

'''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()

_UpperCamelCase						:  Optional[Any]							       =				{
    "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   (	snake_case      : Optional[Any]					,		snake_case      : int					,		snake_case      : Optional[Any]					,		snake_case      : int					,		snake_case      : str=False					,		snake_case      : Union[str, Any]=True			) ->  Any:


    """simple docstring"""


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

    lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							MODEL_CLASSES[model_type]

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

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

    if compare_with_pt_model:
        lowerCAmelCase 						=							tf_model(tf_model.dummy_inputs					,		training=snake_case			)  # build the network

        lowerCAmelCase 						=							torch.load(snake_case					,		map_location='cpu'			)
        lowerCAmelCase 						=							pt_model_class.from_pretrained(
            pretrained_model_name_or_path=snake_case					,		config=snake_case					,		state_dict=snake_case			)

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

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


    """simple docstring"""


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

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

        lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							MODEL_CLASSES[model_type]

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

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(snake_case					,		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
                lowerCAmelCase 						=							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('-' * 100			)

            if config_shortcut_name in aws_config_map:
                lowerCAmelCase 						=							cached_file(snake_case					,		snake_case					,		force_download=not use_cached_models			)
            else:
                lowerCAmelCase 						=							config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                lowerCAmelCase 						=							cached_file(snake_case					,		snake_case					,		force_download=not use_cached_models			)
            else:
                lowerCAmelCase 						=							model_shortcut_name

            if os.path.isfile(snake_case			):
                lowerCAmelCase 						=							'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__":
     _UpperCamelCase						:  Dict							       =				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.")
     _UpperCamelCase						:  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'''



def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    # edges = list of graph's edges
    lowerCAmelCase 						=							get_edges(snake_case			)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCAmelCase						,   lowerCAmelCase 						=							edges.pop()
        chosen_vertices.add(snake_case			)
        chosen_vertices.add(snake_case			)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case			)
    return chosen_vertices


def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)			)
    return edges


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
     # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





