'''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()

SCREAMING_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      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			List[Any] ,   snake_case_	:			Optional[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Any=False ,   snake_case_	:			Any=True      )   ->				Tuple:


     '''simple docstring'''




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

     UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       MODEL_CLASSES[model_type]

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

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

     if compare_with_pt_model:
          UpperCAmelCase_	   =       tf_model(tf_model.dummy_inputs ,   training=snake_case_      )  # build the network

          UpperCAmelCase_	   =       torch.load(snake_case_ ,   map_location="cpu"      )
          UpperCAmelCase_	   =       pt_model_class.from_pretrained(
              pretrained_model_name_or_path=snake_case_ ,   config=snake_case_ ,   state_dict=snake_case_      )

          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(snake_case_ ,   save_format="h5"      )





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]=None ,   snake_case_	:			List[str]=None ,   snake_case_	:			Optional[int]=False ,   snake_case_	:			List[Any]=False ,   snake_case_	:			Optional[int]=False ,   snake_case_	:			Optional[Any]=False ,   )   ->				List[Any]:


     '''simple docstring'''




     if args_model_type is None:
          UpperCAmelCase_	   =       list(MODEL_CLASSES.keys()      )
     else:
          UpperCAmelCase_	   =       [args_model_type]

     for j, model_type in enumerate(snake_case_ ,   start=1      ):
          print("=" * 1_00      )
          print(f""" Converting model type {j}/{len(snake_case_      )}: {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_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			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(snake_case_ ,   snake_case_      ) ,   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(snake_case_      )}: {model_shortcut_name} - model_type {model_type}"""      )
               print("-" * 1_00      )

               if config_shortcut_name in aws_config_map:
                    UpperCAmelCase_	   =       cached_file(snake_case_ ,   snake_case_ ,   force_download=not use_cached_models      )
               else:
                    UpperCAmelCase_	   =       config_shortcut_name

               if model_shortcut_name in aws_model_maps:
                    UpperCAmelCase_	   =       cached_file(snake_case_ ,   snake_case_ ,   force_download=not use_cached_models      )
               else:
                    UpperCAmelCase_	   =       model_shortcut_name

               if os.path.isfile(snake_case_      ):
                    UpperCAmelCase_	   =       "converted_model"

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					List[Any]  =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.')
    SCREAMING_SNAKE_CASE_:					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,
    )





'''simple docstring'''





def      lowerCAmelCase_    ( snake_case_	:			int = 1_00_00_00      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       limit + 1
     UpperCAmelCase_	   =       [0] * limit
     for first_term in range(1 ,   snake_case_      ):
          for n in range(snake_case_ ,   snake_case_ ,   snake_case_      ):
               UpperCAmelCase_	   =       first_term + n / first_term
               if common_difference % 4:  # d must be divisble by 4
                    continue
               else:
                    common_difference /= 4
                    if (
                        first_term > common_difference
                        and first_term < 4 * common_difference
                    ):  # since x,y,z are positive integers
                         frequency[n] += 1  # so z>0 and a>d ,also 4d<a

     UpperCAmelCase_	   =       sum(1 for x in frequency[1:limit] if x == 10      )

     return count


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




