





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_pegasus import PegasusTokenizer
else:
					A       :   Tuple					= None


A       :   int					= logging.get_logger(__name__)


A       :   Tuple					= '''▁'''

A       :   Union[str, Any]					= {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

A       :   List[str]					= {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

A       :   Optional[int]					= {
    '''google/pegasus-xsum''': 5_1_2,
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Optional[Any]							     =       VOCAB_FILES_NAMES
			__lowerCamelCase      :			Any							     =       PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase      :			str							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase      :			Union[str, Any]							     =       PegasusTokenizer
			__lowerCamelCase      :			Optional[Any]							     =       ['''input_ids''', '''attention_mask''']






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       str=None				,      __lowerCAmelCase  :       Dict=None				,      __lowerCAmelCase  :       Any="<pad>"				,      __lowerCAmelCase  :       Tuple="</s>"				,      __lowerCAmelCase  :       Tuple="<unk>"				,      __lowerCAmelCase  :       Union[str, Any]="<mask_2>"				,      __lowerCAmelCase  :       Union[str, Any]="<mask_1>"				,      __lowerCAmelCase  :       Union[str, Any]=None				,      __lowerCAmelCase  :       Optional[int]=1_03				,      **__lowerCAmelCase  :       Dict				,      )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  offset

					if additional_special_tokens is not None:
							if not isinstance(__lowerCAmelCase				,      __lowerCAmelCase  ):
									raise TypeError(
									    f'additional_special_tokens should be of type {type(__lowerCAmelCase  )}, but is'
									    f' {type(__lowerCAmelCase  )}'  )

							A__     =  (
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    f'<unk_{i}>' for i in range(len(__lowerCAmelCase  )				,      self.offset - 1  )
							]

							if len(set(__lowerCAmelCase  )  ) != len(__lowerCAmelCase  ):
									raise ValueError(
									    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
									    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'  )
							A__     =  additional_special_tokens_extended
					else:
							A__     =  [mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [f'<unk_{i}>' for i in range(2				,      self.offset  )]

					super().__init__(
					    __lowerCAmelCase				,      tokenizer_file=__lowerCAmelCase				,      pad_token=__lowerCAmelCase				,      eos_token=__lowerCAmelCase				,      unk_token=__lowerCAmelCase				,      mask_token=__lowerCAmelCase				,      mask_token_sent=__lowerCAmelCase				,      offset=__lowerCAmelCase				,      additional_special_tokens=__lowerCAmelCase				,      **__lowerCAmelCase				,      )
					A__     =  vocab_file
					A__     =  False if not self.vocab_file else True






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       Any  )      ->      int:







					"""simple docstring"""






					A__     =  set(self.all_special_ids  )  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens  ) + 3  )  ):
							raise ValueError(
							    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
							    f' {len(self.additional_special_tokens  )} additional_special_tokens, but got {all_special_ids}'  )

					return [1 if x in all_special_ids else 0 for x in seq]






			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       List				,      __lowerCAmelCase  :       Optional[List] = None				,      __lowerCAmelCase  :       bool = False  )      ->      List[int]:







					"""simple docstring"""






					if already_has_special_tokens:
							return self._special_token_mask(__lowerCAmelCase  )
					elif token_ids_a is None:
							return self._special_token_mask(__lowerCAmelCase  ) + [1]
					else:
							return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]






			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       Optional[int]				,      __lowerCAmelCase  :       Tuple=None  )      ->      List[int]:







					"""simple docstring"""






					if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       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(__lowerCAmelCase  ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
							return
					A__     =  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_torch_available, is_vision_available


A       :   Optional[int]					= {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Optional[int]					= ['''Pix2StructImageProcessor''']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= [
					    '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Pix2StructPreTrainedModel''',
					    '''Pix2StructForConditionalGeneration''',
					    '''Pix2StructVisionModel''',
					    '''Pix2StructTextModel''',
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					A       :   Any					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



