
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   =	{
    """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    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Dict      ,					UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Dict=False      ,					UpperCAmelCase__					:   Optional[Any]=True							)	->     Dict:
							if model_type not in MODEL_CLASSES:
														raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()							)}.'''							)

							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   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Dict=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Optional[int]=False      ,					UpperCAmelCase__					:   Optional[int]=False      ,					UpperCAmelCase__					:   List[Any]=False      ,					UpperCAmelCase__					:   Tuple=False      ,					)	->     int:
							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("""=""" * 1_0_0							)
														print(F''' Converting model type {j}/{len(__snake_case							)}: {model_type}'''							)
														print("""=""" * 1_0_0							)
														if model_type not in MODEL_CLASSES:
																					raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()							)}.'''							)

														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("""-""" * 1_0_0							)
																					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("""-""" * 1_0_0							)

																					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__":
		lowercase   =	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   =	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"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


