
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:
							lowercase__						:Dict					    =       None


lowercase__						:Dict					    =       logging.get_logger(__name__)

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

lowercase__						: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",
    },
}

lowercase__						:Any					    =       {
    "facebook/xglm-564M": 2048,
}







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =VOCAB_FILES_NAMES
							lowercase_			:			Union[str, Any]			   =PRETRAINED_VOCAB_FILES_MAP
							lowercase_			:			Union[str, Any]			   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_			:			Optional[int]			   =['''input_ids''', '''attention_mask''']
							lowercase_			:			List[str]			   =XGLMTokenizer




							def __init__(							self  ,A__=None  ,A__=None  ,A__="<s>"  ,A__="</s>"  ,A__="</s>"  ,A__="<s>"  ,A__="<unk>"  ,A__="<pad>"  ,**A__  ,):
												# Compatibility with the original tokenizer
												lowercase		      =		7
												lowercase		      =		[f'<madeupword{i}>' for i in range(self.num_madeup_words)]

												lowercase		      =		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__  ,)

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




							def 					A__ (							self  ,A__  ,A__ = None):

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




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[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 					A__ (							self  ,A__  ,A__ = None):
												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
												lowercase		      =		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




