


"""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()

lowerCamelCase_   :   int									=  {
    """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						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase=False       ,    lowercase=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 		=MODEL_CLASSES[model_type]

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

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

    if compare_with_pt_model:
        a 		=tf_model(tf_model.dummy_inputs       ,    training=lowercase		)  # build the network

        a 		=torch.load(lowercase       ,    map_location='''cpu'''		)
        a 		=pt_model_class.from_pretrained(
            pretrained_model_name_or_path=lowercase       ,    config=lowercase       ,    state_dict=lowercase		)

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

        a 		=pto[0].numpy()
        a 		=tfo[0].numpy()
        a 		=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(lowercase       ,    save_format='''h5'''		)
def _A						(						lowercase       ,    lowercase       ,    lowercase=None       ,    lowercase=None       ,    lowercase=False       ,    lowercase=False       ,    lowercase=False       ,    lowercase=False       ,    ):







    """simple docstring"""
    if args_model_type is None:
        a 		=list(MODEL_CLASSES.keys()		)
    else:
        a 		=[args_model_type]

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

        a		,      a		,      a		,      a		,      a 		=MODEL_CLASSES[model_type]

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

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(lowercase       ,    lowercase		)       ,    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
                a 		=model_shortcut_name
            elif only_convert_finetuned_models:
                print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                continue
            print(
                f'''    Converting checkpoint {i}/{len(lowercase		)}: {model_shortcut_name} - model_type {model_type}'''		)
            print('''-''' * 1_00		)

            if config_shortcut_name in aws_config_map:
                a 		=cached_file(lowercase       ,    lowercase       ,    force_download=not use_cached_models		)
            else:
                a 		=config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                a 		=cached_file(lowercase       ,    lowercase       ,    force_download=not use_cached_models		)
            else:
                a 		=model_shortcut_name

            if os.path.isfile(lowercase		):
                a 		='''converted_model'''

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


if __name__ == "__main__":
       lowerCamelCase_   :   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.""")
       lowerCamelCase_   :   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,
       )






"""simple docstring"""
from collections.abc import Callable

import numpy as np



def      __lowerCamelCase   (							a_     :		Callable ,   a_     :		float ,   a_     :		float ,   a_     :		float ,   a_     :		float							)			->      np.ndarray:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(np.ceil((x_end - xa) / step_size							)							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.zeros((n + 1,)							)
      __SCREAMING_SNAKE_CASE   :int						=  ya
      __SCREAMING_SNAKE_CASE   :str						=  xa

      for k in range(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  y[k] + step_size * ode_func(a_ ,   y[k]							)
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()