





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__:    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 				lowerCAmelCase_ (							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())}.")

    UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	int					=     MODEL_CLASSES[model_type]

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

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        UpperCamelCase__:	int					=     cached_file(
            A_    ,A_    ,force_download=not use_cached_models)
    # Load PyTorch checkpoint in tf2 model:
    UpperCamelCase__:	Optional[Any]					=     load_pytorch_checkpoint_in_tfa_model(A_    ,A_)

    if compare_with_pt_model:
        UpperCamelCase__:	Dict					=     tf_model(tf_model.dummy_inputs    ,training=A_)  # build the network

        UpperCamelCase__:	Optional[int]					=     torch.load(A_    ,map_location="cpu")
        UpperCamelCase__:	Any					=     pt_model_class.from_pretrained(
            pretrained_model_name_or_path=A_    ,config=A_    ,state_dict=A_)

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

        UpperCamelCase__:	str					=     pto[0].numpy()
        UpperCamelCase__:	Optional[int]					=     tfo[0].numpy()
        UpperCamelCase__:	Tuple					=     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 				lowerCAmelCase_ (							A_    ,A_    ,A_=None    ,A_=None    ,A_=False    ,A_=False    ,A_=False    ,A_=False    ,):
    if args_model_type is None:
        UpperCamelCase__:	List[Any]					=     list(MODEL_CLASSES.keys())
    else:
        UpperCamelCase__:	Union[str, Any]					=     [args_model_type]

    for j, model_type in enumerate(A_    ,start=1):
        print("=" * 1_00)
        print(F" Converting model type {j}/{len(A_)}: {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__:	Any					=     MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            UpperCamelCase__:	Optional[Any]					=     list(aws_model_maps.keys())
        if config_shortcut_names_or_path is None:
            UpperCamelCase__:	Any					=     model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(A_    ,A_)    ,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__:	Optional[int]					=     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("-" * 1_00)

            if config_shortcut_name in aws_config_map:
                UpperCamelCase__:	int					=     cached_file(A_    ,A_    ,force_download=not use_cached_models)
            else:
                UpperCamelCase__:	Union[str, Any]					=     config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                UpperCamelCase__:	Union[str, Any]					=     cached_file(A_    ,A_    ,force_download=not use_cached_models)
            else:
                UpperCamelCase__:	Optional[Any]					=     model_shortcut_name

            if os.path.isfile(A_):
                UpperCamelCase__:	List[str]					=     "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__":
   A__:    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.''')
   A__:    int  	=	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 __future__ import annotations

A__:    List[Any]  	=	10



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Tuple					=     1
    UpperCamelCase__:	str					=     max(A_)
    while placement <= max_digit:
        # declare and initialize empty buckets
        UpperCamelCase__:	list[list]					=     [[] for _ in range(A_)]
        # split list_of_ints between the buckets
        for i in list_of_ints:
            UpperCamelCase__:	Union[str, Any]					=     int((i / placement) % RADIX)
            buckets[tmp].append(A_)
        # put each buckets' contents into list_of_ints
        UpperCamelCase__:	Union[str, Any]					=     0
        for b in range(A_):
            for i in buckets[b]:
                UpperCamelCase__:	Any					=     i
                a += 1
        # move to next
        placement *= RADIX
    return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()

