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_mbartaa import MBartaaTokenizer
else:
	UpperCAmelCase     						=			None


UpperCAmelCase     						=			logging.get_logger(__name__)

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

UpperCAmelCase     						=			{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase     						=			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
UpperCAmelCase     						=			['''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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']




class 				__lowerCAmelCase					(    a_  ):



	'''simple docstring'''
	_lowerCamelCase  :		Optional[int]			      =	VOCAB_FILES_NAMES
	_lowerCamelCase  :		int			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase  :		Dict			      =	PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase  :		Optional[Any]			      =	['input_ids', 'attention_mask']
	_lowerCamelCase  :		int			      =	MBartaaTokenizer

	_lowerCamelCase  :		List[int]			      =	[]
	_lowerCamelCase  :		List[int]			      =	[]

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

								_UpperCamelCase       :    List[Any]					  =					kwargs.get('additional_special_tokens'    ,     []					)
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    lowerCamelCase    ,     src_lang=lowerCamelCase    ,     tgt_lang=lowerCamelCase    ,     tokenizer_file=lowerCamelCase    ,     eos_token=lowerCamelCase    ,     sep_token=lowerCamelCase    ,     cls_token=lowerCamelCase    ,     unk_token=lowerCamelCase    ,     pad_token=lowerCamelCase    ,     mask_token=lowerCamelCase    ,     **lowerCamelCase    ,     )

								_UpperCamelCase       :    List[str]					  =					vocab_file
								_UpperCamelCase       :    List[str]					  =					False if not self.vocab_file else True

								_UpperCamelCase       :    Union[str, Any]					  =					{
								    lang_code: self.convert_tokens_to_ids(lowerCamelCase					) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								_UpperCamelCase       :    Any					  =					src_lang if src_lang is not None else 'en_XX'
								_UpperCamelCase       :    Union[str, Any]					  =					tgt_lang
								_UpperCamelCase       :    List[Any]					  =					self.lang_code_to_id[self._src_lang]
								self.set_src_lang_special_tokens(self._src_lang					)

	@property
	def    __snake_case   (     self    :						List[Any]					)	->							str:
								return self._src_lang

	@src_lang.setter
	def    __snake_case   (     self    :						Optional[Any]    ,     lowerCamelCase    :						str					)	->							None:
								_UpperCamelCase       :    Optional[Any]					  =					new_src_lang
								self.set_src_lang_special_tokens(self._src_lang					)

	def    __snake_case   (     self    :						List[str]    ,     lowerCamelCase    :						List[int]    ,     lowerCamelCase    :						Optional[List[int]] = 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    __snake_case   (     self    :						Optional[Any]    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						str = "en_XX"    ,     lowerCamelCase    :						Optional[List[str]] = None    ,     lowerCamelCase    :						str = "ro_RO"    ,     **lowerCamelCase    :						Dict    ,     )	->							BatchEncoding:
								_UpperCamelCase       :    int					  =					src_lang
								_UpperCamelCase       :    Any					  =					tgt_lang
								return super().prepare_seqaseq_batch(lowerCamelCase    ,     lowerCamelCase    ,     **lowerCamelCase					)

	def    __snake_case   (     self    :						Optional[int]					)	->							Any:
								return self.set_src_lang_special_tokens(self.src_lang					)

	def    __snake_case   (     self    :						Tuple					)	->							int:
								return self.set_tgt_lang_special_tokens(self.tgt_lang					)

	def    __snake_case   (     self    :						List[Any]    ,     lowerCamelCase    :						str					)	->							None:
								_UpperCamelCase       :    int					  =					self.convert_tokens_to_ids(lowerCamelCase					)
								_UpperCamelCase       :    Dict					  =					[self.cur_lang_code_id]
								_UpperCamelCase       :    Optional[Any]					  =					[self.eos_token_id]

								_UpperCamelCase       :    Tuple					  =					self.convert_ids_to_tokens(self.prefix_tokens					)
								_UpperCamelCase       :    Any					  =					self.convert_ids_to_tokens(self.suffix_tokens					)

								_UpperCamelCase       :    Optional[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    __snake_case   (     self    :						int    ,     lowerCamelCase    :						str					)	->							None:
								_UpperCamelCase       :    List[Any]					  =					self.convert_tokens_to_ids(lowerCamelCase					)
								_UpperCamelCase       :    Optional[int]					  =					[self.cur_lang_code_id]
								_UpperCamelCase       :    str					  =					[self.eos_token_id]

								_UpperCamelCase       :    Tuple					  =					self.convert_ids_to_tokens(self.prefix_tokens					)
								_UpperCamelCase       :    List[Any]					  =					self.convert_ids_to_tokens(self.suffix_tokens					)

								_UpperCamelCase       :    List[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    __snake_case   (     self    :						Optional[int]    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						Optional[str]    ,     lowerCamelCase    :						Optional[str]    ,     **lowerCamelCase    :						Optional[int]					)	->							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'					)
								_UpperCamelCase       :    Dict					  =					src_lang
								_UpperCamelCase       :    List[Any]					  =					self(lowerCamelCase    ,     add_special_tokens=lowerCamelCase    ,     return_tensors=lowerCamelCase    ,     **lowerCamelCase					)
								_UpperCamelCase       :    Dict					  =					self.convert_tokens_to_ids(lowerCamelCase					)
								_UpperCamelCase       :    List[Any]					  =					tgt_lang_id
								return inputs

	def    __snake_case   (     self    :						Union[str, Any]    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						Optional[str] = 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
								_UpperCamelCase       :    List[str]					  =					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,)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



