


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					:       Dict			    =     {
    """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_				(				__a			,				__a			,				__a			,				__a			,				__a=False			,				__a=True      )	->   Tuple:
      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__	:  List[str]     =   MODEL_CLASSES[model_type]

      # Initialise TF model
      if config_file in aws_config_map:
            a__	:  Optional[Any]     =   cached_file(__a			,				__a			,				force_download=not use_cached_models      )
      a__	:  Tuple     =   config_class.from_json_file(__a      )
      a__	:  List[Any]     =   True
      a__	:  Optional[int]     =   True
      print(f'''Building TensorFlow model from configuration: {config}'''      )
      a__	:  List[str]     =   model_class(__a      )

      # Load weights from tf checkpoint
      if pytorch_checkpoint_path in aws_config_map.keys():
            a__	:  int     =   cached_file(
                __a			,				__a			,				force_download=not use_cached_models      )
      # Load PyTorch checkpoint in tf2 model:
      a__	:  List[str]     =   load_pytorch_checkpoint_in_tfa_model(__a			,				__a      )

      if compare_with_pt_model:
            a__	:  Optional[int]     =   tf_model(tf_model.dummy_inputs			,				training=__a      )  # build the network

            a__	:  Union[str, Any]     =   torch.load(__a			,				map_location="cpu"      )
            a__	:  Optional[Any]     =   pt_model_class.from_pretrained(
                pretrained_model_name_or_path=__a			,				config=__a			,				state_dict=__a      )

            with torch.no_grad():
                  a__	:  Dict     =   pt_model(**pt_model.dummy_inputs      )

            a__	:  str     =   pto[0].numpy()
            a__	:  Union[str, Any]     =   tfo[0].numpy()
            a__	:  Any     =   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(__a			,				save_format="h5"      )



def UpperCamelCase_				(				__a			,				__a			,				__a=None			,				__a=None			,				__a=False			,				__a=False			,				__a=False			,				__a=False			,				)	->   Dict:
      if args_model_type is None:
            a__	:  str     =   list(MODEL_CLASSES.keys()      )
      else:
            a__	:  int     =   [args_model_type]

      for j, model_type in enumerate(__a			,				start=1      ):
            print("=" * 100      )
            print(f''' Converting model type {j}/{len(__a      )}: {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__	:  List[Any]     =   MODEL_CLASSES[model_type]

            if model_shortcut_names_or_path is None:
                  a__	:  int     =   list(aws_model_maps.keys()      )
            if config_shortcut_names_or_path is None:
                  a__	:  Optional[Any]     =   model_shortcut_names_or_path

            for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                zip(__a			,				__a      )			,				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__	:  Any     =   model_shortcut_name
                  elif only_convert_finetuned_models:
                        print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''      )
                        continue
                  print(
                      f'''    Converting checkpoint {i}/{len(__a      )}: {model_shortcut_name} - model_type {model_type}'''      )
                  print("-" * 100      )

                  if config_shortcut_name in aws_config_map:
                        a__	:  Dict     =   cached_file(__a			,				__a			,				force_download=not use_cached_models      )
                  else:
                        a__	:  str     =   config_shortcut_name

                  if model_shortcut_name in aws_model_maps:
                        a__	:  List[str]     =   cached_file(__a			,				__a			,				force_download=not use_cached_models      )
                  else:
                        a__	:  Optional[Any]     =   model_shortcut_name

                  if os.path.isfile(__a      ):
                        a__	:  int     =   "converted_model"

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


if __name__ == "__main__":
    UpperCamelCase					:       List[str]			    =     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					:       List[Any]			    =     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,
    )









import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:str="sayef/fsner-bert-base-uncased"):



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

          return self.softmax(T * self.cos(snake_case,      snake_case))





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

          _lowercase              =W_supports['sizes'].tolist()
          _lowercase              =W_supports['start_token_id'].item()
          _lowercase              =W_supports['end_token_id'].item()

          del W_supports["sizes"]
          del W_supports["start_token_id"]
          del W_supports["end_token_id"]

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

          _lowercase              =W_supports['input_ids'] == start_token_id
          _lowercase              =W_supports['input_ids'] == end_token_id

          for i, size in enumerate(snake_case):
             if i == 0:
                _lowercase              =0
             else:
                _lowercase              =support_sizes[i - 1]

             _lowercase              =S[s : s + size][start_token_masks[s : s + size]]
             _lowercase              =S[s : s + size][end_token_masks[s : s + size]]

             _lowercase              =torch.matmul(q[i],      s_start.T).sum(1).softmax(0)
             _lowercase              =torch.matmul(q[i],      s_end.T).sum(1).softmax(0)

             if p_starts is not None:
                _lowercase              =torch.vstack((p_starts, p_start))
                _lowercase              =torch.vstack((p_ends, p_end))
             else:
                _lowercase              =p_start
                _lowercase              =p_end

          return p_starts, p_ends


