




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:
		UpperCAmelCase_						=None


UpperCAmelCase_						=logging.get_logger(__name__)


UpperCAmelCase_						="""▁"""

UpperCAmelCase_						={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_						={
    """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"""
    },
}

UpperCAmelCase_						={
    """google/pegasus-xsum""": 512,
}


class 							__UpperCamelCase			( lowercase_  ):





				'''simple docstring'''



				__a							: Optional[int]										=VOCAB_FILES_NAMES
				__a							: int										=PRETRAINED_VOCAB_FILES_MAP
				__a							: Optional[Any]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a							: List[str]										=PegasusTokenizer
				__a							: int										=['''input_ids''', '''attention_mask''']



				def __init__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_="<pad>"       , UpperCAmelCase_="</s>"       , UpperCAmelCase_="<unk>"       , UpperCAmelCase_="<mask_2>"       , UpperCAmelCase_="<mask_1>"       , UpperCAmelCase_=None       , UpperCAmelCase_=1_03       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		offset

											if additional_special_tokens is not None:
																		if not isinstance(UpperCamelCase__       , UpperCamelCase__						):
																									raise TypeError(
																									    F"""additional_special_tokens should be of type {type(UpperCamelCase__						)}, but is"""
																									    F""" {type(UpperCamelCase__						)}"""						)

																		lowerCAmelCase   =		(
																		    ([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(UpperCamelCase__						)       , self.offset - 1						)
																		]

																		if len(set(UpperCamelCase__						)						) != len(UpperCamelCase__						):
																									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}."""						)
																		lowerCAmelCase   =		additional_special_tokens_extended
											else:
																		lowerCAmelCase   =		[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__(
											    UpperCamelCase__       , tokenizer_file=UpperCamelCase__       , pad_token=UpperCamelCase__       , eos_token=UpperCamelCase__       , unk_token=UpperCamelCase__       , mask_token=UpperCamelCase__       , mask_token_sent=UpperCamelCase__       , offset=UpperCamelCase__       , additional_special_tokens=UpperCamelCase__       , **UpperCamelCase__       , )
											lowerCAmelCase   =		vocab_file
											lowerCAmelCase   =		False if not self.vocab_file else True



				def       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		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       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False						):







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



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=None						):







											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       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = 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(UpperCamelCase__						):
																		logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
																		return
											lowerCAmelCase   =		os.path.join(
											    UpperCamelCase__       , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

											return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_						={
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=["""EfficientFormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """EfficientFormerForImageClassification""",
		    """EfficientFormerForImageClassificationWithTeacher""",
		    """EfficientFormerModel""",
		    """EfficientFormerPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFEfficientFormerForImageClassification""",
		    """TFEfficientFormerForImageClassificationWithTeacher""",
		    """TFEfficientFormerModel""",
		    """TFEfficientFormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_efficientformer import EfficientFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				    EfficientFormerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				    TFEfficientFormerPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



