






'''simple docstring'''







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_A	:			str         =logging.get_logger(__name__)

_A	:			str         ={
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_A	:			Dict         =[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							for attribute in key.split("""."""      ):
														lowerCamelCase__					:	Union[str, Any]         =       getattr(UpperCamelCase				,			UpperCamelCase      )

							if weight_type is not None:
														lowerCamelCase__					:	Optional[int]         =       getattr(UpperCamelCase				,			UpperCamelCase      ).shape
							else:
														lowerCamelCase__					:	Optional[int]         =       hf_pointer.shape

							assert hf_shape == value.shape, (
							    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
							    f''' {value.shape} for {full_name}'''
							)

							if weight_type == "weight":
														lowerCamelCase__					:	Optional[Any]         =       value
							elif weight_type == "weight_g":
														lowerCamelCase__					:	str         =       value
							elif weight_type == "weight_v":
														lowerCamelCase__					:	Any         =       value
							elif weight_type == "bias":
														lowerCamelCase__					:	str         =       value
							else:
														lowerCamelCase__					:	Any         =       value

							logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	int         =       []
							lowerCamelCase__					:	Optional[Any]         =       fairseq_model.state_dict()

							lowerCamelCase__					:	Dict         =       hf_model.feature_extractor
							lowerCamelCase__					:	List[str]         =       hf_model.adapter

							for name, value in fairseq_dict.items():
														lowerCamelCase__					:	Optional[int]         =       False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			hf_model.config.feat_extract_norm == """group"""				,			)
																					lowerCamelCase__					:	Tuple         =       True
														elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
																					load_adapter(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
																					lowerCamelCase__					:	List[Any]         =       True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
																																			lowerCamelCase__					:	Dict         =       True
																																			if "*" in mapped_key:
																																										lowerCamelCase__					:	Dict         =       name.split(UpperCamelCase      )[0].split("""."""      )[-2]
																																										lowerCamelCase__					:	Any         =       mapped_key.replace("""*"""				,			UpperCamelCase      )
																																			if "weight_g" in name:
																																										lowerCamelCase__					:	Optional[int]         =       """weight_g"""
																																			elif "weight_v" in name:
																																										lowerCamelCase__					:	Any         =       """weight_v"""
																																			elif "bias" in name:
																																										lowerCamelCase__					:	List[str]         =       """bias"""
																																			elif "weight" in name:
																																										lowerCamelCase__					:	int         =       """weight"""
																																			else:
																																										lowerCamelCase__					:	Optional[Any]         =       None
																																			set_recursively(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
																												continue
														if not is_used:
																					unused_weights.append(UpperCamelCase      )

							logger.warning(f'''Unused weights: {unused_weights}'''      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	Any         =       full_name.split("""conv_layers."""      )[-1]
							lowerCamelCase__					:	int         =       name.split("""."""      )
							lowerCamelCase__					:	List[Any]         =       int(items[0]      )
							lowerCamelCase__					:	List[str]         =       int(items[1]      )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    f'''{full_name} has size {value.shape}, but'''
																					    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
																					)
																					lowerCamelCase__					:	Union[str, Any]         =       value
																					logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    f'''{full_name} has size {value.shape}, but'''
																					    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
																					)
																					lowerCamelCase__					:	List[Any]         =       value
																					logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
																					    " found."
																					)
																					lowerCamelCase__					:	List[Any]         =       value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    f'''{full_name} has size {value.shape}, but'''
																					    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
																					)
																					lowerCamelCase__					:	Dict         =       value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
							else:
														unused_weights.append(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	Union[str, Any]         =       full_name.split("""adaptor."""      )[-1]
							lowerCamelCase__					:	Optional[int]         =       name.split("""."""      )

							if items[1].isdigit():
														lowerCamelCase__					:	Dict         =       int(items[1]      )
							else:
														lowerCamelCase__					:	str         =       None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
																												lowerCamelCase__					:	List[Any]         =       value
																												logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.'''      )
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
																												lowerCamelCase__					:	Dict         =       value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
																												lowerCamelCase__					:	Tuple         =       value
																												logger.info(f'''Adapter proj layer bias was initialized from {full_name}.'''      )
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
																												lowerCamelCase__					:	Union[str, Any]         =       value
																												logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''      )
							elif isinstance(UpperCamelCase				,			UpperCamelCase      ):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
																					lowerCamelCase__					:	Any         =       value
																					logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
																					lowerCamelCase__					:	str         =       value
																					logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
							else:
														unused_weights.append(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       emb.weight.shape
							lowerCamelCase__					:	int         =       nn.Linear(UpperCamelCase				,			UpperCamelCase				,			bias=UpperCamelCase      )
							lowerCamelCase__					:	str         =       emb.weight.data
							return lin_layer





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			)      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[Any]         =       WavaVecaConfig.from_pretrained(
							    UpperCamelCase				,			add_adapter=UpperCamelCase				,			adapter_stride=UpperCamelCase				,			adapter_kernel_size=UpperCamelCase				,			use_auth_token=UpperCamelCase				,			output_hidden_size=UpperCamelCase				,			)
							lowerCamelCase__					:	Dict         =       MBartConfig.from_pretrained(UpperCamelCase      )

							# load model
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	int         =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]				,			arg_overrides={
							        """config_yaml""": config_yaml_path,
							        """data""": """/""".join(dict_path.split("""/"""      )[:-1]      ),
							        """w2v_path""": checkpoint_path,
							        """load_pretrained_decoder_from""": None,
							    }				,			)
							lowerCamelCase__					:	Union[str, Any]         =       model[0].eval()

							# load feature extractor
							lowerCamelCase__					:	Any         =       WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase				,			use_auth_token=UpperCamelCase      )

							# set weights for wav2vec2 encoder
							lowerCamelCase__					:	int         =       WavaVecaModel(UpperCamelCase      )

							recursively_load_weights_wavaveca(model.encoder				,			UpperCamelCase      )

							# load decoder weights
							lowerCamelCase__					:	int         =       MBartForCausalLM(UpperCamelCase      )
							lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,			strict=UpperCamelCase      )
							logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''      )
							logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''      )

							lowerCamelCase__					:	List[str]         =       SpeechEncoderDecoderModel(encoder=UpperCamelCase				,			decoder=UpperCamelCase      )
							lowerCamelCase__					:	Any         =       False

							lowerCamelCase__					:	Any         =       MBartaaTokenizer(UpperCamelCase      )
							tokenizer.save_pretrained(UpperCamelCase      )

							lowerCamelCase__					:	List[str]         =       hf_wavavec.config.to_dict()
							lowerCamelCase__					:	Union[str, Any]         =       tokenizer.pad_token_id
							lowerCamelCase__					:	Dict         =       tokenizer.bos_token_id
							lowerCamelCase__					:	List[str]         =       tokenizer.eos_token_id
							lowerCamelCase__					:	Tuple         =       """mbart50"""
							lowerCamelCase__					:	int         =       """wav2vec2"""

							lowerCamelCase__					:	Any         =       tokenizer.eos_token_id
							lowerCamelCase__					:	List[Any]         =       250004
							lowerCamelCase__					:	Dict         =       tokenizer.eos_token_id

							lowerCamelCase__					:	Tuple         =       SpeechEncoderDecoderConfig.from_dict(UpperCamelCase      )

							hf_wavavec.save_pretrained(UpperCamelCase      )
							feature_extractor.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Optional[Any]         =argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
			parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
			parser.add_argument(
			    '''--encoder_config_path''',
			    default='''facebook/wav2vec2-xls-r-1b''',
			    type=str,
			    help='''Path to hf encoder wav2vec2 checkpoint config''',
			)
			parser.add_argument(
			    '''--decoder_config_path''',
			    default='''facebook/mbart-large-50-one-to-many-mmt''',
			    type=str,
			    help='''Path to hf decoder checkpoint config''',
			)
			parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
			parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
			parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
			parser.add_argument('''--encoder_output_dim''', default=1_024, type=int, help='''encoder output dim''')
			parser.add_argument('''--start_token_id''', default=250_004, type=int, help='''`decoder_start_token_id` of model config''')

			_A	:			Optional[Any]         =parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    args.config_yaml_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    add_adapter=args.add_adapter,
			    adapter_kernel_size=args.adapter_kernel_size,
			    adapter_stride=args.adapter_stride,
			    decoder_start_token_id=args.start_token_id,
			    encoder_output_dim=args.encoder_output_dim,
			)





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


lowerCAmelCase__      		= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__      		= {
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
lowerCAmelCase__      		= ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= MBartTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[int]:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       List[str]				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token

            super().__init__(
                vocab_file=__lowerCamelCase ,						tokenizer_file=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						src_lang=__lowerCamelCase ,						tgt_lang=__lowerCamelCase ,						additional_special_tokens=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Union[str, Any]				    = vocab_file
            _A      :       int				    = False if not self.vocab_file else True

            _A      :       Optional[int]				    = FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   _additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in _additional_special_tokens])

            self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
            _A      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "en_XX"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       int				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def  _lowerCamelCase (						self)			->				str:
            return self._src_lang


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[str]				    = [self.cls_token_id]

            if token_ids_a is None:
                   return len(cls + token_ids_a + sep) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Dict:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A      :       str				    = src_lang
            _A      :       Any				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Dict				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "en_XX" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro_RO" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Any				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            return self.set_src_lang_special_tokens(self.src_lang)


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       int				    = []
            _A      :       List[str]				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Union[str, Any]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       str				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       List[Any]				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Optional[int]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       List[Any]				    = []
            _A      :       str				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Optional[int]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       int				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       str				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                       "tokenizer.")

            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       int				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

            if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase):
                   copyfile(self.vocab_file ,						__lowerCamelCase)

            return (out_vocab_file,)
