



"""simple docstring"""






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__snake_case				:  List[Any] =       'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
__snake_case				:  List[Any] =       direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__snake_case				:  int =       re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__snake_case				:  Any =       re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__snake_case				:  Tuple =       re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__snake_case				:  Optional[int] =       [
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]


def      _lowercase							(    __snake_case      )		->  Any:
			__lowerCAmelCase :    Optional[Any]				=			re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"   ,__snake_case      )
			return [m.group(0      ) for m in matches]


def      _lowercase							(    )		->  str:
			__lowerCAmelCase :    List[Any]				=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			__lowerCAmelCase :    str				=			{
			    config.replace("Config"   ,""      ): model_type for model_type, config in config_maping_names.items()
			}

			# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
			__lowerCAmelCase :    Union[str, Any]				=			collections.defaultdict(__snake_case      )
			__lowerCAmelCase :    Union[str, Any]				=			collections.defaultdict(__snake_case      )
			__lowerCAmelCase :    Optional[int]				=			collections.defaultdict(__snake_case      )

			# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
			for attr_name in dir(__snake_case      ):
						__lowerCAmelCase :    Optional[int]				=			None
						if _re_tf_models.match(__snake_case      ) is not None:
									__lowerCAmelCase :    Optional[int]				=			tf_models
									__lowerCAmelCase :    Union[str, Any]				=			_re_tf_models.match(__snake_case      ).groups()[0]
						elif _re_flax_models.match(__snake_case      ) is not None:
									__lowerCAmelCase :    Dict				=			flax_models
									__lowerCAmelCase :    Tuple				=			_re_flax_models.match(__snake_case      ).groups()[0]
						elif _re_pt_models.match(__snake_case      ) is not None:
									__lowerCAmelCase :    Any				=			pt_models
									__lowerCAmelCase :    Dict				=			_re_pt_models.match(__snake_case      ).groups()[0]

						if lookup_dict is not None:
									while len(__snake_case      ) > 0:
												if attr_name in model_prefix_to_model_type:
															__lowerCAmelCase :    str				=			True
															break
												# Try again after removing the last word in the name
												__lowerCAmelCase :    Dict				=			"".join(camel_case_split(__snake_case      )[:-1]      )

			__lowerCAmelCase :    List[str]				=			set(list(pt_models.keys()      ) + list(tf_models.keys()      ) + list(flax_models.keys()      )      )
			__lowerCAmelCase :    int				=			list(__snake_case      )
			all_models.sort()

			__lowerCAmelCase :    Any				=			{"model_type": all_models}
			__lowerCAmelCase :    Optional[int]				=			[pt_models[t] for t in all_models]
			__lowerCAmelCase :    int				=			[tf_models[t] for t in all_models]
			__lowerCAmelCase :    Union[str, Any]				=			[flax_models[t] for t in all_models]

			# Now let's use the auto-mapping names to make sure
			__lowerCAmelCase :    Dict				=			{}
			for t in all_models:
						if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
									__lowerCAmelCase :    Dict				=			"AutoProcessor"
						elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
									__lowerCAmelCase :    Optional[int]				=			"AutoTokenizer"
						elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
									__lowerCAmelCase :    List[str]				=			"AutoFeatureExtractor"
						else:
									# Default to AutoTokenizer if a model has nothing, for backward compatibility.
									__lowerCAmelCase :    List[Any]				=			"AutoTokenizer"

			__lowerCAmelCase :    Union[str, Any]				=			[processors[t] for t in all_models]

			return pd.DataFrame(__snake_case      )


def      _lowercase							(    __snake_case      )		->  Any:
			__lowerCAmelCase :    Dict				=			[
			    transformers_module.models.auto.modeling_auto,
			    transformers_module.models.auto.modeling_tf_auto,
			    transformers_module.models.auto.modeling_flax_auto,
			]
			for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
						__lowerCAmelCase :    Union[str, Any]				=			[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
						__lowerCAmelCase :    Optional[int]				=			[auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
						# Loop through all three frameworks
						for module, cls, mapping in zip(__snake_case   ,__snake_case   ,__snake_case      ):
									# The type of pipeline may not exist in this framework
									if not hasattr(__snake_case   ,__snake_case      ):
												continue
									# First extract all model_names
									__lowerCAmelCase :    Dict				=			[]
									for name in getattr(__snake_case   ,__snake_case      ).values():
												if isinstance(__snake_case   ,__snake_case      ):
															model_names.append(__snake_case      )
												else:
															model_names.extend(list(__snake_case      )      )

            # Add pipeline tag and auto model class for those models
									table.update({model_name: (pipeline_tag, cls) for model_name in model_names}      )

			return table


def      _lowercase							(    __snake_case   ,__snake_case      )		->  int:
			__lowerCAmelCase :    Dict				=			get_frameworks_table()
			__lowerCAmelCase :    Union[str, Any]				=			Dataset.from_pandas(__snake_case      )

			__lowerCAmelCase :    Optional[int]				=			hf_hub_download(
			    "huggingface/transformers-metadata"   ,"pipeline_tags.json"   ,repo_type="dataset"   ,token=__snake_case      )
			__lowerCAmelCase :    Dict				=			Dataset.from_json(__snake_case      )
			__lowerCAmelCase :    str				=			{
			    tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
			    for i in range(len(__snake_case      )      )
			}
			__lowerCAmelCase :    str				=			update_pipeline_and_auto_class_table(__snake_case      )

			# Sort the model classes to avoid some nondeterministic updates to create false update commits.
			__lowerCAmelCase :    str				=			sorted(table.keys()      )
			__lowerCAmelCase :    List[Any]				=			pd.DataFrame(
			    {
			        "model_class": model_classes,
			        "pipeline_tag": [table[m][0] for m in model_classes],
			        "auto_class": [table[m][1] for m in model_classes],
			    }      )
			__lowerCAmelCase :    List[str]				=			Dataset.from_pandas(__snake_case      )

			with tempfile.TemporaryDirectory() as tmp_dir:
						frameworks_dataset.to_json(os.path.join(__snake_case   ,"frameworks.json"      )      )
						tags_dataset.to_json(os.path.join(__snake_case   ,"pipeline_tags.json"      )      )

						if commit_sha is not None:
									__lowerCAmelCase :    Union[str, Any]				=			(
									    F"""Update with commit {commit_sha}\n\nSee: """
									    F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
									)
						else:
									__lowerCAmelCase :    str				=			"Update"

						upload_folder(
						    repo_id="huggingface/transformers-metadata"   ,folder_path=__snake_case   ,repo_type="dataset"   ,token=__snake_case   ,commit_message=__snake_case   ,)


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Optional[Any]				=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
			__lowerCAmelCase :    Optional[Any]				=			transformers_module.pipelines.SUPPORTED_TASKS
			__lowerCAmelCase :    Optional[Any]				=			[]
			for key in pipeline_tasks:
						if key not in in_table:
									__lowerCAmelCase :    int				=			pipeline_tasks[key]["pt"]
									if isinstance(__snake_case   ,(list, tuple)      ):
												__lowerCAmelCase :    Optional[int]				=			model[0]
									__lowerCAmelCase :    Tuple				=			model.__name__
									if model not in in_table.values():
												missing.append(__snake_case      )

			if len(__snake_case      ) > 0:
						__lowerCAmelCase :    int				=			", ".join(__snake_case      )
						raise ValueError(
						    "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
						    F"""`utils/update_metadata.py`: {msg}. Please add them!"""      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
	parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
	parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
	__snake_case				:  Any =       parser.parse_args()

	if args.check_only:
		check_pipeline_tags()
	else:
		update_metadata(args.token, args.commit_sha)




"""simple docstring"""






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	__snake_case				:  Optional[Any] =       argparse.ArgumentParser(
	    description=(
	        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
	        ' Distillation'
	    )
	)
	parser.add_argument('--model_type', default='bert', choices=['bert'])
	parser.add_argument('--model_name', default='bert-base-uncased', type=str)
	parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
	parser.add_argument('--vocab_transform', action='store_true')
	__snake_case				:  List[Any] =       parser.parse_args()

	if args.model_type == "bert":
		__snake_case				:  int =       BertForMaskedLM.from_pretrained(args.model_name)
		__snake_case				:  Tuple =       'bert'
	else:
		raise ValueError('args.model_type should be "bert".')

	__snake_case				:  List[Any] =       model.state_dict()
	__snake_case				:  Any =       {}

	for w in ["word_embeddings", "position_embeddings"]:
		__snake_case				:  List[str] =       state_dict[F"""{prefix}.embeddings.{w}.weight"""]
	for w in ["weight", "bias"]:
		__snake_case				:  Optional[int] =       state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

	__snake_case				:  List[str] =       0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		for w in ["weight", "bias"]:
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
			]
			__snake_case				:  Tuple =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
			]
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
			]

			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
			]
			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
			]

			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
			]
			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
			]
			__snake_case				:  Optional[int] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
			]
		std_idx += 1

	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.decoder.weight']
	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.bias']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			__snake_case				:  Union[str, Any] =       state_dict[F"""cls.predictions.transform.dense.{w}"""]
			__snake_case				:  List[str] =       state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

	print(F"""N layers selected for distillation: {std_idx}""")
	print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

	print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
	torch.save(compressed_sd, args.dump_checkpoint)