






"""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()

UpperCamelCase							       =    {
    """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        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							int=False     , UpperCamelCase_:							Optional[Any]=True	)       ->							Optional[int]:







  '''simple docstring'''







  if model_type not in MODEL_CLASSES:
    raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()	)}.'''	)

  _a			,	_a			,	_a			,	_a							=	MODEL_CLASSES[model_type]

  # Initialise TF model
  if config_file in aws_config_map:
    _a							=	cached_file(UpperCamelCase_     , UpperCamelCase_     , force_download=not use_cached_models	)
  _a							=	config_class.from_json_file(UpperCamelCase_	)
  _a							=	True
  _a							=	True
  print(f'''Building TensorFlow model from configuration: {config}'''	)
  _a							=	model_class(UpperCamelCase_	)

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

  if compare_with_pt_model:
    _a							=	tf_model(tf_model.dummy_inputs     , training=UpperCamelCase_	)  # build the network

    _a							=	torch.load(UpperCamelCase_     , map_location="cpu"	)
    _a							=	pt_model_class.from_pretrained(
        pretrained_model_name_or_path=UpperCamelCase_     , config=UpperCamelCase_     , state_dict=UpperCamelCase_	)

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

    _a							=	pto[0].numpy()
    _a							=	tfo[0].numpy()
    _a							=	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        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							Any=None     , UpperCamelCase_:							List[Any]=None     , UpperCamelCase_:							str=False     , UpperCamelCase_:							List[str]=False     , UpperCamelCase_:							Optional[int]=False     , UpperCamelCase_:							Optional[int]=False     , )       ->							List[str]:







  '''simple docstring'''







  if args_model_type is None:
    _a							=	list(MODEL_CLASSES.keys()	)
  else:
    _a							=	[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()	)}.'''	)

    _a			,	_a			,	_a			,	_a			,	_a							=	MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      _a							=	list(aws_model_maps.keys()	)
    if config_shortcut_names_or_path is None:
      _a							=	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
        _a							=	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:
        _a							=	cached_file(UpperCamelCase_     , UpperCamelCase_     , force_download=not use_cached_models	)
      else:
        _a							=	config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        _a							=	cached_file(UpperCamelCase_     , UpperCamelCase_     , force_download=not use_cached_models	)
      else:
        _a							=	model_shortcut_name

      if os.path.isfile(UpperCamelCase_	):
        _a							=	"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__":
     UpperCamelCase							       =    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.""")
     UpperCamelCase							       =    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,
     )










"""simple docstring"""




from ..utils import DummyObject, requires_backends




class    lowercase_    (metaclass=_UpperCAmelCase     ):
      A__	:		Any       			=				['''torch''', '''torchsde''']






      def __init__(    self     ,     *a_     ,     **a_	)	->Optional[int]:







        '''simple docstring'''


        requires_backends(self     ,     ["torch", "torchsde"]	)






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     *a_     ,     **a_	)	->List[Any]:







        '''simple docstring'''


        requires_backends(cls     ,     ["torch", "torchsde"]	)






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     *a_     ,     **a_	)	->str:







        '''simple docstring'''


        requires_backends(cls     ,     ["torch", "torchsde"]	)


