





"""simple docstring"""




import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
      import numpy as np
      import torch

      from . import (
          AlbertForPreTraining,
          BartForConditionalGeneration,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          CamembertForMaskedLM,
          CTRLLMHeadModel,
          DistilBertForMaskedLM,
          DistilBertForQuestionAnswering,
          DPRContextEncoder,
          DPRQuestionEncoder,
          DPRReader,
          ElectraForPreTraining,
          FlaubertWithLMHeadModel,
          GPTaLMHeadModel,
          LayoutLMForMaskedLM,
          LxmertForPreTraining,
          LxmertVisualFeatureEncoder,
          OpenAIGPTLMHeadModel,
          RobertaForMaskedLM,
          RobertaForSequenceClassification,
          TaForConditionalGeneration,
          TransfoXLLMHeadModel,
          XLMRobertaForMaskedLM,
          XLMWithLMHeadModel,
          XLNetLMHeadModel,
      )


logging.set_verbosity_info()

lowercase_          =		{
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=True		)    ->       Optional[int]:
  if model_type not in MODEL_CLASSES:
    raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}."""		)

  lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__           =  MODEL_CLASSES[model_type]

  # Initialise TF model
  if config_file in aws_config_map:
    lowercase__           =  cached_file(UpperCAmelCase_		,						UpperCAmelCase_		,						force_download=not use_cached_models		)
  lowercase__           =  config_class.from_json_file(UpperCAmelCase_		)
  lowercase__           =  True
  lowercase__           =  True
  print(F"""Building TensorFlow model from configuration: {config}"""		)
  lowercase__           =  model_class(UpperCAmelCase_		)

  # Load weights from tf checkpoint
  if pytorch_checkpoint_path in aws_config_map.keys():
    lowercase__           =  cached_file(
        UpperCAmelCase_		,						UpperCAmelCase_		,						force_download=not use_cached_models		)
  # Load PyTorch checkpoint in tf2 model:
  lowercase__           =  load_pytorch_checkpoint_in_tfa_model(UpperCAmelCase_		,						UpperCAmelCase_		)

  if compare_with_pt_model:
    lowercase__           =  tf_model(tf_model.dummy_inputs		,						training=UpperCAmelCase_		)  # build the network

    lowercase__           =  torch.load(UpperCAmelCase_		,						map_location='cpu'		)
    lowercase__           =  pt_model_class.from_pretrained(
        pretrained_model_name_or_path=UpperCAmelCase_		,						config=UpperCAmelCase_		,						state_dict=UpperCAmelCase_		)

    with torch.no_grad():
      lowercase__           =  pt_model(**pt_model.dummy_inputs		)

    lowercase__           =  pto[0].numpy()
    lowercase__           =  tfo[0].numpy()
    lowercase__           =  np.amax(np.abs(np_pt - np_tf		)		)
    print(F"""Max absolute difference between models outputs {diff}"""		)
    assert diff <= 2E-2, F"""Error, model absolute difference is >2e-2: {diff}"""

  # Save pytorch-model
  print(F"""Save TensorFlow model to {tf_dump_path}"""		)
  tf_model.save_weights(UpperCAmelCase_		,						save_format='h5'		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		,						)    ->       Union[str, Any]:
  if args_model_type is None:
    lowercase__           =  list(MODEL_CLASSES.keys()		)
  else:
    lowercase__           =  [args_model_type]

  for j, model_type in enumerate(UpperCAmelCase_		,						start=1		):
    print('=' * 100		)
    print(F""" Converting model type {j}/{len(UpperCAmelCase_		)}: {model_type}"""		)
    print('=' * 100		)
    if model_type not in MODEL_CLASSES:
      raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}."""		)

    lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__           =  MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      lowercase__           =  list(aws_model_maps.keys()		)
    if config_shortcut_names_or_path is None:
      lowercase__           =  model_shortcut_names_or_path

    for i, (model_shortcut_name, config_shortcut_name) in enumerate(
        zip(UpperCAmelCase_		,						UpperCAmelCase_		)		,						start=1		):
      print('-' * 100		)
      if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
        if not only_convert_finetuned_models:
          print(F"""    Skipping finetuned checkpoint {model_shortcut_name}"""		)
          continue
        lowercase__           =  model_shortcut_name
      elif only_convert_finetuned_models:
        print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""		)
        continue
      print(
          F"""    Converting checkpoint {i}/{len(UpperCAmelCase_		)}: {model_shortcut_name} - model_type {model_type}"""		)
      print('-' * 100		)

      if config_shortcut_name in aws_config_map:
        lowercase__           =  cached_file(UpperCAmelCase_		,						UpperCAmelCase_		,						force_download=not use_cached_models		)
      else:
        lowercase__           =  config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        lowercase__           =  cached_file(UpperCAmelCase_		,						UpperCAmelCase_		,						force_download=not use_cached_models		)
      else:
        lowercase__           =  model_shortcut_name

      if os.path.isfile(UpperCAmelCase_		):
        lowercase__           =  'converted_model'

      convert_pt_checkpoint_to_tf(
          model_type=UpperCAmelCase_		,						pytorch_checkpoint_path=UpperCAmelCase_		,						config_file=UpperCAmelCase_		,						tf_dump_path=os.path.join(UpperCAmelCase_		,						model_shortcut_name + '-tf_model.h5'		)		,						compare_with_pt_model=UpperCAmelCase_		,						)
      if remove_cached_files:
        os.remove(UpperCAmelCase_		)
        os.remove(UpperCAmelCase_		)


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
      )
      parser.add_argument(
          """--model_type""",
          default=None,
          type=str,
          help=(
              f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
              """convert all the models from AWS."""
          ),
      )
      parser.add_argument(
          """--pytorch_checkpoint_path""",
          default=None,
          type=str,
          help=(
              """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
              """If not given, will download and convert all the checkpoints from AWS."""
          ),
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          help=(
              """The config json file corresponding to the pre-trained model. \n"""
              """This specifies the model architecture. If not given and """
              """--pytorch_checkpoint_path is not given or is a shortcut name """
              """use the configuration associated to the shortcut name on the AWS"""
          ),
      )
      parser.add_argument(
          """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
      )
      parser.add_argument(
          """--use_cached_models""",
          action="""store_true""",
          help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
      )
      parser.add_argument(
          """--remove_cached_files""",
          action="""store_true""",
          help="""Remove pytorch models after conversion (save memory when converting in batches).""",
      )
      parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
      lowercase_          =		parser.parse_args()

      # if args.pytorch_checkpoint_path is not None:
      #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
      #                                 args.pytorch_checkpoint_path,
      #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
      #                                 args.tf_dump_path,
      #                                 compare_with_pt_model=args.compare_with_pt_model,
      #                                 use_cached_models=args.use_cached_models)
      # else:
      convert_all_pt_checkpoints_to_tf(
          args.model_type.lower() if args.model_type is not None else None,
          args.tf_dump_path,
          model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
          if args.pytorch_checkpoint_path is not None
          else None,
          config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
          compare_with_pt_model=args.compare_with_pt_model,
          use_cached_models=args.use_cached_models,
          remove_cached_files=args.remove_cached_files,
          only_convert_finetuned_models=args.only_convert_finetuned_models,
      )









from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowercase_          =		TypeVar("""KEY""")
lowercase_          =		TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase      ,     slots=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (Generic[KEY, VAL]				):
   _UpperCamelCase   :		KEY
   _UpperCamelCase   :		VAL






class     SCREAMING_SNAKE_CASE       (_Item				):


   def __init__(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     super().__init__(a      ,					a	)


   def __bool__(   self      :    str	)->       bool:





     """simple docstring"""







     return False






lowercase_          =		_DeletedItem()
class     SCREAMING_SNAKE_CASE       (MutableMapping[KEY, VAL]				):


   def __init__(   self      :    Tuple      ,					a      :    int = 8      ,					a      :    float = 0.75	)->       None:





     """simple docstring"""







     lowercase__           =  initial_block_size
     lowercase__           =  [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     lowercase__           =  capacity_factor
     lowercase__           =  0


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       int:





     """simple docstring"""







     return hash(a	) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       int:





     """simple docstring"""







     return (ind + 1) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    int      ,					a      :    KEY      ,					a      :    VAL	)->       bool:





     """simple docstring"""







     lowercase__           =  self._buckets[ind]
     if not stored:
       lowercase__           =  _Item(a      ,					a	)
       self._len += 1
       return True
     elif stored.key == key:
       lowercase__           =  _Item(a      ,					a	)
       return True
     else:
       return False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       bool:





     """simple docstring"""







     lowercase__           =  len(self._buckets	) * self._capacity_factor
     return len(self	) >= int(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       bool:





     """simple docstring"""







     if len(self._buckets	) <= self._initial_block_size:
       return False
     lowercase__           =  len(self._buckets	) * self._capacity_factor / 2
     return len(self	) < limit


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     lowercase__           =  self._buckets
     lowercase__           =  [None] * new_size
     lowercase__           =  0
     for item in old_buckets:
       if item:
         self._add_item(item.key      ,					item.val	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) * 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) // 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       Iterator[int]:





     """simple docstring"""







     lowercase__           =  self._get_bucket_index(a	)
     for _ in range(len(self._buckets	)	):
       yield ind
       lowercase__           =  self._get_next_ind(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       if self._try_set(a      ,					a      ,					a	):
         break


   def __setitem__(   self      :    List[Any]      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     if self._is_full():
       self._size_up()

     self._add_item(a      ,					a	)


   def __delitem__(   self      :    str      ,					a      :    KEY	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         raise KeyError(a	)
       if item is _deleted:
         continue
       if item.key == key:
         lowercase__           =  _deleted
         self._len -= 1
         break
     if self._is_sparse():
       self._size_down()


   def __getitem__(   self      :    List[str]      ,					a      :    KEY	)->       VAL:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         break
       if item is _deleted:
         continue
       if item.key == key:
         return item.val
     raise KeyError(a	)


   def __len__(   self      :    Tuple	)->       int:





     """simple docstring"""







     return self._len


   def __iter__(   self      :    int	)->       Iterator[KEY]:





     """simple docstring"""







     yield from (item.key for item in self._buckets if item)


   def __repr__(   self      :    Union[str, Any]	)->       str:





     """simple docstring"""







     lowercase__           =  ' ,'.join(
         f"""{item.key}: {item.val}""" for item in self._buckets if item	)
     return f"""HashMap({val_string})"""


