
"""simple docstring"""

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:
      UpperCamelCase      :							Dict          =					None


UpperCamelCase      :							Dict          =					logging.get_logger(__name__)

UpperCamelCase      :							Optional[int]          =					{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase      :							Any          =					{
    "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",
    },
}

UpperCamelCase      :							Any          =					{
    "facebook/xglm-564M": 2_0_4_8,
}

class __lowerCAmelCase						( SCREAMING_SNAKE_CASE__     ):
     lowercase		  =		VOCAB_FILES_NAMES
     lowercase		  =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		  =		['''input_ids''', '''attention_mask''']
     lowercase		  =		XGLMTokenizer





     def __init__(      self      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase="<s>"      ,	__UpperCAmelCase="</s>"      ,	__UpperCAmelCase="</s>"      ,	__UpperCAmelCase="<s>"      ,	__UpperCAmelCase="<unk>"      ,	__UpperCAmelCase="<pad>"      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			7
      __UpperCamelCase							    =			[F'<madeupword{i}>' for i in range(self.num_madeup_words )]

      __UpperCamelCase							    =			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__(
          A__      ,	tokenizer_file=A__      ,	bos_token=A__      ,	eos_token=A__      ,	sep_token=A__      ,	cls_token=A__      ,	unk_token=A__      ,	pad_token=A__      ,	**A__      ,	)

      __UpperCamelCase							    =			vocab_file
      __UpperCamelCase							    =			False if not self.vocab_file else True





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
      __UpperCamelCase							    =			[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

      '''simple docstring'''




      __UpperCamelCase							    =			[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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = None ):

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

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

      return (out_vocab_file,)





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =['''image_processor''', '''tokenizer''']
							lowercase_			:			List[str]			   ='''AutoImageProcessor'''
							lowercase_			:			Optional[Any]			   ='''AutoTokenizer'''




							def __init__(							self  ,A__=None  ,A__=None  ,**A__):
												lowercase		      =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''  ,A__  ,)
																	lowercase		      =		kwargs.pop('''feature_extractor''')

												lowercase		      =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.''')
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.''')

												super().__init__(A__  ,A__)
												lowercase		      =		self.image_processor
												lowercase		      =		False




							def __call__(							self  ,*A__  ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*A__  ,**A__)

												lowercase		      =		kwargs.pop('''images'''  ,A__)
												lowercase		      =		kwargs.pop('''text'''  ,A__)
												if len(A__) > 0:
																	lowercase		      =		args[0]
																	lowercase		      =		args[1:]

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

												if images is not None:
																	lowercase		      =		self.image_processor(A__  ,*A__  ,**A__)
												if text is not None:
																	lowercase		      =		self.tokenizer(A__  ,**A__)

												if text is None:
																	return inputs
												elif images is None:
																	return encodings
												else:
																	lowercase		      =		encodings['''input_ids''']
																	return inputs




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.batch_decode(*A__  ,**A__)




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.decode(*A__  ,**A__)




							@contextmanager
							def 					A__ (							self):
												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 images inputs, or in a separate call.''')
												lowercase		      =		True
												lowercase		      =		self.tokenizer
												yield
												lowercase		      =		self.image_processor
												lowercase		      =		False




							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,A__  ,)
												return self.image_processor_class





							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,A__  ,)
												return self.image_processor




