"""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									=			{
    '''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 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=False						,					UpperCAmelCase=True					)				->      int:
     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_					       =					MODEL_CLASSES[model_type]

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

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

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

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

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

          snake_case_					       =					pto[0].numpy()
          snake_case_					       =					tfo[0].numpy()
          snake_case_					       =					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(UpperCAmelCase						,					save_format='h5'					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					UpperCAmelCase=False						,					UpperCAmelCase=False						,					UpperCAmelCase=False						,					UpperCAmelCase=False						,					)				->      Union[str, Any]:
     if args_model_type is None:
          snake_case_					       =					list(MODEL_CLASSES.keys()					)
     else:
          snake_case_					       =					[args_model_type]

     for j, model_type in enumerate(UpperCAmelCase						,					start=1					):
          print('=' * 100					)
          print(f' Converting model type {j}/{len(UpperCAmelCase					)}: {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_					       =					MODEL_CLASSES[model_type]

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

          for i, (model_shortcut_name, config_shortcut_name) in enumerate(
              zip(UpperCAmelCase						,					UpperCAmelCase					)						,					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_					       =					model_shortcut_name
               elif only_convert_finetuned_models:
                    print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'					)
                    continue
               print(
                   f'    Converting checkpoint {i}/{len(UpperCAmelCase					)}: {model_shortcut_name} - model_type {model_type}'					)
               print('-' * 100					)

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

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

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

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


if __name__ == "__main__":
    __UpperCamelCase									=			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									=			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,
    )






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length, 2)   , _UpperCAmelCase				)
    else:
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length)   , _UpperCAmelCase				)

    for i, tensor in enumerate(_UpperCAmelCase				):
        if padding_side == "right":
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]
        else:
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]

    return out_tensor.tolist()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				ord(_UpperCAmelCase				)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    __a						       =				unicodedata.category(_UpperCAmelCase				)
    if cat.startswith('''P'''				):
        return True
    return False

@dataclass
class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    PreTrainedTokenizerBase
       UpperCamelCase__  :    Union[bool, str, PaddingStrategy]											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						-100
       UpperCamelCase__  :    str											=						"pt"



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           import torch

           __a						       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
           __a						       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
           __a						       =				self.tokenizer.pad(
               __SCREAMING_SNAKE_CASE     ,		padding=self.padding     ,		max_length=self.max_length     ,		pad_to_multiple_of=self.pad_to_multiple_of     ,		return_tensors='''pt''' if labels is None else None     ,		)

           if labels is None:
               return batch

           __a						       =				torch.tensor(batch['''entity_ids''']).shape[1]
           __a						       =				self.tokenizer.padding_side
           if padding_side == "right":
               __a						       =				[
                   list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
               ]
           else:
               __a						       =				[
                   [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
               ]

           __a						       =				[feature['''ner_tags'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		-1     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				[feature['''original_entity_spans'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		(-1, -1)     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{k: torch.tensor(__SCREAMING_SNAKE_CASE     ,		dtype=torch.intaa) for k, v in batch.items()}

           return batch





