

'''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     =  {
    '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 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case=False		,							snake_case=True     )			->	List[str]:



   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			:      Tuple			     =   MODEL_CLASSES[model_type]

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

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

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

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

      with torch.no_grad():
         _lowercase			:      List[str]			     =   pt_model(**pt_model.dummy_inputs     )

      _lowercase			:      Any			     =   pto[0].numpy()
      _lowercase			:      Tuple			     =   tfo[0].numpy()
      _lowercase			:      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 					_A		(	snake_case		,							snake_case		,							snake_case=None		,							snake_case=None		,							snake_case=False		,							snake_case=False		,							snake_case=False		,							snake_case=False		,							)			->	List[str]:



   if args_model_type is None:
      _lowercase			:      List[Any]			     =   list(MODEL_CLASSES.keys()     )
   else:
      _lowercase			:      Optional[int]			     =   [args_model_type]

   for j, model_type in enumerate(A__		,							start=1     ):
      print("=" * 1_00     )
      print(F''' Converting model type {j}/{len(A__     )}: {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()     )}.'''     )

      _lowercase     ,      _lowercase     ,      _lowercase     ,      _lowercase     ,      _lowercase			:      List[Any]			     =   MODEL_CLASSES[model_type]

      if model_shortcut_names_or_path is None:
         _lowercase			:      List[Any]			     =   list(aws_model_maps.keys()     )
      if config_shortcut_names_or_path is None:
         _lowercase			:      Tuple			     =   model_shortcut_names_or_path

      for i, (model_shortcut_name, config_shortcut_name) in enumerate(
          zip(A__		,							A__     )		,							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
            _lowercase			:      List[str]			     =   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("-" * 1_00     )

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

         if model_shortcut_name in aws_model_maps:
            _lowercase			:      Tuple			     =   cached_file(A__		,							A__		,							force_download=not use_cached_models     )
         else:
            _lowercase			:      int			     =   model_shortcut_name

         if os.path.isfile(A__     ):
            _lowercase			:      Union[str, Any]			     =   "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     =  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     =  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 __future__ import annotations
def      a      (			A__	:   list[int]     )    ->      int:





      """simple docstring"""



      if not nums:
            return 0
      _lowercase	       =nums[0]
      _lowercase	       =0
      for num in nums[1:]:
            _lowercase       ,       _lowercase	       =(
                max_excluding + num,
                max(A__      ,   A__     ),
            )
      return max(A__      ,   A__     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


