




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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ["image_processor", "tokenizer"]
 lowercase_							       =       "BlipImageProcessor"
 lowercase_							       =       "AutoTokenizer"



 def __init__(				self    :						str							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     False
       super().__init__(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.image_processor



 def __call__(				self    :						Dict							,							_lowerCAmelCase    :						ImageInput = None							,							_lowerCAmelCase    :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Union[bool, str, PaddingStrategy] = False							,							_lowerCAmelCase    :						Union[bool, str, TruncationStrategy] = None							,							_lowerCAmelCase    :						Optional[int] = None							,							_lowerCAmelCase    :						int = 0							,							_lowerCAmelCase    :						Optional[int] = None							,							_lowerCAmelCase    :						Optional[bool] = None							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Optional[Union[str, TensorType]] = None							,							**_lowerCAmelCase    :						Any							,							):
       if images is None and text is None:
             raise ValueError('You have to specify either images or text.'		)

       # Get only text
       if images is None:
             SCREAMING_SNAKE_CASE_      		=     self.tokenizer
             SCREAMING_SNAKE_CASE_      		=     self.tokenizer(
                 text=_lowerCAmelCase							,							add_special_tokens=_lowerCAmelCase							,							padding=_lowerCAmelCase							,							truncation=_lowerCAmelCase							,							max_length=_lowerCAmelCase							,							stride=_lowerCAmelCase							,							pad_to_multiple_of=_lowerCAmelCase							,							return_attention_mask=_lowerCAmelCase							,							return_overflowing_tokens=_lowerCAmelCase							,							return_special_tokens_mask=_lowerCAmelCase							,							return_offsets_mapping=_lowerCAmelCase							,							return_token_type_ids=_lowerCAmelCase							,							return_length=_lowerCAmelCase							,							verbose=_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase							,							)
             return text_encoding

       # add pixel_values
       SCREAMING_SNAKE_CASE_      		=     self.image_processor(_lowerCAmelCase							,							return_tensors=_lowerCAmelCase		)

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

       if text_encoding is not None:
             encoding_image_processor.update(_lowerCAmelCase		)

       return encoding_image_processor



 def  lowerCAmelCase_    (				self    :						int							,							*_lowerCAmelCase    :						int							,							**_lowerCAmelCase    :						Optional[int]		):
       return self.tokenizer.batch_decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							*_lowerCAmelCase    :						List[Any]							,							**_lowerCAmelCase    :						Optional[Any]		):
       return self.tokenizer.decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)




 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  lowerCAmelCase_    (				self    :						int		):
       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		)		)





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__)