






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__      :     int       =					{
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     Tuple       =					['MaskFormerFeatureExtractor']
							A__      :     List[Any]       =					['MaskFormerImageProcessor']


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     Dict       =					[
							    'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MaskFormerForInstanceSegmentation',
							    'MaskFormerModel',
							    'MaskFormerPreTrainedModel',
							]
							A__      :     Optional[Any]       =					[
							    'MaskFormerSwinBackbone',
							    'MaskFormerSwinModel',
							    'MaskFormerSwinPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
							from .configuration_maskformer_swin import MaskFormerSwinConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_maskformer import MaskFormerFeatureExtractor
														from .image_processing_maskformer import MaskFormerImageProcessor
							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskformer import (
														    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskFormerForInstanceSegmentation,
														    MaskFormerModel,
														    MaskFormerPreTrainedModel,
														)
														from .modeling_maskformer_swin import (
														    MaskFormerSwinBackbone,
														    MaskFormerSwinModel,
														    MaskFormerSwinPreTrainedModel,
														)


else:
							import sys

							A__      :     Union[str, Any]       =					_LazyModule(__name__, globals()['__file__'], _import_structure)












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__      :     Optional[Any]       =					None


A__      :     str       =					logging.get_logger(__name__)


A__      :     Optional[int]       =					'▁'

A__      :     List[str]       =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__      :     Dict       =					{
    '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__      :     int       =					{
    'google/pegasus-xsum': 5_12,
}
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			VOCAB_FILES_NAMES
				lowercase__											=			PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=			PegasusTokenizer
				lowercase__											=			["""input_ids""", """attention_mask"""]




				def __init__(	self		:       Dict,    lowerCamelCase		:       str=None,    lowerCamelCase		:       List[str]=None,    lowerCamelCase		:       int="<pad>",    lowerCamelCase		:       str="</s>",    lowerCamelCase		:       Optional[Any]="<unk>",    lowerCamelCase		:       Any="<mask_2>",    lowerCamelCase		:       Optional[Any]="<mask_1>",    lowerCamelCase		:       int=None,    lowerCamelCase		:       Dict=103,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								lowercase__  					=     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    )}"""    )

												lowercase__  					=     (
												    ([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}."""    )
												lowercase__  					=     additional_special_tokens_extended
								else:
												lowercase__  					=     [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,    )
								lowercase__  					=     vocab_file
								lowercase__  					=     False if not self.vocab_file else True




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       int    ):

								'''simple docstring'''
								lowercase__  					=     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 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List,    lowerCamelCase		:       Optional[List] = None,    lowerCamelCase		:       bool = False    ):

								'''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 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Optional[Any]=None    ):

								'''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 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[str] = 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(lowerCamelCase    ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
												return
								lowercase__  					=     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,)




