


'''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
_UpperCamelCase 							=  """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_UpperCamelCase 							=  direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
_UpperCamelCase 							=  re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCamelCase 							=  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.
_UpperCamelCase 							=  re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_UpperCamelCase 							=  [
    ("""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 (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Any      =							re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"       ,					SCREAMING_SNAKE_CASE							)
     return [m.group(0							) for m in matches]


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Tuple      =							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     __A	:       Optional[Any]      =							{
         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.
     __A	:       str      =							collections.defaultdict(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							collections.defaultdict(SCREAMING_SNAKE_CASE							)
     __A	:       Union[str, Any]      =							collections.defaultdict(SCREAMING_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(SCREAMING_SNAKE_CASE							):
          __A	:       Any      =							None
          if _re_tf_models.match(SCREAMING_SNAKE_CASE							) is not None:
               __A	:       Optional[Any]      =							tf_models
               __A	:       Any      =							_re_tf_models.match(SCREAMING_SNAKE_CASE							).groups()[0]
          elif _re_flax_models.match(SCREAMING_SNAKE_CASE							) is not None:
               __A	:       Optional[Any]      =							flax_models
               __A	:       Dict      =							_re_flax_models.match(SCREAMING_SNAKE_CASE							).groups()[0]
          elif _re_pt_models.match(SCREAMING_SNAKE_CASE							) is not None:
               __A	:       Optional[Any]      =							pt_models
               __A	:       Union[str, Any]      =							_re_pt_models.match(SCREAMING_SNAKE_CASE							).groups()[0]

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

     __A	:       Optional[Any]      =							set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
     __A	:       Optional[Any]      =							list(SCREAMING_SNAKE_CASE							)
     all_models.sort()

     __A	:       Dict      =							{"model_type": all_models}
     __A	:       Tuple      =							[pt_models[t] for t in all_models]
     __A	:       List[Any]      =							[tf_models[t] for t in all_models]
     __A	:       int      =							[flax_models[t] for t in all_models]

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

     __A	:       str      =							[processors[t] for t in all_models]

     return pd.DataFrame(SCREAMING_SNAKE_CASE							)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Any      =							[
         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:
          __A	:       List[Any]      =							[model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"]
          __A	:       Optional[int]      =							[auto_class, f"TF_{auto_class}", f"Flax_{auto_class}"]
          # Loop through all three frameworks
          for module, cls, mapping in zip(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               # The type of pipeline may not exist in this framework
               if not hasattr(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
                    continue
               # First extract all model_names
               __A	:       str      =							[]
               for name in getattr(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							).values():
                    if isinstance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
                         model_names.append(SCREAMING_SNAKE_CASE							)
                    else:
                         model_names.extend(list(SCREAMING_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 (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							get_frameworks_table()
     __A	:       Optional[int]      =							Dataset.from_pandas(SCREAMING_SNAKE_CASE							)

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

     # Sort the model classes to avoid some nondeterministic updates to create false update commits.
     __A	:       str      =							sorted(table.keys()							)
     __A	:       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],
         }							)
     __A	:       str      =							Dataset.from_pandas(SCREAMING_SNAKE_CASE							)

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

          if commit_sha is not None:
               __A	:       List[Any]      =							(
                   f"Update with commit {commit_sha}\n\nSee: "
                   f"https://github.com/huggingface/transformers/commit/{commit_sha}"
               )
          else:
               __A	:       Optional[Any]      =							"Update"

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


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Any      =							{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
     __A	:       int      =							transformers_module.pipelines.SUPPORTED_TASKS
     __A	:       Optional[Any]      =							[]
     for key in pipeline_tasks:
          if key not in in_table:
               __A	:       List[str]      =							pipeline_tasks[key]["pt"]
               if isinstance(SCREAMING_SNAKE_CASE       ,					(list, tuple)							):
                    __A	:       Optional[Any]      =							model[0]
               __A	:       Optional[int]      =							model.__name__
               if model not in in_table.values():
                    missing.append(SCREAMING_SNAKE_CASE							)

     if len(SCREAMING_SNAKE_CASE							) > 0:
          __A	:       Optional[int]      =							", ".join(SCREAMING_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__":
      _UpperCamelCase 							=  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.""")
      _UpperCamelCase 							=  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 GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser(
          description=(
              """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
              """ Distillation"""
          )
      )
      parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
      parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
      parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
      parser.add_argument("""--vocab_transform""", action="""store_true""")
      _UpperCamelCase 							=  parser.parse_args()

      if args.model_type == "roberta":
            _UpperCamelCase 							=  RobertaForMaskedLM.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """roberta"""
      elif args.model_type == "gpt2":
            _UpperCamelCase 							=  GPTaLMHeadModel.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """transformer"""

      _UpperCamelCase 							=  model.state_dict()
      _UpperCamelCase 							=  {}

      # Embeddings #
      if args.model_type == "gpt2":
            for param_name in ["wte.weight", "wpe.weight"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.{param_name}"""]
      else:
            for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.{w}.weight"""
                  _UpperCamelCase 							=  state_dict[param_name]
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.LayerNorm.{w}"""
                  _UpperCamelCase 							=  state_dict[param_name]

    # Transformer Blocks #
      _UpperCamelCase 							=  0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            if args.model_type == "gpt2":
                  for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                              ]
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
            else:
                  for layer in [
                      "attention.self.query",
                      "attention.self.key",
                      "attention.self.value",
                      "attention.output.dense",
                      "attention.output.LayerNorm",
                      "intermediate.dense",
                      "output.dense",
                      "output.LayerNorm",
                  ]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
                              ]
            std_idx += 1

      # Language Modeling Head ###s
      if args.model_type == "roberta":
            for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{layer}"""]
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        _UpperCamelCase 							=  state_dict[F"""lm_head.dense.{w}"""]
                        _UpperCamelCase 							=  state_dict[F"""lm_head.layer_norm.{w}"""]
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.ln_f.{w}"""]
            _UpperCamelCase 							=  state_dict["""lm_head.weight"""]

      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)

