



"""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()

lowercase__    :  str							    =	{
    '''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 __lowercase			(							_a		,			_a		,			_a		,			_a		,			_a=False		,			_a=True    ):
    if model_type not in MODEL_CLASSES:
        raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()    )}."    )

    snake_case_,	snake_case_,	snake_case_,	snake_case_ :     Any				=	MODEL_CLASSES[model_type]

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

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

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

        snake_case_ :     Any				=	torch.load(_a		,			map_location='''cpu'''    )
        snake_case_ :     Any				=	pt_model_class.from_pretrained(
            pretrained_model_name_or_path=_a		,			config=_a		,			state_dict=_a    )

        with torch.no_grad():
            snake_case_ :     Any				=	pt_model(**pt_model.dummy_inputs    )

        snake_case_ :     str				=	pto[0].numpy()
        snake_case_ :     List[Any]				=	tfo[0].numpy()
        snake_case_ :     List[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 __lowercase			(							_a		,			_a		,			_a=None		,			_a=None		,			_a=False		,			_a=False		,			_a=False		,			_a=False		,			):
    if args_model_type is None:
        snake_case_ :     Tuple				=	list(MODEL_CLASSES.keys()    )
    else:
        snake_case_ :     Optional[Any]				=	[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()    )}."    )

        snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_ :     Union[str, Any]				=	MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            snake_case_ :     Optional[Any]				=	list(aws_model_maps.keys()    )
        if config_shortcut_names_or_path is None:
            snake_case_ :     int				=	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
                snake_case_ :     Union[str, 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:
                snake_case_ :     List[str]				=	cached_file(_a		,			_a		,			force_download=not use_cached_models    )
            else:
                snake_case_ :     Union[str, Any]				=	config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                snake_case_ :     Union[str, Any]				=	cached_file(_a		,			_a		,			force_download=not use_cached_models    )
            else:
                snake_case_ :     Tuple				=	model_shortcut_name

            if os.path.isfile(_a    ):
                snake_case_ :     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__":
     lowercase__    :  Optional[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.''')
     lowercase__    :  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 __future__ import annotations




def __lowercase			(							_a		,			_a		,			_a		,			):
    if (stress, tangential_force, area).count(0    ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''    )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''    )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''    )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''    )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





