


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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xglm import XGLMTokenizer
else:
 lowerCamelCase		      = None


lowerCamelCase		      = logging.get_logger(__name__)

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

lowerCamelCase		      = {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

lowerCamelCase		      = {
    'facebook/xglm-564M': 20_48,
}




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[Any]   =VOCAB_FILES_NAMES
      UpperCamelCase__							:						Dict   =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:						str   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:						Optional[Any]   =['input_ids', 'attention_mask']
      UpperCamelCase__							:						Dict   =XGLMTokenizer






      def __init__(      self		:  Optional[Any]    ,     lowercase_		:  str=None    ,     lowercase_		:  Optional[Any]=None    ,     lowercase_		:  Union[str, Any]="<s>"    ,     lowercase_		:  Optional[Any]="</s>"    ,     lowercase_		:  Union[str, Any]="</s>"    ,     lowercase_		:  Optional[int]="<s>"    ,     lowercase_		:  Dict="<unk>"    ,     lowercase_		:  List[Any]="<pad>"    ,     **lowercase_		:  Union[str, Any]    ,     )      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=7
             _lowerCamelCase :	Optional[Any]						=[F'''<madeupword{i}>''' for i in range(self.num_madeup_words				)]

             _lowerCamelCase :	Dict						=kwargs.get('additional_special_tokens'    ,     []				)
             kwargs["additional_special_tokens"] += [
                 word for word in madeup_words if word not in kwargs["additional_special_tokens"]
             ]

             super().__init__(
                 lowercase_    ,     tokenizer_file=lowercase_    ,     bos_token=lowercase_    ,     eos_token=lowercase_    ,     sep_token=lowercase_    ,     cls_token=lowercase_    ,     unk_token=lowercase_    ,     pad_token=lowercase_    ,     **lowercase_    ,     )

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






      def  lowerCamelCase (      self		:  Union[str, Any]    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             if token_ids_a is None:
                    return [self.sep_token_id] + token_ids_a
             _lowerCamelCase :	List[str]						=[self.sep_token_id]
             return sep + token_ids_a + sep + sep + token_ids_a






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             _lowerCamelCase :	Dict						=[self.sep_token_id]

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






      def  lowerCamelCase (      self		:  List[str]    ,     lowercase_		:  str    ,     lowercase_		:  Optional[str] = None				)      ->		Tuple[str]:
             """simple docstring"""




             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(lowercase_				):
                    logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''				)
                    return
             _lowerCamelCase :	str						=os.path.join(
                 lowercase_    ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

             return (out_vocab_file,)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[Any]   ='AutoFeatureExtractor'
      UpperCamelCase__							:						List[str]   ='Speech2Text2Tokenizer'






      def __init__(      self		:  str    ,     lowercase_		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             super().__init__(lowercase_    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=self.feature_extractor
             _lowerCamelCase :	Tuple						=False






      def __call__(      self		:  Tuple    ,     *lowercase_		:  str    ,     **lowercase_		:  List[Any]				)      ->		List[str]:
             """simple docstring"""




             if self._in_target_context_manager:
                    return self.current_processor(*lowercase_    ,     **lowercase_				)

             if "raw_speech" in kwargs:
                    warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
                    _lowerCamelCase :	List[str]						=kwargs.pop('raw_speech'				)
             else:
                    _lowerCamelCase :	str						=kwargs.pop('audio'    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=kwargs.pop('sampling_rate'    ,     lowercase_				)
             _lowerCamelCase :	List[str]						=kwargs.pop('text'    ,     lowercase_				)
             if len(lowercase_				) > 0:
                    _lowerCamelCase :	Dict						=args[0]
                    _lowerCamelCase :	Optional[Any]						=args[1:]

             if audio is None and text is None:
                    raise ValueError('You need to specify either an `audio` or `text` input to process.'				)

             if audio is not None:
                    _lowerCamelCase :	List[Any]						=self.feature_extractor(lowercase_    ,     *lowercase_    ,     sampling_rate=lowercase_    ,     **lowercase_				)
             if text is not None:
                    _lowerCamelCase :	Tuple						=self.tokenizer(lowercase_    ,     **lowercase_				)

             if text is None:
                    return inputs
             elif audio is None:
                    return encodings
             else:
                    _lowerCamelCase :	Dict						=encodings['input_ids']
                    return inputs






      def  lowerCamelCase (      self		:  str    ,     *lowercase_		:  List[Any]    ,     **lowercase_		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




             return self.tokenizer.batch_decode(*lowercase_    ,     **lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]    ,     *lowercase_		:  Dict    ,     **lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             return self.tokenizer.decode(*lowercase_    ,     **lowercase_				)






      @contextmanager
      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """simple docstring"""




             warnings.warn(
                 '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
                 'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
                 'your audio inputs, or in a separate call.'				)
             _lowerCamelCase :	Optional[Any]						=True
             _lowerCamelCase :	List[Any]						=self.tokenizer
             yield
             _lowerCamelCase :	Optional[int]						=self.feature_extractor
             _lowerCamelCase :	List[str]						=False


