




import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =["image_processor", "tokenizer"]
						lowerCAmelCase__			      ="FlavaImageProcessor"
						lowerCAmelCase__			      =("BertTokenizer", "BertTokenizerFast")



						def __init__(			self	:   Union[str, Any]					,							snake_case__	:   Any=None					,							snake_case__	:   Optional[Any]=None					,							**snake_case__	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'					,							snake_case__					,							)
										SCREAMING_SNAKE_CASE_					      =							kwargs.pop('feature_extractor'  )

								SCREAMING_SNAKE_CASE_					      =							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__(snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.image_processor



						def __call__(			self	:   Tuple					,							snake_case__	:   Optional[ImageInput] = None					,							snake_case__	:   Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None					,							snake_case__	:   bool = True					,							snake_case__	:   Union[bool, str, PaddingStrategy] = False					,							snake_case__	:   Union[bool, str, TruncationStrategy] = False					,							snake_case__	:   Optional[int] = None					,							snake_case__	:   int = 0					,							snake_case__	:   Optional[int] = None					,							snake_case__	:   Optional[bool] = None					,							snake_case__	:   Optional[bool] = None					,							snake_case__	:   Optional[bool] = None					,							snake_case__	:   Optional[bool] = None					,							snake_case__	:   bool = False					,							snake_case__	:   bool = False					,							snake_case__	:   bool = False					,							snake_case__	:   bool = False					,							snake_case__	:   bool = True					,							snake_case__	:   Optional[Union[str, TensorType]] = None					,							**snake_case__	:   Union[str, Any]					,							):

								"""simple docstring"""

								if text is None and images is None:
										raise ValueError('You have to specify either text or images. Both cannot be none.'  )

								if text is not None:
										SCREAMING_SNAKE_CASE_					      =							self.tokenizer(
										    text=snake_case__					,							add_special_tokens=snake_case__					,							padding=snake_case__					,							truncation=snake_case__					,							max_length=snake_case__					,							stride=snake_case__					,							pad_to_multiple_of=snake_case__					,							return_token_type_ids=snake_case__					,							return_attention_mask=snake_case__					,							return_overflowing_tokens=snake_case__					,							return_special_tokens_mask=snake_case__					,							return_offsets_mapping=snake_case__					,							return_length=snake_case__					,							verbose=snake_case__					,							return_tensors=snake_case__					,							**snake_case__					,							)
								if images is not None:
										SCREAMING_SNAKE_CASE_					      =							self.image_processor(
										    snake_case__					,							return_image_mask=snake_case__					,							return_codebook_pixels=snake_case__					,							return_tensors=snake_case__					,							**snake_case__					,							)

								if text is not None and images is not None:
										encoding.update(snake_case__  )
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**snake_case__  )					,							tensor_type=snake_case__  )



						def 	__a					(			self	:   Dict					,							*snake_case__	:   str					,							**snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.tokenizer.batch_decode(*snake_case__					,							**snake_case__  )



						def 	__a					(			self	:   Union[str, Any]					,							*snake_case__	:   Dict					,							**snake_case__	:   int  ):

								"""simple docstring"""

								return self.tokenizer.decode(*snake_case__					,							**snake_case__  )



						@property
						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.tokenizer.model_input_names
								SCREAMING_SNAKE_CASE_					      =							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )



						@property
						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,							snake_case__					,							)
								return self.image_processor_class






						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,							snake_case__					,							)
								return self.image_processor





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE:     Any									= {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     List[Any]									= [
						    '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''PegasusXForConditionalGeneration''',
						    '''PegasusXModel''',
						    '''PegasusXPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus_x import (
												    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusXForConditionalGeneration,
												    PegasusXModel,
												    PegasusXPreTrainedModel,
												)


else:
						import sys

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