case__ : Optional[Any] = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
snake_case__ : Any = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
snake_case__ : Union[str, Any] = {
"""ernie-m-base""": 5_14,
"""ernie-m-large""": 5_14,
}
snake_case__ : Optional[int] = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[str] = ["input_ids"]
_snake_case : Union[str, Any] = VOCAB_FILES_NAMES
_snake_case : Dict = PRETRAINED_INIT_CONFIGURATION
_snake_case : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : List[str] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : List[str] = RESOURCE_FILES_NAMES
def __init__( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : List[str]=None , lowerCamelCase : int=False , lowerCamelCase : List[Any]="utf8" , lowerCamelCase : Optional[int]="[UNK]" , lowerCamelCase : List[str]="[SEP]" , lowerCamelCase : int="[PAD]" , lowerCamelCase : List[str]="[CLS]" , lowerCamelCase : Optional[Any]="[MASK]" , lowerCamelCase : Optional[Dict[str, Any]] = None , **lowerCamelCase : List[str] , ):
'''simple docstring'''
__lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , pad_token=lowerCamelCase , cls_token=lowerCamelCase , mask_token=lowerCamelCase , vocab_file=lowerCamelCase , encoding=lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase , )
__lowercase = do_lower_case
__lowercase = sentencepiece_model_ckpt
__lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCamelCase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
__lowercase = self.load_vocab(filepath=lowerCamelCase )
else:
__lowercase = {self.sp_model.id_to_piece(lowerCamelCase ): id for id in range(self.sp_model.get_piece_size() )}
__lowercase = {v: k for k, v in self.vocab.items()}
def _snake_case ( self : str , lowerCamelCase : Optional[int] ):
'''simple docstring'''
if text is None:
return None
__lowercase = self.tokenize(lowerCamelCase )
__lowercase , __lowercase = "", []
for i, ch in enumerate(lowerCamelCase ):
if ch in self.SP_CHAR_MAPPING:
__lowercase = self.SP_CHAR_MAPPING.get(lowerCamelCase )
else:
__lowercase = unicodedata.normalize("NFKC" , lowerCamelCase )
if self.is_whitespace(lowerCamelCase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(lowerCamelCase ) )
__lowercase , __lowercase , __lowercase = normalized_text, [], 0
if self.do_lower_case:
__lowercase = text.lower()
for token in split_tokens:
if token[:1] == "▁":
__lowercase = token[1:]
__lowercase = text[offset:].index(lowerCamelCase ) + offset
__lowercase = start + len(lowerCamelCase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
__lowercase = end
return token_mapping
@property
def _snake_case ( self : Any ):
'''simple docstring'''
return len(self.vocab )
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self : Optional[int] ):
'''simple docstring'''
__lowercase = self.__dict__.copy()
__lowercase = None
return state
def __setstate__( self : str , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowercase = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__lowercase = {}
__lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def _snake_case ( self : int , lowerCamelCase : List[str] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(lowerCamelCase , lowerCamelCase ) for c in text) )
def _snake_case ( self : List[str] , lowerCamelCase : Optional[int] , lowerCamelCase : int=False , lowerCamelCase : Tuple=64 , lowerCamelCase : Optional[int]=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
__lowercase = True
if self.sp_model_kwargs.get("alpha" ) is not None:
__lowercase = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
__lowercase = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
__lowercase = self.sp_model.EncodeAsPieces(lowerCamelCase )
else:
__lowercase = self.sp_model.SampleEncodeAsPieces(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = []
for pi, piece in enumerate(lowerCamelCase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(lowerCamelCase ) and pi != 0:
new_pieces.append(lowerCamelCase )
continue
else:
continue
__lowercase = 0
for i, chunk in enumerate(lowerCamelCase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(lowerCamelCase ) or self.is_punct(lowerCamelCase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(lowerCamelCase )
__lowercase = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
__lowercase = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
__lowercase = i
if len(lowerCamelCase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def _snake_case ( self : Any , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowercase = "".join(lowerCamelCase ).replace(lowerCamelCase , " " ).strip()
return out_string
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = self.convert_ids_to_tokens(lowerCamelCase )
__lowercase = "".join(lowerCamelCase ).replace(lowerCamelCase , " " ).strip()
return out_string
def _snake_case ( self : Tuple , lowerCamelCase : List[str] ):
'''simple docstring'''
return self.vocab.get(lowerCamelCase , self.vocab.get(self.unk_token ) )
def _snake_case ( self : List[Any] , lowerCamelCase : List[Any] ):
'''simple docstring'''
return self.reverse_vocab.get(lowerCamelCase , self.unk_token )
def _snake_case ( self : List[Any] , lowerCamelCase : List[str] , lowerCamelCase : List[str]=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def _snake_case ( self : Optional[int] , lowerCamelCase : Optional[Any] , lowerCamelCase : str=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def _snake_case ( self : List[str] , lowerCamelCase : Union[str, Any] , lowerCamelCase : int=None , lowerCamelCase : List[str]=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase )) + [1]
return [1] + ([0] * len(lowerCamelCase )) + [1]
def _snake_case ( self : Union[str, Any] , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(lowerCamelCase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(lowerCamelCase ) + 1) + [1] * (len(lowerCamelCase ) + 3)
def _snake_case ( self : Any , lowerCamelCase : int ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def _snake_case ( self : List[str] , lowerCamelCase : Any ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def _snake_case ( self : List[str] , lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def _snake_case ( self : Optional[int] , lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(lowerCamelCase ) == 1:
__lowercase = unicodedata.category(lowerCamelCase )
if cat == "Zs":
return True
return False
def _snake_case ( self : Optional[Any] , lowerCamelCase : Dict ):
'''simple docstring'''
__lowercase = {}
with io.open(lowerCamelCase , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(lowerCamelCase ):
__lowercase = line.rstrip("\n" )
__lowercase = int(lowerCamelCase )
return token_to_idx
def _snake_case ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
__lowercase = 0
if os.path.isdir(lowerCamelCase ):
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
__lowercase = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
__lowercase = token_index
writer.write(token + "\n" )
index += 1
__lowercase = os.path.join(lowerCamelCase , "sentencepiece.bpe.model" )
with open(lowerCamelCase , "wb" ) as fi:
__lowercase = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase )
return (vocab_file,)
| 655 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[str] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
snake_case__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
from __future__ import annotations
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = position
__lowercase = [
(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),
]
__lowercase = []
for position in positions:
__lowercase , __lowercase = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_SCREAMING_SNAKE_CASE )
return permissible_positions
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
return not any(elem == 0 for row in board for elem in row )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if is_complete(_SCREAMING_SNAKE_CASE ):
return True
for position in get_valid_pos(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) ):
__lowercase , __lowercase = position
if board[y][x] == 0:
__lowercase = curr + 1
if open_knight_tour_helper(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , curr + 1 ):
return True
__lowercase = 0
return False
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = [[0 for i in range(_SCREAMING_SNAKE_CASE )] for j in range(_SCREAMING_SNAKE_CASE )]
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
__lowercase = 1
if open_knight_tour_helper(_SCREAMING_SNAKE_CASE , (i, j) , 1 ):
return board
__lowercase = 0
__lowercase = F"""Open Kight Tour cannot be performed on a board of size {n}"""
raise ValueError(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
from __future__ import annotations
import bisect
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = 0
__lowercase = len(_SCREAMING_SNAKE_CASE ) - 1
while left <= right:
__lowercase = left + (right - left) // 2
__lowercase = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__lowercase = midpoint - 1
else:
__lowercase = midpoint + 1
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = bisect.bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
return index
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if right < left:
return None
__lowercase = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint - 1 )
else:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint + 1 , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Optional[Any] = input("""Enter numbers separated by comma:\n""").strip()
snake_case__ : Any = sorted(int(item) for item in user_input.split(""","""))
snake_case__ : Any = int(input("""Enter a single number to be found in the list:\n"""))
snake_case__ : List[Any] = binary_search(collection, target)
if result is None:
print(F'''{target} was not found in {collection}.''')
else:
print(F'''{target} was found at position {result} in {collection}.''')
| 655 | 1 |
snake_case__ : Optional[int] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
__lowercase = Stack()
__lowercase = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_SCREAMING_SNAKE_CASE ) )
elif i in operators:
# RULE 2
operator_stack.push(_SCREAMING_SNAKE_CASE )
elif i == ")":
# RULE 4
__lowercase = operator_stack.peek()
operator_stack.pop()
__lowercase = operand_stack.peek()
operand_stack.pop()
__lowercase = operand_stack.peek()
operand_stack.pop()
__lowercase = operators[opr](_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
operand_stack.push(_SCREAMING_SNAKE_CASE )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
snake_case__ : Union[str, Any] = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')
| 655 |
import copy
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
from ..auto import CONFIG_MAPPING
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """conditional_detr"""
_snake_case : Union[str, Any] = ["""past_key_values"""]
_snake_case : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Optional[Any] , lowerCamelCase : int=True , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[int]=3 , lowerCamelCase : Optional[int]=300 , lowerCamelCase : List[Any]=6 , lowerCamelCase : str=2_048 , lowerCamelCase : Any=8 , lowerCamelCase : List[str]=6 , lowerCamelCase : Any=2_048 , lowerCamelCase : List[Any]=8 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : List[str]=0.0 , lowerCamelCase : List[Any]=True , lowerCamelCase : str="relu" , lowerCamelCase : int=256 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Dict=0.0 , lowerCamelCase : Tuple=0.02 , lowerCamelCase : int=1.0 , lowerCamelCase : Tuple=False , lowerCamelCase : List[str]="sine" , lowerCamelCase : List[Any]="resnet50" , lowerCamelCase : Any=True , lowerCamelCase : Any=False , lowerCamelCase : List[Any]=2 , lowerCamelCase : List[Any]=5 , lowerCamelCase : str=2 , lowerCamelCase : Dict=1 , lowerCamelCase : List[str]=1 , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Dict=5 , lowerCamelCase : List[Any]=2 , lowerCamelCase : Tuple=0.25 , **lowerCamelCase : List[str] , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
__lowercase = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = backbone_config.get("model_type" )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowerCamelCase )
__lowercase = use_timm_backbone
__lowercase = backbone_config
__lowercase = num_channels
__lowercase = num_queries
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = encoder_layers
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
__lowercase = backbone
__lowercase = use_pretrained_backbone
__lowercase = dilation
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = cls_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase , **lowerCamelCase )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _snake_case ( self : str ):
'''simple docstring'''
return self.d_model
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Any = version.parse("""1.11""" )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _snake_case ( self : Any ):
'''simple docstring'''
return 1e-5
@property
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
return 12
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
__lowercase = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1 or len(_SCREAMING_SNAKE_CASE ) <= key:
return input_string
for position, character in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(_SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(_SCREAMING_SNAKE_CASE )
__lowercase = ["".join(_SCREAMING_SNAKE_CASE ) for row in temp_grid]
__lowercase = "".join(_SCREAMING_SNAKE_CASE )
return output_string
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = []
__lowercase = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1:
return input_string
__lowercase = [[] for _ in range(_SCREAMING_SNAKE_CASE )] # generates template
for position in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(_SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("*" )
__lowercase = 0
for row in temp_grid: # fills in the characters
__lowercase = input_string[counter : counter + len(_SCREAMING_SNAKE_CASE )]
grid.append(list(_SCREAMING_SNAKE_CASE ) )
counter += len(_SCREAMING_SNAKE_CASE )
__lowercase = "" # reads as zigzag
for position in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(_SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = {}
for key_guess in range(1 , len(_SCREAMING_SNAKE_CASE ) ): # tries every key
__lowercase = decrypt(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Any = logging.get_logger(__name__)
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
_snake_case : Dict = """maskformer-swin"""
_snake_case : List[str] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : List[str] , lowerCamelCase : Any=224 , lowerCamelCase : Optional[Any]=4 , lowerCamelCase : Dict=3 , lowerCamelCase : Tuple=96 , lowerCamelCase : str=[2, 2, 6, 2] , lowerCamelCase : Dict=[3, 6, 12, 24] , lowerCamelCase : Optional[Any]=7 , lowerCamelCase : Any=4.0 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[str]=0.0 , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : int="gelu" , lowerCamelCase : Optional[int]=False , lowerCamelCase : List[Any]=0.02 , lowerCamelCase : Tuple=1e-5 , lowerCamelCase : Dict=None , lowerCamelCase : Dict=None , **lowerCamelCase : int , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(lowerCamelCase )
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = layer_norm_eps
__lowercase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowercase = int(embed_dim * 2 ** (len(lowerCamelCase ) - 1) )
__lowercase = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(lowerCamelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase , out_indices=lowerCamelCase , stage_names=self.stage_names )
| 655 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , *,
lowerCamelCase : int = 4 , lowerCamelCase : int = 768 , lowerCamelCase : int , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
super().__init__()
__lowercase = nn.Parameter(torch.zeros(lowerCamelCase ) )
# parameters for additional clip time embeddings
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
# parameters for encoder hidden states
__lowercase = clip_extra_context_tokens
__lowercase = nn.Linear(
lowerCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.LayerNorm(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , *, lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
__lowercase = image_embeddings.shape[0]
__lowercase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
__lowercase = classifier_free_guidance_embeddings.expand(
lowerCamelCase , -1 )
__lowercase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
__lowercase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
__lowercase = self.embedding_proj(lowerCamelCase )
__lowercase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase )
__lowercase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
__lowercase = self.clip_extra_context_tokens_proj(lowerCamelCase )
__lowercase = clip_extra_context_tokens.reshape(lowerCamelCase , -1 , self.clip_extra_context_tokens )
__lowercase = clip_extra_context_tokens.permute(0 , 2 , 1 )
__lowercase = self.encoder_hidden_states_proj(lowerCamelCase )
__lowercase = self.text_encoder_hidden_states_norm(lowerCamelCase )
__lowercase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__lowercase = gray_code_sequence_string(_SCREAMING_SNAKE_CASE )
#
# convert them to integers
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = int(sequence[i] , 2 )
return sequence
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__lowercase = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__lowercase = gray_code_sequence_string(bit_count - 1 )
__lowercase = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__lowercase = "0" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__lowercase = "1" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1_0_0_0 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__lowercase = n - 1
__lowercase = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
__lowercase = 0
while count < prec:
__lowercase = random.randint(2 , n - 1 )
__lowercase = bin_exp_mod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if b != 1:
__lowercase = True
for _ in range(_SCREAMING_SNAKE_CASE ):
if b == n - 1:
__lowercase = False
break
__lowercase = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
snake_case__ : Dict = abs(int(input("""Enter bound : """).strip()))
print("""Here's the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 655 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=True ):
model.train()
__lowercase = model(_SCREAMING_SNAKE_CASE )
__lowercase = F.mse_loss(_SCREAMING_SNAKE_CASE , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
set_seed(4_2 )
__lowercase = RegressionModel()
__lowercase = deepcopy(_SCREAMING_SNAKE_CASE )
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
model.to(accelerator.device )
if sched:
__lowercase = AdamW(params=model.parameters() , lr=1E-3 )
__lowercase = AdamW(params=ddp_model.parameters() , lr=1E-3 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
# Make a copy of `model`
if sched:
__lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test when on a single CPU or GPU that the context manager does nothing
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test on distributed setup that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(_SCREAMING_SNAKE_CASE ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
GradientState._reset_state()
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
__lowercase = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE ))
if accelerator.num_processes > 1:
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
GradientState._reset_state()
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase = RegressionDataset(length=9_6 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if iteration < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if batch_num < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = accelerator.state
if state.local_process_index == 0:
print("**Test `accumulate` gradient accumulation with dataloader break**" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("**Test NOOP `no_sync` context manager**" )
test_noop_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("**Test Distributed `no_sync` context manager**" )
test_distributed_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("<" , "2.0" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = os.path.join(args.tf_model_dir , "parameters.json" )
__lowercase = json.loads(open(_SCREAMING_SNAKE_CASE ).read() )
if not params:
raise ValueError(
F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
if not args.output.endswith(".pt" ):
__lowercase = args.output + ".pt"
__lowercase = OrderedDict()
with tf.device("/CPU:0" ):
__lowercase = tf.train.load_checkpoint(args.tf_model_dir )
__lowercase = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
__lowercase = reader.get_tensor(_SCREAMING_SNAKE_CASE ).astype(np.floataa )
if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
continue
if key_name.startswith("pasts/" ):
if key_name.startswith("pasts/mlp" ):
__lowercase = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
__lowercase = 8
__lowercase = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
__lowercase = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.startswith("model/moe" ):
__lowercase = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
__lowercase = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
__lowercase = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/softmlp/kernel" ):
__lowercase = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
__lowercase = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
__lowercase = key_name[-9:-7]
for i in range(1_6 ):
__lowercase = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
__lowercase = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.startswith("model/mlp" ):
__lowercase = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
__lowercase = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
__lowercase = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/p1/bias" ):
__lowercase = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
__lowercase = vnp.copy() # same because it is one dimensional
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/p2/kernel" ):
__lowercase = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
__lowercase = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/p2/bias" ):
__lowercase = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
__lowercase = vnp.copy() # same because it is one dimensional
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.startswith("model/ln" ):
__lowercase = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
__lowercase = "model.blocks.%d.feed_forward.norm.bias" % player
__lowercase = vnp.copy() # same because it is one dimensional
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/g" ):
__lowercase = "model.blocks.%d.feed_forward.norm.weight" % player
__lowercase = vnp.copy() # same because it is one dimensional
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.startswith("model/att" ):
__lowercase = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
__lowercase = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
__lowercase = state[:, 0, :, :]
__lowercase = state[:, 1, :, :]
__lowercase = state[:, 2, :, :]
__lowercase = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
__lowercase = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
__lowercase = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
__lowercase = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
__lowercase = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
__lowercase = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/o/kernel" ):
__lowercase = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
__lowercase = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.startswith("model/an" ):
__lowercase = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
__lowercase = "model.blocks.%d.self_attn.norm.bias" % player
__lowercase = vnp.copy() # same because it is one dimensional
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.endswith("/g" ):
__lowercase = "model.blocks.%d.self_attn.norm.weight" % player
__lowercase = vnp.copy() # same because it is one dimensional
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
__lowercase = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
__lowercase = "model.%s.weight" % nlayer
__lowercase = vnp.copy() # same in embedded
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
if key_name.startswith("model/wte" ):
__lowercase = "lm_head.weight"
__lowercase = vnp.copy() # same in embedded
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name.startswith("model/wob" ):
__lowercase = "final_logits_bias"
__lowercase = vnp.copy() # same in embedded
__lowercase = state.reshape((1, -1) )
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name == "model/dense/kernel":
__lowercase = "model.last_project.weight"
__lowercase = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
elif key_name == "model/dense_1/bias":
__lowercase = "model.last_project.bias"
__lowercase = vnp.copy() # same because it is one dimensional
__lowercase = torch.tensor(_SCREAMING_SNAKE_CASE )
torch.save(_SCREAMING_SNAKE_CASE , args.output )
if __name__ == "__main__":
snake_case__ : Tuple = argparse.ArgumentParser(
description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
snake_case__ : Dict = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 655 |
from ....utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : List[str] , lowerCamelCase : Any , lowerCamelCase : Dict=None , lowerCamelCase : Dict=2_048 ):
'''simple docstring'''
__lowercase = config.__dict__
__lowercase = modal_hidden_size
if num_labels:
__lowercase = num_labels
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = [[0] * n for i in range(_SCREAMING_SNAKE_CASE )]
for i in range(_SCREAMING_SNAKE_CASE ):
__lowercase = y_points[i]
for i in range(2 , _SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class _A ( _lowercase , _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Dict = StableUnCLIPImgaImgPipeline
_snake_case : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
_snake_case : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_snake_case : int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_snake_case : int = frozenset([] )
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = 32
__lowercase = embedder_hidden_size
# image encoding components
__lowercase = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
__lowercase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowerCamelCase , projection_dim=lowerCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__lowercase = StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase )
__lowercase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowercase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowercase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCamelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCamelCase , layers_per_block=1 , upcast_attention=lowerCamelCase , use_linear_projection=lowerCamelCase , )
torch.manual_seed(0 )
__lowercase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=lowerCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
__lowercase = AutoencoderKL()
__lowercase = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def _snake_case ( self : List[Any] , lowerCamelCase : str , lowerCamelCase : Any=0 , lowerCamelCase : Union[str, Any]=True ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
__lowercase = torch.manual_seed(lowerCamelCase )
else:
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
if pil_image:
__lowercase = input_image * 0.5 + 0.5
__lowercase = input_image.clamp(0 , 1 )
__lowercase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowercase = DiffusionPipeline.numpy_to_pil(lowerCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableUnCLIPImgaImgPipeline(**lowerCamelCase )
__lowercase = sd_pipe.to(lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
inputs.update({"image_embeds": None} )
__lowercase = sd_pipe(**lowerCamelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _snake_case ( self : str ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase )
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowercase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowercase = pipe(lowerCamelCase , "anime turle" , generator=lowerCamelCase , output_type="np" )
__lowercase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCamelCase , lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowercase = pipe(
lowerCamelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 655 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=True ):
model.train()
__lowercase = model(_SCREAMING_SNAKE_CASE )
__lowercase = F.mse_loss(_SCREAMING_SNAKE_CASE , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
set_seed(4_2 )
__lowercase = RegressionModel()
__lowercase = deepcopy(_SCREAMING_SNAKE_CASE )
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
model.to(accelerator.device )
if sched:
__lowercase = AdamW(params=model.parameters() , lr=1E-3 )
__lowercase = AdamW(params=ddp_model.parameters() , lr=1E-3 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
__lowercase = LambdaLR(_SCREAMING_SNAKE_CASE , lr_lambda=lambda _SCREAMING_SNAKE_CASE : epoch**0.6_5 )
# Make a copy of `model`
if sched:
__lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test when on a single CPU or GPU that the context manager does nothing
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# Test on distributed setup that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
# Use a single batch
__lowercase , __lowercase = next(iter(_SCREAMING_SNAKE_CASE ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# Sync grads
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(_SCREAMING_SNAKE_CASE ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
__lowercase = ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE ) )]
GradientState._reset_state()
def snake_case_ ( _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False ):
__lowercase = Accelerator(
split_batches=_SCREAMING_SNAKE_CASE , dispatch_batches=_SCREAMING_SNAKE_CASE , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase = get_training_setup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = batch.values()
# Gather the distributed inputs and targs for the base model
__lowercase , __lowercase = accelerator.gather((ddp_input, ddp_target) )
__lowercase , __lowercase = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(_SCREAMING_SNAKE_CASE ):
step_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
__lowercase = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE ))
if accelerator.num_processes > 1:
check_model_parameters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Shuffle ddp_input on each iteration
torch.manual_seed(1_3_3_7 + iteration )
GradientState._reset_state()
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = RegressionDataset(length=8_0 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase = RegressionDataset(length=9_6 )
__lowercase = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=1_6 )
__lowercase , __lowercase = accelerator.prepare(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if iteration < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(_SCREAMING_SNAKE_CASE ):
assert id(accelerator.gradient_state.active_dataloader ) == id(_SCREAMING_SNAKE_CASE )
if batch_num < len(_SCREAMING_SNAKE_CASE ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def snake_case_ ( ):
__lowercase = Accelerator()
__lowercase = accelerator.state
if state.local_process_index == 0:
print("**Test `accumulate` gradient accumulation with dataloader break**" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("**Test NOOP `no_sync` context manager**" )
test_noop_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("**Test Distributed `no_sync` context manager**" )
test_distributed_sync(_SCREAMING_SNAKE_CASE )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("<" , "2.0" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , *,
lowerCamelCase : int = 4 , lowerCamelCase : int = 768 , lowerCamelCase : int , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
super().__init__()
__lowercase = nn.Parameter(torch.zeros(lowerCamelCase ) )
# parameters for additional clip time embeddings
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
# parameters for encoder hidden states
__lowercase = clip_extra_context_tokens
__lowercase = nn.Linear(
lowerCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase )
__lowercase = nn.LayerNorm(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , *, lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
__lowercase = image_embeddings.shape[0]
__lowercase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
__lowercase = classifier_free_guidance_embeddings.expand(
lowerCamelCase , -1 )
__lowercase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
__lowercase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
__lowercase = self.embedding_proj(lowerCamelCase )
__lowercase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase )
__lowercase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
__lowercase = self.clip_extra_context_tokens_proj(lowerCamelCase )
__lowercase = clip_extra_context_tokens.reshape(lowerCamelCase , -1 , self.clip_extra_context_tokens )
__lowercase = clip_extra_context_tokens.permute(0 , 2 , 1 )
__lowercase = self.encoder_hidden_states_proj(lowerCamelCase )
__lowercase = self.text_encoder_hidden_states_norm(lowerCamelCase )
__lowercase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 655 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
snake_case__ : Dict = logging.getLogger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return (preds == labels).mean()
@dataclass
class _A :
'''simple docstring'''
_snake_case : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
_snake_case : Optional[str] = field(
default=_lowercase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
_snake_case : Optional[str] = field(
default=_lowercase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
_snake_case : Optional[str] = field(
default=_lowercase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class _A :
'''simple docstring'''
_snake_case : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
_snake_case : str = field(metadata={"""help""": """Should contain the data files for the task."""} )
_snake_case : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_snake_case : bool = field(
default=_lowercase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def snake_case_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , _SCREAMING_SNAKE_CASE )
# Set seed
set_seed(training_args.seed )
try:
__lowercase = processors[data_args.task_name]()
__lowercase = processor.get_labels()
__lowercase = len(_SCREAMING_SNAKE_CASE )
except KeyError:
raise ValueError("Task not found: %s" % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowercase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_SCREAMING_SNAKE_CASE , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
__lowercase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
__lowercase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=_SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , )
# Get datasets
__lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
__lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=_SCREAMING_SNAKE_CASE , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(_SCREAMING_SNAKE_CASE ) -> Dict:
__lowercase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE , p.label_ids )}
# Data collator
__lowercase = DataCollatorWithPadding(_SCREAMING_SNAKE_CASE , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
__lowercase = Trainer(
model=_SCREAMING_SNAKE_CASE , args=_SCREAMING_SNAKE_CASE , train_dataset=_SCREAMING_SNAKE_CASE , eval_dataset=_SCREAMING_SNAKE_CASE , compute_metrics=_SCREAMING_SNAKE_CASE , data_collator=_SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
__lowercase = trainer.evaluate()
__lowercase = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_master():
with open(_SCREAMING_SNAKE_CASE , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
writer.write("%s = %s\n" % (key, value) )
results.update(_SCREAMING_SNAKE_CASE )
return results
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case__ : Union[str, Any] = TypeVar("""T""")
snake_case__ : Optional[int] = TypeVar("""U""")
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : T | None , lowerCamelCase : U | None ):
'''simple docstring'''
__lowercase = key
__lowercase = val
__lowercase = None
__lowercase = None
def __repr__( self : Any ):
'''simple docstring'''
return (
f"""Node: key: {self.key}, val: {self.val}, """
f"""has next: {bool(self.next )}, has prev: {bool(self.prev )}"""
)
class _A ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
__lowercase , __lowercase = self.rear, self.head
def __repr__( self : Optional[Any] ):
'''simple docstring'''
__lowercase = ["DoubleLinkedList"]
__lowercase = self.head
while node.next is not None:
rep.append(str(lowerCamelCase ) )
__lowercase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
__lowercase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
__lowercase = node
__lowercase = previous
__lowercase = node
__lowercase = self.rear
def _snake_case ( self : Optional[int] , lowerCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
if node.prev is None or node.next is None:
return None
__lowercase = node.next
__lowercase = node.prev
__lowercase = None
__lowercase = None
return node
class _A ( Generic[T, U] ):
'''simple docstring'''
_snake_case : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = DoubleLinkedList()
__lowercase = capacity
__lowercase = 0
__lowercase = 0
__lowercase = 0
__lowercase = {}
def __repr__( self : Optional[Any] ):
'''simple docstring'''
return (
f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
f"""capacity={self.capacity}, current size={self.num_keys})"""
)
def __contains__( self : Dict , lowerCamelCase : T ):
'''simple docstring'''
return key in self.cache
def _snake_case ( self : List[Any] , lowerCamelCase : T ):
'''simple docstring'''
if key in self.cache:
self.hits += 1
__lowercase = self.cache[key]
__lowercase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowerCamelCase )
return node.val
self.miss += 1
return None
def _snake_case ( self : Union[str, Any] , lowerCamelCase : T , lowerCamelCase : U ):
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
__lowercase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowerCamelCase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
__lowercase = DoubleLinkedListNode(lowerCamelCase , lowerCamelCase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
__lowercase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
__lowercase = value
self.list.add(lowerCamelCase )
@classmethod
def _snake_case ( cls : Union[str, Any] , lowerCamelCase : int = 128 ):
'''simple docstring'''
def cache_decorator_inner(lowerCamelCase : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*lowerCamelCase : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
__lowercase = LRUCache(lowerCamelCase )
__lowercase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
__lowercase = func(*lowerCamelCase )
cls.decorator_function_to_instance_map[func].put(args[0] , lowerCamelCase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowerCamelCase , "cache_info" , lowerCamelCase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
snake_case__ : str = """scheduler_config.json"""
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Union[str, Any] = 1
_snake_case : int = 2
_snake_case : Optional[int] = 3
_snake_case : Optional[int] = 4
_snake_case : int = 5
@dataclass
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : jnp.ndarray
class _A :
'''simple docstring'''
_snake_case : Optional[int] = SCHEDULER_CONFIG_NAME
_snake_case : Dict = ["""dtype"""]
_snake_case : Dict = []
_snake_case : Union[str, Any] = True
@classmethod
def _snake_case ( cls : Dict , lowerCamelCase : Dict[str, Any] = None , lowerCamelCase : Optional[str] = None , lowerCamelCase : Optional[Any]=False , **lowerCamelCase : Union[str, Any] , ):
'''simple docstring'''
__lowercase , __lowercase = cls.load_config(
pretrained_model_name_or_path=lowerCamelCase , subfolder=lowerCamelCase , return_unused_kwargs=lowerCamelCase , **lowerCamelCase , )
__lowercase , __lowercase = cls.from_config(lowerCamelCase , return_unused_kwargs=lowerCamelCase , **lowerCamelCase )
if hasattr(lowerCamelCase , "create_state" ) and getattr(lowerCamelCase , "has_state" , lowerCamelCase ):
__lowercase = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def _snake_case ( self : List[str] , lowerCamelCase : Union[str, os.PathLike] , lowerCamelCase : bool = False , **lowerCamelCase : List[str] ):
'''simple docstring'''
self.save_config(save_directory=lowerCamelCase , push_to_hub=lowerCamelCase , **lowerCamelCase )
@property
def _snake_case ( self : int ):
'''simple docstring'''
return self._get_compatibles()
@classmethod
def _snake_case ( cls : Union[str, Any] ):
'''simple docstring'''
__lowercase = list(set([cls.__name__] + cls._compatibles ) )
__lowercase = importlib.import_module(__name__.split("." )[0] )
__lowercase = [
getattr(lowerCamelCase , lowerCamelCase ) for c in compatible_classes_str if hasattr(lowerCamelCase , lowerCamelCase )
]
return compatible_classes
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert len(_SCREAMING_SNAKE_CASE ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_SCREAMING_SNAKE_CASE ) - x.ndim) ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0.9_9_9 , _SCREAMING_SNAKE_CASE=jnp.floataa ):
def alpha_bar(_SCREAMING_SNAKE_CASE ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
__lowercase = []
for i in range(_SCREAMING_SNAKE_CASE ):
__lowercase = i / num_diffusion_timesteps
__lowercase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(_SCREAMING_SNAKE_CASE ) / alpha_bar(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) )
return jnp.array(_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
@flax.struct.dataclass
class _A :
'''simple docstring'''
_snake_case : jnp.ndarray
_snake_case : jnp.ndarray
_snake_case : jnp.ndarray
@classmethod
def _snake_case ( cls : str , lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = scheduler.config
if config.trained_betas is not None:
__lowercase = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__lowercase = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowercase = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowercase = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}""" )
__lowercase = 1.0 - betas
__lowercase = jnp.cumprod(lowerCamelCase , axis=0 )
return cls(
alphas=lowerCamelCase , betas=lowerCamelCase , alphas_cumprod=lowerCamelCase , )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = state.alphas_cumprod
__lowercase = alphas_cumprod[timesteps] ** 0.5
__lowercase = sqrt_alpha_prod.flatten()
__lowercase = broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE , original_samples.shape )
__lowercase = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowercase = sqrt_one_minus_alpha_prod.flatten()
__lowercase = broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase , __lowercase = get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 655 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
snake_case__ : Optional[Any] = logging.getLogger()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = "\n".join(_SCREAMING_SNAKE_CASE )
Path(_SCREAMING_SNAKE_CASE ).open("w" ).writelines(_SCREAMING_SNAKE_CASE )
snake_case__ : List[str] = """patrickvonplaten/t5-tiny-random"""
snake_case__ : int = """sshleifer/bart-tiny-random"""
snake_case__ : Union[str, Any] = """sshleifer/tiny-mbart"""
snake_case__ : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _A ( _lowercase ):
'''simple docstring'''
def _snake_case ( self : str , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
_dump_articles(lowerCamelCase , lowerCamelCase )
__lowercase = str(Path(self.get_auto_remove_tmp_dir() ) / "scores.json" )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
run_generate()
assert Path(lowerCamelCase ).exists()
# os.remove(Path(output_file_name))
def _snake_case ( self : Dict ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
self.run_eval_tester(lowerCamelCase )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def _snake_case ( self : Optional[Any] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = Path(self.get_auto_remove_tmp_dir() ) / "utest_input.source"
__lowercase = input_file_name.parent / "utest_output.txt"
assert not output_file_name.exists()
__lowercase = {
"en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
"de": [
"Maschinelles Lernen ist großartig, oder?",
"Ich esse gerne Bananen",
"Morgen ist wieder ein toller Tag!",
],
}
__lowercase = Path(self.get_auto_remove_tmp_dir() )
__lowercase = str(tmp_dir / "scores.json" )
__lowercase = str(tmp_dir / "val.target" )
_dump_articles(lowerCamelCase , text["en"] )
_dump_articles(lowerCamelCase , text["de"] )
__lowercase = "translation_en_to_de" if model == T5_TINY else "summarization"
__lowercase = f"""
run_eval_search.py
{model}
{str(lowerCamelCase )}
{str(lowerCamelCase )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"] )
with patch.object(lowerCamelCase , "argv" , lowerCamelCase ):
with CaptureStdout() as cs:
run_search()
__lowercase = [" num_beams | length_penalty", model, "Best score args"]
__lowercase = ["Info"]
if "translation" in task:
expected_strings.append("bleu" )
else:
expected_strings.extend(lowerCamelCase )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(lowerCamelCase ).exists()
os.remove(Path(lowerCamelCase ) )
| 655 | 1 |
from collections import deque
class _A :
'''simple docstring'''
def __init__( self : List[str] , lowerCamelCase : str , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = process_name # process name
__lowercase = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
__lowercase = arrival_time
__lowercase = burst_time # remaining burst time
__lowercase = 0 # total time of the process wait in ready queue
__lowercase = 0 # time from arrival time to completion time
class _A :
'''simple docstring'''
def __init__( self : Any , lowerCamelCase : int , lowerCamelCase : list[int] , lowerCamelCase : deque[Process] , lowerCamelCase : int , ):
'''simple docstring'''
__lowercase = number_of_queues
# time slice of queues that round robin algorithm applied
__lowercase = time_slices
# unfinished process is in this ready_queue
__lowercase = queue
# current time
__lowercase = current_time
# finished process is in this sequence queue
__lowercase = deque()
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def _snake_case ( self : Any , lowerCamelCase : list[Process] ):
'''simple docstring'''
__lowercase = []
for i in range(len(lowerCamelCase ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def _snake_case ( self : Optional[int] , lowerCamelCase : list[Process] ):
'''simple docstring'''
__lowercase = []
for i in range(len(lowerCamelCase ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def _snake_case ( self : Union[str, Any] , lowerCamelCase : list[Process] ):
'''simple docstring'''
__lowercase = []
for i in range(len(lowerCamelCase ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def _snake_case ( self : str , lowerCamelCase : deque[Process] ):
'''simple docstring'''
return [q.burst_time for q in queue]
def _snake_case ( self : int , lowerCamelCase : Process ):
'''simple docstring'''
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def _snake_case ( self : List[Any] , lowerCamelCase : deque[Process] ):
'''simple docstring'''
__lowercase = deque() # sequence deque of finished process
while len(lowerCamelCase ) != 0:
__lowercase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(lowerCamelCase )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
__lowercase = 0
# set the process's turnaround time because it is finished
__lowercase = self.current_time - cp.arrival_time
# set the completion time
__lowercase = self.current_time
# add the process to queue that has finished queue
finished.append(lowerCamelCase )
self.finish_queue.extend(lowerCamelCase ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def _snake_case ( self : str , lowerCamelCase : deque[Process] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(lowerCamelCase ) ):
__lowercase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(lowerCamelCase )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
__lowercase = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(lowerCamelCase )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
__lowercase = 0
# set the finish time
__lowercase = self.current_time
# update the process' turnaround time because it is finished
__lowercase = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(lowerCamelCase )
self.finish_queue.extend(lowerCamelCase ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
for i in range(self.number_of_queues - 1 ):
__lowercase , __lowercase = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
snake_case__ : List[Any] = Process("""P1""", 0, 53)
snake_case__ : List[str] = Process("""P2""", 0, 17)
snake_case__ : List[Any] = Process("""P3""", 0, 68)
snake_case__ : Union[str, Any] = Process("""P4""", 0, 24)
snake_case__ : Union[str, Any] = 3
snake_case__ : Union[str, Any] = [17, 25]
snake_case__ : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"""queue""": deque([Pa, Pa, Pa, Pa])})
snake_case__ : Optional[Any] = Process("""P1""", 0, 53)
snake_case__ : Dict = Process("""P2""", 0, 17)
snake_case__ : Dict = Process("""P3""", 0, 68)
snake_case__ : int = Process("""P4""", 0, 24)
snake_case__ : Union[str, Any] = 3
snake_case__ : Optional[Any] = [17, 25]
snake_case__ : Union[str, Any] = deque([Pa, Pa, Pa, Pa])
snake_case__ : List[str] = MLFQ(number_of_queues, time_slices, queue, 0)
snake_case__ : List[Any] = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
F'''waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print completion times of processes(P1, P2, P3, P4)
print(
F'''completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
F'''turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print sequence of finished processes
print(
F'''sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}'''
)
| 655 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _A :
'''simple docstring'''
_snake_case : int
_snake_case : TreeNode | None = None
_snake_case : TreeNode | None = None
snake_case__ : Dict = namedtuple("""CoinsDistribResult""", """moves excess""")
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if root is None:
return 0
# Validation
def count_nodes(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_SCREAMING_SNAKE_CASE ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_SCREAMING_SNAKE_CASE ) != count_coins(_SCREAMING_SNAKE_CASE ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(_SCREAMING_SNAKE_CASE ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
__lowercase , __lowercase = get_distrib(node.left )
__lowercase , __lowercase = get_distrib(node.right )
__lowercase = 1 - left_distrib_excess
__lowercase = 1 - right_distrib_excess
__lowercase = (
left_distrib_moves
+ right_distrib_moves
+ abs(_SCREAMING_SNAKE_CASE )
+ abs(_SCREAMING_SNAKE_CASE )
)
__lowercase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return get_distrib(_SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from __future__ import annotations
snake_case__ : Optional[int] = 8.9_8_8e9 # units = N * m^s * C^-2
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if distance < 0:
raise ValueError("Distance cannot be negative" )
if force == 0:
__lowercase = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__lowercase = abs(_SCREAMING_SNAKE_CASE ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__lowercase = abs(_SCREAMING_SNAKE_CASE ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__lowercase = (COULOMBS_CONSTANT * charge_product / abs(_SCREAMING_SNAKE_CASE )) ** 0.5
return {"distance": distance}
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = SwinvaConfig()
__lowercase = swinva_name.split("_" )
__lowercase = name_split[1]
if "to" in name_split[3]:
__lowercase = int(name_split[3][-3:] )
else:
__lowercase = int(name_split[3] )
if "to" in name_split[2]:
__lowercase = int(name_split[2][-2:] )
else:
__lowercase = int(name_split[2][6:] )
if model_size == "tiny":
__lowercase = 9_6
__lowercase = (2, 2, 6, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "small":
__lowercase = 9_6
__lowercase = (2, 2, 1_8, 2)
__lowercase = (3, 6, 1_2, 2_4)
elif model_size == "base":
__lowercase = 1_2_8
__lowercase = (2, 2, 1_8, 2)
__lowercase = (4, 8, 1_6, 3_2)
else:
__lowercase = 1_9_2
__lowercase = (2, 2, 1_8, 2)
__lowercase = (6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
__lowercase = (1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__lowercase = 2_1_8_4_1
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-22k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
else:
__lowercase = 1_0_0_0
__lowercase = "huggingface/label-files"
__lowercase = "imagenet-1k-id2label.json"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = img_size
__lowercase = num_classes
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
return config
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
if "patch_embed.proj" in name:
__lowercase = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
__lowercase = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
__lowercase = "encoder." + name
if "attn.proj" in name:
__lowercase = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
__lowercase = name.replace("attn" , "attention.self" )
if "norm1" in name:
__lowercase = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
__lowercase = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
__lowercase = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
__lowercase = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
__lowercase = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
__lowercase = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
__lowercase = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
__lowercase = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
__lowercase = "layernorm.weight"
if name == "norm.bias":
__lowercase = "layernorm.bias"
if "head" in name:
__lowercase = name.replace("head" , "classifier" )
else:
__lowercase = "swinv2." + name
return name
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for key in orig_state_dict.copy().keys():
__lowercase = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
__lowercase = key.split("." )
__lowercase = int(key_split[1] )
__lowercase = int(key_split[3] )
__lowercase = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
__lowercase = val[:dim, :]
__lowercase = val[dim : dim * 2, :]
__lowercase = val[-dim:, :]
else:
__lowercase = val[:dim]
__lowercase = val[
dim : dim * 2
]
__lowercase = val[-dim:]
else:
__lowercase = val
return orig_state_dict
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
__lowercase = get_swinva_config(_SCREAMING_SNAKE_CASE )
__lowercase = SwinvaForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
__lowercase = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
__lowercase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
__lowercase = timm_model(inputs["pixel_values"] )
__lowercase = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
model.push_to_hub(
repo_path_or_name=Path(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swinv2_name""",
default="""swinv2_tiny_patch4_window8_256""",
type=str,
help="""Name of the Swinv2 timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case__ : str = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 655 | 1 |
from __future__ import annotations
import time
snake_case__ : Tuple = list[tuple[int, int]]
snake_case__ : Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
snake_case__ : Any = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class _A :
'''simple docstring'''
def __init__( self : List[Any] , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : Node | None ):
'''simple docstring'''
__lowercase = pos_x
__lowercase = pos_y
__lowercase = (pos_y, pos_x)
__lowercase = goal_x
__lowercase = goal_y
__lowercase = parent
class _A :
'''simple docstring'''
def __init__( self : int , lowerCamelCase : tuple[int, int] , lowerCamelCase : tuple[int, int] ):
'''simple docstring'''
__lowercase = Node(start[1] , start[0] , goal[1] , goal[0] , lowerCamelCase )
__lowercase = Node(goal[1] , goal[0] , goal[1] , goal[0] , lowerCamelCase )
__lowercase = [self.start]
__lowercase = False
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
while self.node_queue:
__lowercase = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
__lowercase = True
return self.retrace_path(lowerCamelCase )
__lowercase = self.get_successors(lowerCamelCase )
for node in successors:
self.node_queue.append(lowerCamelCase )
if not self.reached:
return [self.start.pos]
return None
def _snake_case ( self : Dict , lowerCamelCase : Node ):
'''simple docstring'''
__lowercase = []
for action in delta:
__lowercase = parent.pos_x + action[1]
__lowercase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCamelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(lowerCamelCase , lowerCamelCase , self.target.pos_y , self.target.pos_x , lowerCamelCase ) )
return successors
def _snake_case ( self : Any , lowerCamelCase : Node | None ):
'''simple docstring'''
__lowercase = node
__lowercase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__lowercase = current_node.parent
path.reverse()
return path
class _A :
'''simple docstring'''
def __init__( self : Any , lowerCamelCase : Any , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = BreadthFirstSearch(lowerCamelCase , lowerCamelCase )
__lowercase = BreadthFirstSearch(lowerCamelCase , lowerCamelCase )
__lowercase = False
def _snake_case ( self : Any ):
'''simple docstring'''
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
__lowercase = self.fwd_bfs.node_queue.pop(0 )
__lowercase = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
__lowercase = True
return self.retrace_bidirectional_path(
lowerCamelCase , lowerCamelCase )
__lowercase = current_bwd_node
__lowercase = current_fwd_node
__lowercase = {
self.fwd_bfs: self.fwd_bfs.get_successors(lowerCamelCase ),
self.bwd_bfs: self.bwd_bfs.get_successors(lowerCamelCase ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(lowerCamelCase )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def _snake_case ( self : int , lowerCamelCase : Node , lowerCamelCase : Node ):
'''simple docstring'''
__lowercase = self.fwd_bfs.retrace_path(lowerCamelCase )
__lowercase = self.bwd_bfs.retrace_path(lowerCamelCase )
bwd_path.pop()
bwd_path.reverse()
__lowercase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
snake_case__ : Dict = (0, 0)
snake_case__ : List[Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
snake_case__ : Any = time.time()
snake_case__ : str = BreadthFirstSearch(init, goal)
snake_case__ : int = bfs.search()
snake_case__ : List[str] = time.time() - start_bfs_time
print("""Unidirectional BFS computation time : """, bfs_time)
snake_case__ : List[Any] = time.time()
snake_case__ : Optional[Any] = BidirectionalBreadthFirstSearch(init, goal)
snake_case__ : Union[str, Any] = bd_bfs.search()
snake_case__ : Union[str, Any] = time.time() - start_bd_bfs_time
print("""Bidirectional BFS computation time : """, bd_bfs_time)
| 655 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
snake_case__ : Optional[Any] = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
snake_case__ : List[str] = {
"""allenai/led-base-16384""": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def snake_case_ ( ):
__lowercase = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
__lowercase = bs[:]
__lowercase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
__lowercase = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
return pairs
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : List[str] = VOCAB_FILES_NAMES
_snake_case : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Optional[int]="replace" , lowerCamelCase : Dict="<s>" , lowerCamelCase : Dict="</s>" , lowerCamelCase : Optional[Any]="</s>" , lowerCamelCase : Any="<s>" , lowerCamelCase : List[str]="<unk>" , lowerCamelCase : Union[str, Any]="<pad>" , lowerCamelCase : Any="<mask>" , lowerCamelCase : str=False , **lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else bos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else eos_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else sep_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else cls_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else unk_token
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__lowercase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else mask_token
super().__init__(
errors=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , add_prefix_space=lowerCamelCase , **lowerCamelCase , )
with open(lowerCamelCase , encoding="utf-8" ) as vocab_handle:
__lowercase = json.load(lowerCamelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
__lowercase = errors # how to handle errors in decoding
__lowercase = bytes_to_unicode()
__lowercase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase , encoding="utf-8" ) as merges_handle:
__lowercase = merges_handle.read().split("\n" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in bpe_merges]
__lowercase = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
__lowercase = {}
__lowercase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__lowercase = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return len(self.encoder )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self : List[Any] , lowerCamelCase : str ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__lowercase = tuple(lowerCamelCase )
__lowercase = get_pairs(lowerCamelCase )
if not pairs:
return token
while True:
__lowercase = min(lowerCamelCase , key=lambda lowerCamelCase : self.bpe_ranks.get(lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(lowerCamelCase ):
try:
__lowercase = word.index(lowerCamelCase , lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__lowercase = j
if word[i] == first and i < len(lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(lowerCamelCase )
__lowercase = new_word
if len(lowerCamelCase ) == 1:
break
else:
__lowercase = get_pairs(lowerCamelCase )
__lowercase = " ".join(lowerCamelCase )
__lowercase = word
return word
def _snake_case ( self : List[Any] , lowerCamelCase : Tuple ):
'''simple docstring'''
__lowercase = []
for token in re.findall(self.pat , lowerCamelCase ):
__lowercase = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase ).split(" " ) )
return bpe_tokens
def _snake_case ( self : Dict , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token ) )
def _snake_case ( self : str , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.decoder.get(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = "".join(lowerCamelCase )
__lowercase = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = os.path.join(
lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + "\n" )
__lowercase = 0
with open(lowerCamelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
__lowercase = token_index
writer.write(" ".join(lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self : Tuple , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__lowercase = [self.cls_token_id]
__lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self : str , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase )) + [1]
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase )) + [1]
def _snake_case ( self : int , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [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 _snake_case ( self : Dict , lowerCamelCase : Any , lowerCamelCase : Tuple=False , **lowerCamelCase : Any ):
'''simple docstring'''
__lowercase = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase ) > 0 and not text[0].isspace()):
__lowercase = " " + text
return (text, kwargs)
def _snake_case ( self : List[Any] , lowerCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , lowerCamelCase : Optional[int] = None , lowerCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[bool] = None , ):
'''simple docstring'''
__lowercase = super()._pad(
encoded_inputs=lowerCamelCase , max_length=lowerCamelCase , padding_strategy=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , )
# Load from model defaults
if return_attention_mask is None:
__lowercase = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__lowercase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__lowercase = len(encoded_inputs["global_attention_mask"] ) != len(lowerCamelCase )
if needs_to_be_padded:
__lowercase = len(lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__lowercase = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
__lowercase = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 655 | 1 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _A ( _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : Any = ConsistencyModelPipeline
_snake_case : Union[str, Any] = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
_snake_case : Optional[Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
_snake_case : str = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = UNetaDModel.from_pretrained(
"diffusers/consistency-models-test" , subfolder="test_unet" , )
return unet
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = UNetaDModel.from_pretrained(
"diffusers/consistency-models-test" , subfolder="test_unet_class_cond" , )
return unet
def _snake_case ( self : Optional[Any] , lowerCamelCase : List[str]=False ):
'''simple docstring'''
if class_cond:
__lowercase = self.dummy_cond_unet
else:
__lowercase = self.dummy_uncond_unet
# Default to CM multistep sampler
__lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
__lowercase = {
"unet": unet,
"scheduler": scheduler,
}
return components
def _snake_case ( self : Tuple , lowerCamelCase : Dict , lowerCamelCase : Optional[int]=0 ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("mps" ):
__lowercase = torch.manual_seed(lowerCamelCase )
else:
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = {
"batch_size": 1,
"num_inference_steps": None,
"timesteps": [22, 0],
"generator": generator,
"output_type": "np",
}
return inputs
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = ConsistencyModelPipeline(**lowerCamelCase )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 32, 32, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components(class_cond=lowerCamelCase )
__lowercase = ConsistencyModelPipeline(**lowerCamelCase )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = 0
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 32, 32, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = ConsistencyModelPipeline(**lowerCamelCase )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = 1
__lowercase = None
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 32, 32, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self : Tuple ):
'''simple docstring'''
__lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components(class_cond=lowerCamelCase )
__lowercase = ConsistencyModelPipeline(**lowerCamelCase )
__lowercase = pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = 1
__lowercase = None
__lowercase = 0
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 32, 32, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self : str ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : str , lowerCamelCase : Tuple=0 , lowerCamelCase : List[str]=False , lowerCamelCase : Tuple="cpu" , lowerCamelCase : Optional[Any]=torch.floataa , lowerCamelCase : Optional[int]=(1, 3, 64, 64) ):
'''simple docstring'''
__lowercase = torch.manual_seed(lowerCamelCase )
__lowercase = {
"num_inference_steps": None,
"timesteps": [22, 0],
"class_labels": 0,
"generator": generator,
"output_type": "np",
}
if get_fixed_latents:
__lowercase = self.get_fixed_latents(seed=lowerCamelCase , device=lowerCamelCase , dtype=lowerCamelCase , shape=lowerCamelCase )
__lowercase = latents
return inputs
def _snake_case ( self : Tuple , lowerCamelCase : Tuple=0 , lowerCamelCase : Dict="cpu" , lowerCamelCase : Optional[int]=torch.floataa , lowerCamelCase : Dict=(1, 3, 64, 64) ):
'''simple docstring'''
if type(lowerCamelCase ) == str:
__lowercase = torch.device(lowerCamelCase )
__lowercase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
__lowercase = randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase , dtype=lowerCamelCase )
return latents
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
__lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
__lowercase = ConsistencyModelPipeline(unet=lowerCamelCase , scheduler=lowerCamelCase )
pipe.to(torch_device=lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_inputs()
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 64, 64, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
__lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
__lowercase = ConsistencyModelPipeline(unet=lowerCamelCase , scheduler=lowerCamelCase )
pipe.to(torch_device=lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_inputs()
__lowercase = 1
__lowercase = None
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 64, 64, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
@require_torch_a
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
__lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
__lowercase = ConsistencyModelPipeline(unet=lowerCamelCase , scheduler=lowerCamelCase )
pipe.to(torch_device=lowerCamelCase , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_inputs(get_fixed_latents=lowerCamelCase , device=lowerCamelCase )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=lowerCamelCase , enable_math=lowerCamelCase , enable_mem_efficient=lowerCamelCase ):
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 64, 64, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@require_torch_a
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
__lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
__lowercase = ConsistencyModelPipeline(unet=lowerCamelCase , scheduler=lowerCamelCase )
pipe.to(torch_device=lowerCamelCase , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_inputs(get_fixed_latents=lowerCamelCase , device=lowerCamelCase )
__lowercase = 1
__lowercase = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=lowerCamelCase , enable_math=lowerCamelCase , enable_mem_efficient=lowerCamelCase ):
__lowercase = pipe(**lowerCamelCase ).images
assert image.shape == (1, 64, 64, 3)
__lowercase = image[0, -3:, -3:, -1]
__lowercase = np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zero." )
if any(p < 0 for p in profit ):
raise ValueError("Profit can not be negative." )
if any(w < 0 for w in weight ):
raise ValueError("Weight can not be negative." )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
__lowercase = [p / w for p, w in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
# Creating a copy of the list and sorting profit/weight in ascending order
__lowercase = sorted(_SCREAMING_SNAKE_CASE )
# declaring useful variables
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = 0
__lowercase = 0
__lowercase = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
__lowercase = sorted_profit_by_weight[length - i - 1]
__lowercase = profit_by_weight.index(_SCREAMING_SNAKE_CASE )
__lowercase = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"""Input profits, weights, and then max_weight (all positive ints) separated by """
"""spaces."""
)
snake_case__ : str = [int(x) for x in input("""Input profits separated by spaces: """).split()]
snake_case__ : str = [int(x) for x in input("""Input weights separated by spaces: """).split()]
snake_case__ : Optional[Any] = int(input("""Max weight allowed: """))
# Function Call
calc_profit(profit, weight, max_weight)
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : int = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[str] = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Tuple = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Optional[Any] = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Optional[int] = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
snake_case__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
# 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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """openai/whisper-base"""
_snake_case : Union[str, Any] = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
_snake_case : Any = """transcriber"""
_snake_case : Any = WhisperProcessor
_snake_case : Optional[int] = WhisperForConditionalGeneration
_snake_case : str = ["""audio"""]
_snake_case : Optional[int] = ["""text"""]
def _snake_case ( self : List[str] , lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.pre_processor(lowerCamelCase , return_tensors="pt" ).input_features
def _snake_case ( self : str , lowerCamelCase : List[Any] ):
'''simple docstring'''
return self.model.generate(inputs=lowerCamelCase )
def _snake_case ( self : List[str] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(lowerCamelCase , skip_special_tokens=lowerCamelCase )[0]
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case__ : Optional[int] = {
"""configuration_owlvit""": [
"""OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""OwlViTConfig""",
"""OwlViTOnnxConfig""",
"""OwlViTTextConfig""",
"""OwlViTVisionConfig""",
],
"""processing_owlvit""": ["""OwlViTProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[Any] = ["""OwlViTFeatureExtractor"""]
snake_case__ : List[Any] = ["""OwlViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Optional[int] = [
"""OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OwlViTModel""",
"""OwlViTPreTrainedModel""",
"""OwlViTTextModel""",
"""OwlViTVisionModel""",
"""OwlViTForObjectDetection""",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
snake_case__ : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _A :
'''simple docstring'''
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
__lowercase = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
__lowercase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
__lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["prompt"]
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
if "image" in inputs:
__lowercase = inputs["image"]
else:
__lowercase = None
if "mask_image" in inputs:
__lowercase = inputs["mask_image"]
else:
__lowercase = None
if "original_image" in inputs:
__lowercase = inputs["original_image"]
else:
__lowercase = None
__lowercase , __lowercase = pipe.encode_prompt(lowerCamelCase )
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase , lowerCamelCase ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = inputs["generator"]
__lowercase = inputs["num_inference_steps"]
__lowercase = inputs["output_type"]
# inputs with prompt converted to embeddings
__lowercase = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
__lowercase = image
if mask_image is not None:
__lowercase = mask_image
if original_image is not None:
__lowercase = original_image
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
__lowercase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
__lowercase = self.get_dummy_inputs(lowerCamelCase )
__lowercase = pipe_loaded(**lowerCamelCase )[0]
__lowercase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
| 655 | 1 |
def snake_case_ ( _SCREAMING_SNAKE_CASE = 1_0 ):
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("Invalid input" )
__lowercase = 1_0**n
__lowercase = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , _SCREAMING_SNAKE_CASE )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(10) = }''')
| 655 |
import numpy as np
snake_case__ : Tuple = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class _A :
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase , __lowercase = np.where(letter == self.SQUARE )
__lowercase = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def _snake_case ( self : List[Any] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = self.SQUARE[indexa - 1, indexa - 1]
return letter
def _snake_case ( self : int , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
__lowercase = message.replace(" " , "" )
__lowercase = message.replace("j" , "i" )
__lowercase = np.empty((2, len(lowerCamelCase )) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape(2 * len(lowerCamelCase ) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[numbers_index * 2] )
__lowercase = int(second_step[(numbers_index * 2) + 1] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = encoded_message + letter
return encoded_message
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
message.replace(" " , "" )
__lowercase = np.empty(2 * len(lowerCamelCase ) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape((2, len(lowerCamelCase )) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[0, numbers_index] )
__lowercase = int(second_step[1, numbers_index] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = decoded_message + letter
return decoded_message
| 655 | 1 |
from __future__ import annotations
from typing import Any
class _A :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = num_of_nodes
__lowercase = []
__lowercase = {}
def _snake_case ( self : Dict , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def _snake_case ( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
__lowercase = self.find_component(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : list[int] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
__lowercase = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__lowercase = self.find_component(lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCamelCase )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = []
__lowercase = 0
__lowercase = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__lowercase = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__lowercase = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
__lowercase = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def snake_case_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _A ( ctypes.Structure ):
'''simple docstring'''
_snake_case : Optional[Any] = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def snake_case_ ( ):
if os.name == "nt":
__lowercase = CursorInfo()
__lowercase = ctypes.windll.kernelaa.GetStdHandle(-1_1 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
__lowercase = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE , ctypes.byref(_SCREAMING_SNAKE_CASE ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def snake_case_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 655 | 1 |
import numpy as np
snake_case__ : Tuple = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class _A :
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__lowercase = np.array(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase , __lowercase = np.where(letter == self.SQUARE )
__lowercase = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def _snake_case ( self : List[Any] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = self.SQUARE[indexa - 1, indexa - 1]
return letter
def _snake_case ( self : int , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
__lowercase = message.replace(" " , "" )
__lowercase = message.replace("j" , "i" )
__lowercase = np.empty((2, len(lowerCamelCase )) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape(2 * len(lowerCamelCase ) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[numbers_index * 2] )
__lowercase = int(second_step[(numbers_index * 2) + 1] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = encoded_message + letter
return encoded_message
def _snake_case ( self : Optional[Any] , lowerCamelCase : str ):
'''simple docstring'''
__lowercase = message.lower()
message.replace(" " , "" )
__lowercase = np.empty(2 * len(lowerCamelCase ) )
for letter_index in range(len(lowerCamelCase ) ):
__lowercase = self.letter_to_numbers(message[letter_index] )
__lowercase = numbers[0]
__lowercase = numbers[1]
__lowercase = first_step.reshape((2, len(lowerCamelCase )) )
__lowercase = ""
for numbers_index in range(len(lowerCamelCase ) ):
__lowercase = int(second_step[0, numbers_index] )
__lowercase = int(second_step[1, numbers_index] )
__lowercase = self.numbers_to_letter(lowerCamelCase , lowerCamelCase )
__lowercase = decoded_message + letter
return decoded_message
| 655 |
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__ : List[Any] = 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 ( _lowercase ):
'''simple docstring'''
_snake_case : List[Any] = """yolos"""
def __init__( self : Union[str, Any] , lowerCamelCase : Union[str, Any]=768 , lowerCamelCase : int=12 , lowerCamelCase : Union[str, Any]=12 , lowerCamelCase : Optional[Any]=3_072 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Dict=0.0 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Any=0.02 , lowerCamelCase : Optional[Any]=1e-12 , lowerCamelCase : Optional[Any]=[512, 864] , lowerCamelCase : str=16 , lowerCamelCase : Dict=3 , lowerCamelCase : str=True , lowerCamelCase : List[Any]=100 , lowerCamelCase : Dict=True , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=1 , lowerCamelCase : str=5 , lowerCamelCase : Any=2 , lowerCamelCase : str=5 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : List[Any]=0.1 , **lowerCamelCase : List[Any] , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
__lowercase = num_detection_tokens
__lowercase = use_mid_position_embeddings
__lowercase = auxiliary_loss
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = version.parse("""1.11""" )
@property
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _snake_case ( self : str ):
'''simple docstring'''
return 1e-4
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return 12
| 655 | 1 |
snake_case__ : List[str] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# Return True if there is node that has not iterated.
__lowercase = [False] * len(_SCREAMING_SNAKE_CASE )
__lowercase = [s]
__lowercase = True
while queue:
__lowercase = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_SCREAMING_SNAKE_CASE )
__lowercase = True
__lowercase = u
return visited[t]
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = [-1] * (len(_SCREAMING_SNAKE_CASE ))
__lowercase = 0
__lowercase = []
__lowercase = [i[:] for i in graph] # Record original cut, copy.
while bfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = float("Inf" )
__lowercase = sink
while s != source:
# Find the minimum value in select path
__lowercase = min(_SCREAMING_SNAKE_CASE , graph[parent[s]][s] )
__lowercase = parent[s]
max_flow += path_flow
__lowercase = sink
while v != source:
__lowercase = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
__lowercase = parent[v]
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
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))
| 655 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Optional[int] = logging.get_logger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = MobileNetVaConfig(layer_norm_eps=0.0_0_1 )
if "_quant" in model_name:
raise ValueError("Quantized models are not supported." )
__lowercase = re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" , _SCREAMING_SNAKE_CASE )
if matches:
__lowercase = float(matches[1] )
__lowercase = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
__lowercase = 1_0_0_1
__lowercase = "imagenet-1k-id2label.json"
__lowercase = "huggingface/label-files"
__lowercase = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ) + 1: v for k, v in idalabel.items()}
__lowercase = "background"
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ( ):
__lowercase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
__lowercase = get_mobilenet_va_config(_SCREAMING_SNAKE_CASE )
# Load 🤗 model
__lowercase = MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
__lowercase = MobileNetVaImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size} , size={"shortest_edge": config.image_size + 3_2} , )
__lowercase = image_processor(images=prepare_img() , return_tensors="pt" )
__lowercase = model(**_SCREAMING_SNAKE_CASE )
__lowercase = outputs.logits
assert logits.shape == (1, 1_0_0_1)
if model_name == "mobilenet_v1_1.0_224":
__lowercase = torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5] )
elif model_name == "mobilenet_v1_0.75_192":
__lowercase = torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3] )
else:
__lowercase = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print("Pushing to the hub..." )
__lowercase = "google/" + model_name
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""mobilenet_v1_1.0_224""",
type=str,
help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt 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."""
)
snake_case__ : Dict = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 655 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="pt" ):
__lowercase = {"add_prefix_space": True} if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and not line.startswith(" " ) else {}
__lowercase = padding_side
return tokenizer(
[line] , max_length=_SCREAMING_SNAKE_CASE , padding="max_length" if pad_to_max_length else None , truncation=_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , ):
__lowercase = input_ids.ne(_SCREAMING_SNAKE_CASE ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : Tuple , lowerCamelCase : Dict , lowerCamelCase : int , lowerCamelCase : Optional[int] , lowerCamelCase : Any , lowerCamelCase : int="train" , lowerCamelCase : List[str]=None , lowerCamelCase : Dict=None , lowerCamelCase : Optional[Any]=None , lowerCamelCase : List[Any]="" , ):
'''simple docstring'''
super().__init__()
__lowercase = Path(lowerCamelCase ).joinpath(type_path + ".source" )
__lowercase = Path(lowerCamelCase ).joinpath(type_path + ".target" )
__lowercase = self.get_char_lens(self.src_file )
__lowercase = max_source_length
__lowercase = max_target_length
assert min(self.src_lens ) > 0, f"""found empty line in {self.src_file}"""
__lowercase = tokenizer
__lowercase = prefix
if n_obs is not None:
__lowercase = self.src_lens[:n_obs]
__lowercase = src_lang
__lowercase = tgt_lang
def __len__( self : Dict ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Optional[int] , lowerCamelCase : Tuple ):
'''simple docstring'''
__lowercase = index + 1 # linecache starts at 1
__lowercase = self.prefix + linecache.getline(str(self.src_file ) , lowerCamelCase ).rstrip("\n" )
__lowercase = linecache.getline(str(self.tgt_file ) , lowerCamelCase ).rstrip("\n" )
assert source_line, f"""empty source line for index {index}"""
assert tgt_line, f"""empty tgt line for index {index}"""
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__lowercase = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCamelCase ) else self.tokenizer
)
__lowercase = self.tokenizer.generator if isinstance(self.tokenizer , lowerCamelCase ) else self.tokenizer
__lowercase = encode_line(lowerCamelCase , lowerCamelCase , self.max_source_length , "right" )
__lowercase = encode_line(lowerCamelCase , lowerCamelCase , self.max_target_length , "right" )
__lowercase = source_inputs["input_ids"].squeeze()
__lowercase = target_inputs["input_ids"].squeeze()
__lowercase = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _snake_case ( lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
return [len(lowerCamelCase ) for x in Path(lowerCamelCase ).open().readlines()]
def _snake_case ( self : Optional[int] , lowerCamelCase : Dict ):
'''simple docstring'''
__lowercase = torch.stack([x["input_ids"] for x in batch] )
__lowercase = torch.stack([x["attention_mask"] for x in batch] )
__lowercase = torch.stack([x["decoder_input_ids"] for x in batch] )
__lowercase = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase )
else self.tokenizer.pad_token_id
)
__lowercase = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCamelCase )
else self.tokenizer.pad_token_id
)
__lowercase = trim_batch(lowerCamelCase , lowerCamelCase )
__lowercase , __lowercase = trim_batch(lowerCamelCase , lowerCamelCase , attention_mask=lowerCamelCase )
__lowercase = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
snake_case__ : List[Any] = getLogger(__name__)
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = get_git_info()
save_json(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , "git_log.json" ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=4 , **_SCREAMING_SNAKE_CASE ):
with open(_SCREAMING_SNAKE_CASE , "w" ) as f:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
with open(_SCREAMING_SNAKE_CASE ) as f:
return json.load(_SCREAMING_SNAKE_CASE )
def snake_case_ ( ):
__lowercase = git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE )
__lowercase = {
"repo_id": str(_SCREAMING_SNAKE_CASE ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return list(map(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
with open(_SCREAMING_SNAKE_CASE , "wb" ) as f:
return pickle.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
def remove_articles(_SCREAMING_SNAKE_CASE ):
return re.sub(R"\b(a|an|the)\b" , " " , _SCREAMING_SNAKE_CASE )
def white_space_fix(_SCREAMING_SNAKE_CASE ):
return " ".join(text.split() )
def remove_punc(_SCREAMING_SNAKE_CASE ):
__lowercase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_SCREAMING_SNAKE_CASE ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE ) ) ) )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = normalize_answer(_SCREAMING_SNAKE_CASE ).split()
__lowercase = normalize_answer(_SCREAMING_SNAKE_CASE ).split()
__lowercase = Counter(_SCREAMING_SNAKE_CASE ) & Counter(_SCREAMING_SNAKE_CASE )
__lowercase = sum(common.values() )
if num_same == 0:
return 0
__lowercase = 1.0 * num_same / len(_SCREAMING_SNAKE_CASE )
__lowercase = 1.0 * num_same / len(_SCREAMING_SNAKE_CASE )
__lowercase = (2 * precision * recall) / (precision + recall)
return fa
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return normalize_answer(_SCREAMING_SNAKE_CASE ) == normalize_answer(_SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE )
__lowercase = 0
for hypo, pred in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
em += exact_match_score(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
em /= len(_SCREAMING_SNAKE_CASE )
return {"em": em}
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
return model_prefix.startswith("rag" )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__lowercase = "dropout_rate"
for p in extra_params:
if getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if not hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and not hasattr(_SCREAMING_SNAKE_CASE , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(_SCREAMING_SNAKE_CASE ) )
delattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
__lowercase = p if hasattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else equivalent_param[p]
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
delattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return hparams, config
| 655 |
from __future__ import annotations
from typing import Any
class _A :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
__lowercase = num_of_nodes
__lowercase = []
__lowercase = {}
def _snake_case ( self : Dict , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def _snake_case ( self : List[Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
__lowercase = self.find_component(lowerCamelCase )
def _snake_case ( self : Union[str, Any] , lowerCamelCase : list[int] , lowerCamelCase : int , lowerCamelCase : int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
__lowercase = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__lowercase = self.find_component(lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCamelCase )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = []
__lowercase = 0
__lowercase = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__lowercase = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__lowercase = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase , __lowercase , __lowercase = edge
__lowercase = self.m_component[u]
__lowercase = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
__lowercase = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def snake_case_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def snake_case_ ( ):
__lowercase = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
__lowercase = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw ).convert("RGB" )
return image
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias") )
# fmt: on
return rename_keys
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = dct.pop(_SCREAMING_SNAKE_CASE )
__lowercase = val
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__lowercase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias""" )
__lowercase = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
__lowercase = torch.cat((q_bias, torch.zeros_like(_SCREAMING_SNAKE_CASE , requires_grad=_SCREAMING_SNAKE_CASE ), v_bias) )
__lowercase = qkv_bias
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = 3_6_4 if "coco" in model_name else 2_2_4
__lowercase = InstructBlipVisionConfig(image_size=_SCREAMING_SNAKE_CASE ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
__lowercase = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__lowercase = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
__lowercase = LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf" , vocab_size=3_2_0_0_1 ).to_dict()
elif "vicuna-13b" in model_name:
__lowercase = LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf" , vocab_size=3_2_0_0_1 ).to_dict()
else:
raise ValueError("Model name not supported" )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
__lowercase = InstructBlipQFormerConfig(vocab_size=3_0_5_2_3 ).to_dict()
__lowercase = InstructBlipConfig(vision_config=_SCREAMING_SNAKE_CASE , text_config=_SCREAMING_SNAKE_CASE , qformer_config=_SCREAMING_SNAKE_CASE )
return config, image_size
@torch.no_grad()
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False ):
__lowercase = AutoTokenizer.from_pretrained("bert-base-uncased" , truncation_side="left" )
qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"} )
if "t5" in model_name:
__lowercase = TaTokenizerFast.from_pretrained("google/flan-t5-xl" , truncation_side="left" )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
__lowercase = LlamaTokenizerFast.from_pretrained(
"huggyllama/llama-7b" , truncation_side="left" , bos_token="</s>" , unk_token="</s>" )
tokenizer.add_special_tokens({"pad_token": "[PAD]"} )
__lowercase , __lowercase = get_blipa_config(_SCREAMING_SNAKE_CASE )
__lowercase = InstructBlipForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval()
__lowercase = {
"instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
"instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
"instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
"instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
}
__lowercase , __lowercase = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
__lowercase = "cuda:1" if torch.cuda.is_available() else "cpu"
__lowercase = "cuda:2" if torch.cuda.is_available() else "cpu"
__lowercase , __lowercase , __lowercase = load_model_and_preprocess(
name=_SCREAMING_SNAKE_CASE , model_type=_SCREAMING_SNAKE_CASE , is_eval=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE )
original_model.eval()
print("Done!" )
# update state dict keys
__lowercase = original_model.state_dict()
__lowercase = create_rename_keys(_SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__lowercase = state_dict.pop(_SCREAMING_SNAKE_CASE )
if key.startswith("Qformer.bert" ):
__lowercase = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
__lowercase = key.replace("self" , "attention" )
if "llm_proj" in key:
__lowercase = key.replace("llm_proj" , "language_projection" )
if "t5_proj" in key:
__lowercase = key.replace("t5_proj" , "language_projection" )
if key.startswith("llm_model" ):
__lowercase = key.replace("llm_model" , "language_model" )
if key.startswith("t5" ):
__lowercase = key.replace("t5" , "language" )
__lowercase = val
# read in qv biases
read_in_q_v_bias(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
__lowercase = load_demo_image()
__lowercase = "What is unusual about this image?"
# create processor
__lowercase = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=_SCREAMING_SNAKE_CASE , image_std=_SCREAMING_SNAKE_CASE )
__lowercase = InstructBlipProcessor(
image_processor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , qformer_tokenizer=_SCREAMING_SNAKE_CASE , )
__lowercase = processor(images=_SCREAMING_SNAKE_CASE , text=_SCREAMING_SNAKE_CASE , return_tensors="pt" ).to(_SCREAMING_SNAKE_CASE )
# make sure processor creates exact same pixel values
__lowercase = vis_processors["eval"](_SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(_SCREAMING_SNAKE_CASE )
__lowercase = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , _SCREAMING_SNAKE_CASE )
original_model.to(_SCREAMING_SNAKE_CASE )
hf_model.to(_SCREAMING_SNAKE_CASE )
with torch.no_grad():
if "vicuna" in model_name:
__lowercase = original_model({"image": original_pixel_values, "text_input": [prompt]} ).logits
__lowercase = hf_model(**_SCREAMING_SNAKE_CASE ).logits
else:
__lowercase = original_model(
{"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]} ).logits
__lowercase = tokenizer("\n" , return_tensors="pt" ).input_ids.to(_SCREAMING_SNAKE_CASE )
__lowercase = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -1_0_0 )
__lowercase = hf_model(**_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE ).logits
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
__lowercase = 1E-4 if "vicuna" in model_name else 1E-5
assert torch.allclose(original_logits.to(logits.device ) , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE )
print("Looks ok!" )
print("Generating with original model..." )
__lowercase = original_model.generate({"image": original_pixel_values, "prompt": prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print("Generating with HF model..." )
__lowercase = hf_model.generate(
**_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , num_beams=5 , max_length=2_5_6 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
__lowercase = 2
print("Original generation:" , _SCREAMING_SNAKE_CASE )
__lowercase = processor.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
__lowercase = [text.strip() for text in output_text]
print("HF generation:" , _SCREAMING_SNAKE_CASE )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
processor.push_to_hub(F"""Salesforce/{model_name}""" )
hf_model.push_to_hub(F"""Salesforce/{model_name}""" )
if __name__ == "__main__":
snake_case__ : Optional[Any] = argparse.ArgumentParser()
snake_case__ : Tuple = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
snake_case__ : Dict = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[str] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
snake_case__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
F"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
F"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
F"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
F"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = []
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token") )
return token
def snake_case_ ( ):
__lowercase = []
head.append(("layernorm.weight", "norm.weight") )
head.append(("layernorm.bias", "norm.bias") )
head.append(("classifier.weight", "head.weight") )
head.append(("classifier.bias", "head.bias") )
return head
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = "imagenet-1k-id2label.json"
__lowercase = 1_0_0_0
__lowercase = "huggingface/label-files"
__lowercase = num_labels
__lowercase = json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="dataset" ) ) , "r" ) )
__lowercase = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = __lowercase = CvtConfig(num_labels=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , labelaid=_SCREAMING_SNAKE_CASE )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("/" , 1 )[-1][4:6] == "13":
__lowercase = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1 )[-1][4:6] == "21":
__lowercase = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__lowercase = [2, 2, 2_0]
__lowercase = [3, 1_2, 1_6]
__lowercase = [1_9_2, 7_6_8, 1_0_2_4]
__lowercase = CvtForImageClassification(_SCREAMING_SNAKE_CASE )
__lowercase = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
__lowercase = image_size
__lowercase = torch.load(_SCREAMING_SNAKE_CASE , map_location=torch.device("cpu" ) )
__lowercase = OrderedDict()
__lowercase = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__lowercase = list_of_state_dict + cls_token(_SCREAMING_SNAKE_CASE )
__lowercase = list_of_state_dict + embeddings(_SCREAMING_SNAKE_CASE )
for cnt in range(config.depth[idx] ):
__lowercase = list_of_state_dict + attention(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
snake_case__ : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"""--cvt_model""",
default="""cvt-w24""",
type=str,
help="""Name of the cvt model you'd like to convert.""",
)
parser.add_argument(
"""--image_size""",
default=3_84,
type=int,
help="""Input Image Size""",
)
parser.add_argument(
"""--cvt_file_name""",
default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
type=str,
help="""Input Image Size""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
snake_case__ : List[str] = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 655 |
from __future__ import annotations
import bisect
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
if hi < 0:
__lowercase = len(_SCREAMING_SNAKE_CASE )
while lo < hi:
__lowercase = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__lowercase = mid + 1
else:
__lowercase = mid
return lo
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ):
sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = 0
__lowercase = len(_SCREAMING_SNAKE_CASE ) - 1
while left <= right:
__lowercase = left + (right - left) // 2
__lowercase = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__lowercase = midpoint - 1
else:
__lowercase = midpoint + 1
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = bisect.bisect_left(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
return index
return None
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if right < left:
return None
__lowercase = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint - 1 )
else:
return binary_search_by_recursion(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , midpoint + 1 , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
snake_case__ : Optional[Any] = input("""Enter numbers separated by comma:\n""").strip()
snake_case__ : Any = sorted(int(item) for item in user_input.split(""","""))
snake_case__ : Any = int(input("""Enter a single number to be found in the list:\n"""))
snake_case__ : List[Any] = binary_search(collection, target)
if result is None:
print(F'''{target} was not found in {collection}.''')
else:
print(F'''{target} was found at position {result} in {collection}.''')
| 655 | 1 |
import math
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = int(math.floor(math.sqrt(_SCREAMING_SNAKE_CASE ) ) )
__lowercase = 0
while arr[min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) - 1] < x:
__lowercase = step
step += int(math.floor(math.sqrt(_SCREAMING_SNAKE_CASE ) ) )
if prev >= n:
return -1
while arr[prev] < x:
__lowercase = prev + 1
if prev == min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
snake_case__ : List[Any] = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ : Dict = [int(item) for item in user_input.split(""",""")]
snake_case__ : str = int(input("""Enter the number to be searched:\n"""))
snake_case__ : int = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(F'''Number {x} is at index {res}''')
| 655 |
import copy
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
from ..auto import CONFIG_MAPPING
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Dict = """conditional_detr"""
_snake_case : Union[str, Any] = ["""past_key_values"""]
_snake_case : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Optional[Any] , lowerCamelCase : int=True , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[int]=3 , lowerCamelCase : Optional[int]=300 , lowerCamelCase : List[Any]=6 , lowerCamelCase : str=2_048 , lowerCamelCase : Any=8 , lowerCamelCase : List[str]=6 , lowerCamelCase : Any=2_048 , lowerCamelCase : List[Any]=8 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : List[str]=0.0 , lowerCamelCase : List[Any]=True , lowerCamelCase : str="relu" , lowerCamelCase : int=256 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[Any]=0.0 , lowerCamelCase : Dict=0.0 , lowerCamelCase : Tuple=0.02 , lowerCamelCase : int=1.0 , lowerCamelCase : Tuple=False , lowerCamelCase : List[str]="sine" , lowerCamelCase : List[Any]="resnet50" , lowerCamelCase : Any=True , lowerCamelCase : Any=False , lowerCamelCase : List[Any]=2 , lowerCamelCase : List[Any]=5 , lowerCamelCase : str=2 , lowerCamelCase : Dict=1 , lowerCamelCase : List[str]=1 , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Dict=5 , lowerCamelCase : List[Any]=2 , lowerCamelCase : Tuple=0.25 , **lowerCamelCase : List[str] , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
__lowercase = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = backbone_config.get("model_type" )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowerCamelCase )
__lowercase = use_timm_backbone
__lowercase = backbone_config
__lowercase = num_channels
__lowercase = num_queries
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = encoder_layers
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
__lowercase = backbone
__lowercase = use_pretrained_backbone
__lowercase = dilation
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = cls_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase , **lowerCamelCase )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _snake_case ( self : str ):
'''simple docstring'''
return self.d_model
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
class _A ( _lowercase ):
'''simple docstring'''
_snake_case : Any = version.parse("""1.11""" )
@property
def _snake_case ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _snake_case ( self : Any ):
'''simple docstring'''
return 1e-5
@property
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
return 12
| 655 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : str , lowerCamelCase : int=13 , lowerCamelCase : Any=7 , lowerCamelCase : Dict=True , lowerCamelCase : Tuple=True , lowerCamelCase : Tuple=True , lowerCamelCase : Optional[Any]=True , lowerCamelCase : List[str]=True , lowerCamelCase : Optional[int]=False , lowerCamelCase : Tuple=False , lowerCamelCase : Dict=False , lowerCamelCase : List[str]=2 , lowerCamelCase : Optional[Any]=99 , lowerCamelCase : Tuple=0 , lowerCamelCase : Optional[int]=32 , lowerCamelCase : Tuple=5 , lowerCamelCase : Optional[Any]=4 , lowerCamelCase : Any=0.1 , lowerCamelCase : Optional[Any]=0.1 , lowerCamelCase : List[Any]=512 , lowerCamelCase : Any=12 , lowerCamelCase : Dict=2 , lowerCamelCase : str=0.02 , lowerCamelCase : Tuple=3 , lowerCamelCase : str=4 , lowerCamelCase : Optional[Any]="last" , lowerCamelCase : int=None , lowerCamelCase : List[Any]=None , ):
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_lengths
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = gelu_activation
__lowercase = sinusoidal_embeddings
__lowercase = causal
__lowercase = asm
__lowercase = n_langs
__lowercase = vocab_size
__lowercase = n_special
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = summary_type
__lowercase = use_proj
__lowercase = scope
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
if self.use_input_lengths:
__lowercase = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__lowercase = None
if self.use_token_type_ids:
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , 2 ).float()
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def _snake_case ( self : int ):
'''simple docstring'''
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def _snake_case ( self : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : int , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : List[str] , lowerCamelCase : str , lowerCamelCase : Optional[Any] , ):
'''simple docstring'''
__lowercase = FlaubertModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase , lengths=lowerCamelCase , langs=lowerCamelCase )
__lowercase = model(lowerCamelCase , langs=lowerCamelCase )
__lowercase = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self : Any , lowerCamelCase : Dict , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Tuple , lowerCamelCase : List[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : List[str] , ):
'''simple docstring'''
__lowercase = FlaubertWithLMHeadModel(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase , token_type_ids=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self : int , lowerCamelCase : Union[str, Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str] , lowerCamelCase : int , lowerCamelCase : str , lowerCamelCase : Tuple , lowerCamelCase : str , lowerCamelCase : Any , ):
'''simple docstring'''
__lowercase = FlaubertForQuestionAnsweringSimple(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase )
__lowercase = model(lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase )
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 _snake_case ( self : Dict , lowerCamelCase : str , lowerCamelCase : Dict , lowerCamelCase : Tuple , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : int , lowerCamelCase : Tuple , ):
'''simple docstring'''
__lowercase = FlaubertForQuestionAnswering(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase )
__lowercase = model(
lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase , cls_index=lowerCamelCase , is_impossible=lowerCamelCase , p_mask=lowerCamelCase , )
__lowercase = model(
lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase , cls_index=lowerCamelCase , is_impossible=lowerCamelCase , )
((__lowercase) , ) = result_with_labels.to_tuple()
__lowercase = model(lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase )
((__lowercase) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def _snake_case ( self : List[str] , lowerCamelCase : int , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[Any] , lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : Any , lowerCamelCase : List[str] , lowerCamelCase : Dict , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
__lowercase = FlaubertForSequenceClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase )
__lowercase = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _snake_case ( self : int , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str] , lowerCamelCase : Dict , lowerCamelCase : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Tuple , lowerCamelCase : str , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[int] , ):
'''simple docstring'''
__lowercase = self.num_labels
__lowercase = FlaubertForTokenClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self : str , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] , lowerCamelCase : Dict , lowerCamelCase : int , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any] , lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any] , ):
'''simple docstring'''
__lowercase = self.num_choices
__lowercase = FlaubertForMultipleChoice(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , labels=lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = config_and_inputs
__lowercase = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"lengths": input_lengths,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class _A ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : List[str] = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
_snake_case : Any = (
{
"""feature-extraction""": FlaubertModel,
"""fill-mask""": FlaubertWithLMHeadModel,
"""question-answering""": FlaubertForQuestionAnsweringSimple,
"""text-classification""": FlaubertForSequenceClassification,
"""token-classification""": FlaubertForTokenClassification,
"""zero-shot""": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def _snake_case ( self : List[str] , lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : int , lowerCamelCase : Dict ):
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : List[Any]=False ):
'''simple docstring'''
__lowercase = super()._prepare_for_class(lowerCamelCase , lowerCamelCase , return_labels=lowerCamelCase )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
__lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase )
__lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase )
return inputs_dict
def _snake_case ( self : List[str] ):
'''simple docstring'''
__lowercase = FlaubertModelTester(self )
__lowercase = ConfigTester(self , config_class=lowerCamelCase , emb_dim=37 )
def _snake_case ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*lowerCamelCase )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*lowerCamelCase )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*lowerCamelCase )
def _snake_case ( self : Optional[int] ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*lowerCamelCase )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*lowerCamelCase )
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*lowerCamelCase )
def _snake_case ( self : Any ):
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*lowerCamelCase )
@slow
def _snake_case ( self : int ):
'''simple docstring'''
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = FlaubertModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
@slow
@require_torch_gpu
def _snake_case ( self : Optional[Any] ):
'''simple docstring'''
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=lowerCamelCase )
__lowercase = self._prepare_for_class(lowerCamelCase , lowerCamelCase )
__lowercase = torch.jit.trace(
lowerCamelCase , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(lowerCamelCase , os.path.join(lowerCamelCase , "traced_model.pt" ) )
__lowercase = torch.jit.load(os.path.join(lowerCamelCase , "traced_model.pt" ) , map_location=lowerCamelCase )
loaded(inputs_dict["input_ids"].to(lowerCamelCase ) , inputs_dict["attention_mask"].to(lowerCamelCase ) )
@require_torch
class _A ( unittest.TestCase ):
'''simple docstring'''
@slow
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
__lowercase = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased" )
__lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
with torch.no_grad():
__lowercase = model(lowerCamelCase )[0]
__lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , lowerCamelCase )
__lowercase = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCamelCase , atol=1e-4 ) )
| 655 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
snake_case__ : Any = logging.get_logger(__name__)
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
_snake_case : Dict = """maskformer-swin"""
_snake_case : List[str] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : List[str] , lowerCamelCase : Any=224 , lowerCamelCase : Optional[Any]=4 , lowerCamelCase : Dict=3 , lowerCamelCase : Tuple=96 , lowerCamelCase : str=[2, 2, 6, 2] , lowerCamelCase : Dict=[3, 6, 12, 24] , lowerCamelCase : Optional[Any]=7 , lowerCamelCase : Any=4.0 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : List[str]=0.0 , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : int="gelu" , lowerCamelCase : Optional[int]=False , lowerCamelCase : List[Any]=0.02 , lowerCamelCase : Tuple=1e-5 , lowerCamelCase : Dict=None , lowerCamelCase : Dict=None , **lowerCamelCase : int , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(lowerCamelCase )
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = layer_norm_eps
__lowercase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowercase = int(embed_dim * 2 ** (len(lowerCamelCase ) - 1) )
__lowercase = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(lowerCamelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase , out_indices=lowerCamelCase , stage_names=self.stage_names )
| 655 | 1 |
snake_case__ : str = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
snake_case__ : List[str] = ["""a""", """b""", """c""", """d""", """e"""]
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = start
# add current to visited
visited.append(_SCREAMING_SNAKE_CASE )
__lowercase = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__lowercase = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# if all neighbors visited add current to sort
sort.append(_SCREAMING_SNAKE_CASE )
# if all vertices haven't been visited select a new one to visit
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
for vertice in vertices:
if vertice not in visited:
__lowercase = topological_sort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# return sort
return sort
if __name__ == "__main__":
snake_case__ : Optional[int] = topological_sort("""a""", [], [])
print(sort)
| 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__lowercase = gray_code_sequence_string(_SCREAMING_SNAKE_CASE )
#
# convert them to integers
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
__lowercase = int(sequence[i] , 2 )
return sequence
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
__lowercase = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
__lowercase = gray_code_sequence_string(bit_count - 1 )
__lowercase = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
__lowercase = "0" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
__lowercase = "1" + smaller_sequence[i]
sequence.append(_SCREAMING_SNAKE_CASE )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
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