


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()

_A			=						{
    "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__      : Dict      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Dict=False      ,					SCREAMING_SNAKE_CASE__      : List[Any]=True    ):
     if model_type not in MODEL_CLASSES:
          raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}.'    )

     __UpperCamelCase   							=MODEL_CLASSES[model_type]

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

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

     if compare_with_pt_model:
          __UpperCamelCase   							=tf_model(tf_model.dummy_inputs      ,					training=_UpperCAmelCase    )  # build the network

          __UpperCamelCase   							=torch.load(_UpperCAmelCase      ,					map_location='cpu'    )
          __UpperCamelCase   							=pt_model_class.from_pretrained(
              pretrained_model_name_or_path=_UpperCAmelCase      ,					config=_UpperCAmelCase      ,					state_dict=_UpperCAmelCase    )

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

          __UpperCamelCase   							=pto[0].numpy()
          __UpperCamelCase   							=tfo[0].numpy()
          __UpperCamelCase   							=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__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]=None      ,					SCREAMING_SNAKE_CASE__      : Dict=None      ,					SCREAMING_SNAKE_CASE__      : str=False      ,					SCREAMING_SNAKE_CASE__      : Tuple=False      ,					SCREAMING_SNAKE_CASE__      : Dict=False      ,					SCREAMING_SNAKE_CASE__      : str=False      ,					):
     if args_model_type is None:
          __UpperCamelCase   							=list(MODEL_CLASSES.keys()    )
     else:
          __UpperCamelCase   							=[args_model_type]

     for j, model_type in enumerate(_UpperCAmelCase      ,					start=1    ):
          print('=' * 1_00    )
          print(F' Converting model type {j}/{len(_UpperCAmelCase    )}: {model_type}'    )
          print('=' * 1_00    )
          if model_type not in MODEL_CLASSES:
               raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()    )}.'    )

          __UpperCamelCase   							=MODEL_CLASSES[model_type]

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

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(_UpperCAmelCase      ,					_UpperCAmelCase    )      ,					start=1    ):
               print('-' * 1_00    )
               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
                    __UpperCamelCase   							=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('-' * 1_00    )

               if config_shortcut_name in aws_config_map:
                    __UpperCamelCase   							=cached_file(_UpperCAmelCase      ,					_UpperCAmelCase      ,					force_download=not use_cached_models    )
               else:
                    __UpperCamelCase   							=config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    __UpperCamelCase   							=cached_file(_UpperCAmelCase      ,					_UpperCAmelCase      ,					force_download=not use_cached_models    )
               else:
                    __UpperCamelCase   							=model_shortcut_name

               if os.path.isfile(_UpperCAmelCase    ):
                    __UpperCamelCase   							="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__":
   _A			=						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.')
   _A			=						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 ....utils import logging


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_=None ,       A_=2048   )  ->	Any:
         __UpperCamelCase   							=config.__dict__
         __UpperCamelCase   							=modal_hidden_size
         if num_labels:
              __UpperCamelCase   							=num_labels





