





'''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()

a__			:					Dict       						=						{
    '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		( SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			List[str]=False			,	SCREAMING_SNAKE_CASE_ :			Any=True )							->     List[str]:

       """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(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	force_download=not use_cached_models )
       UpperCAmelCase  =						config_class.from_json_file(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						True
       UpperCAmelCase  =						True
       print(f"Building TensorFlow model from configuration: {config}" )
       UpperCAmelCase  =						model_class(SCREAMING_SNAKE_CASE_ )

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

       if compare_with_pt_model:
              UpperCAmelCase  =						tf_model(tf_model.dummy_inputs			,	training=SCREAMING_SNAKE_CASE_ )  # build the network

              UpperCAmelCase  =						torch.load(SCREAMING_SNAKE_CASE_			,	map_location='''cpu''' )
              UpperCAmelCase  =						pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_			,	config=SCREAMING_SNAKE_CASE_			,	state_dict=SCREAMING_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(SCREAMING_SNAKE_CASE_			,	save_format='''h5''' )
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			Dict=None			,	SCREAMING_SNAKE_CASE_ :			int=None			,	SCREAMING_SNAKE_CASE_ :			str=False			,	SCREAMING_SNAKE_CASE_ :			Optional[int]=False			,	SCREAMING_SNAKE_CASE_ :			Any=False			,	SCREAMING_SNAKE_CASE_ :			Dict=False			,	)							->     Tuple:

       """simple docstring"""


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

       for j, model_type in enumerate(SCREAMING_SNAKE_CASE_			,	start=1 ):
              print('''=''' * 100 )
              print(f" Converting model type {j}/{len(SCREAMING_SNAKE_CASE_ )}: {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() )}." )

              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(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )			,	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
                            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(SCREAMING_SNAKE_CASE_ )}: {model_shortcut_name} - model_type {model_type}" )
                     print('''-''' * 100 )

                     if config_shortcut_name in aws_config_map:
                            UpperCAmelCase  =						cached_file(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	force_download=not use_cached_models )
                     else:
                            UpperCAmelCase  =						config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            UpperCAmelCase  =						cached_file(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	force_download=not use_cached_models )
                     else:
                            UpperCAmelCase  =						model_shortcut_name

                     if os.path.isfile(SCREAMING_SNAKE_CASE_ ):
                            UpperCAmelCase  =						'''converted_model'''

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


if __name__ == "__main__":
    a__			:					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.')
    a__			:					Dict       						=						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	A				:       str      =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





