
'''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()

__snake_case:  Tuple								=			{
    "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 	_snake_case						(      A_       :    Tuple     ,		A_       :    str     ,		A_       :    Optional[int]     ,		A_       :    Union[str, Any]     ,		A_       :    Tuple=False     ,		A_       :    Any=True   ):
  """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_      :	List[str]      =							MODEL_CLASSES[model_type]

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

  # Load weights from tf checkpoint
  if pytorch_checkpoint_path in aws_config_map.keys():
    a_      :	Tuple      =							cached_file(
        A_     ,		A_     ,		force_download=not use_cached_models   )
  # Load PyTorch checkpoint in tf2 model:
  a_      :	Union[str, Any]      =							load_pytorch_checkpoint_in_tfa_model(A_     ,		A_   )

  if compare_with_pt_model:
    a_      :	Union[str, Any]      =							tf_model(tf_model.dummy_inputs     ,		training=A_   )  # build the network

    a_      :	str      =							torch.load(A_     ,		map_location="""cpu"""   )
    a_      :	List[Any]      =							pt_model_class.from_pretrained(
        pretrained_model_name_or_path=A_     ,		config=A_     ,		state_dict=A_   )

    with torch.no_grad():
      a_      :	List[Any]      =							pt_model(**pt_model.dummy_inputs   )

    a_      :	int      =							pto[0].numpy()
    a_      :	List[Any]      =							tfo[0].numpy()
    a_      :	Optional[int]      =							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 	_snake_case						(      A_       :    Optional[Any]     ,		A_       :    Optional[Any]     ,		A_       :    List[Any]=None     ,		A_       :    Dict=None     ,		A_       :    Optional[int]=False     ,		A_       :    Union[str, Any]=False     ,		A_       :    Tuple=False     ,		A_       :    Union[str, Any]=False     ,		):
  """simple docstring"""







  if args_model_type is None:
    a_      :	List[Any]      =							list(MODEL_CLASSES.keys()   )
  else:
    a_      :	Dict      =							[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_      :	Any      =							MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      a_      :	Optional[Any]      =							list(aws_model_maps.keys()   )
    if config_shortcut_names_or_path is None:
      a_      :	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_      :	Optional[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_      :	List[str]      =							cached_file(A_     ,		A_     ,		force_download=not use_cached_models   )
      else:
        a_      :	List[str]      =							config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        a_      :	Union[str, Any]      =							cached_file(A_     ,		A_     ,		force_download=not use_cached_models   )
      else:
        a_      :	Optional[int]      =							model_shortcut_name

      if os.path.isfile(A_   ):
        a_      :	Optional[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__":
    __snake_case:  Optional[int]								=			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.")
    __snake_case:  Tuple								=			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 math import factorial

__snake_case:  List[Any]								=			{str(d): factorial(d) for d in range(10)}






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  return sum(DIGIT_FACTORIAL[d] for d in str(A_   )   )






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Union[str, Any]      =							7 * factorial(9   ) + 1
  return sum(i for i in range(3     ,		A_   ) if sum_of_digit_factorial(A_   ) == i   )


if __name__ == "__main__":
    print(F"""{solution() = }""")


