


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_			):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__							:     Optional[Any]         =						['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__							:     Dict         =						'''BlipImageProcessor'''
				SCREAMING_SNAKE_CASE__							:     List[str]         =						('''BertTokenizer''', '''BertTokenizerFast''')


				def __init__(			self     :Any     ,     lowerCAmelCase__     :Dict     ,     lowerCAmelCase__     :int      )						->     List[str]:
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						False
										super().__init__(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE      )
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						self.image_processor


				def __call__(			self     :int     ,     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__     :Tuple     ,     )						->     BatchEncoding:
										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		:	List[Any]						     =						self.tokenizer
																__SCREAMING_SNAKE_CASE		:	int						     =						self.tokenizer(
																    text=__SCREAMING_SNAKE_CASE     ,     add_special_tokens=__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     truncation=__SCREAMING_SNAKE_CASE     ,     max_length=__SCREAMING_SNAKE_CASE     ,     stride=__SCREAMING_SNAKE_CASE     ,     pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,     return_attention_mask=__SCREAMING_SNAKE_CASE     ,     return_overflowing_tokens=__SCREAMING_SNAKE_CASE     ,     return_special_tokens_mask=__SCREAMING_SNAKE_CASE     ,     return_offsets_mapping=__SCREAMING_SNAKE_CASE     ,     return_token_type_ids=__SCREAMING_SNAKE_CASE     ,     return_length=__SCREAMING_SNAKE_CASE     ,     verbose=__SCREAMING_SNAKE_CASE     ,     return_tensors=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE     ,     )
																return text_encoding

										# add pixel_values
										__SCREAMING_SNAKE_CASE		:	int						     =						self.image_processor(__SCREAMING_SNAKE_CASE     ,     return_tensors=__SCREAMING_SNAKE_CASE      )

										if text is not None:
																__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						self.tokenizer(
																    text=__SCREAMING_SNAKE_CASE     ,     add_special_tokens=__SCREAMING_SNAKE_CASE     ,     padding=__SCREAMING_SNAKE_CASE     ,     truncation=__SCREAMING_SNAKE_CASE     ,     max_length=__SCREAMING_SNAKE_CASE     ,     stride=__SCREAMING_SNAKE_CASE     ,     pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,     return_attention_mask=__SCREAMING_SNAKE_CASE     ,     return_overflowing_tokens=__SCREAMING_SNAKE_CASE     ,     return_special_tokens_mask=__SCREAMING_SNAKE_CASE     ,     return_offsets_mapping=__SCREAMING_SNAKE_CASE     ,     return_token_type_ids=__SCREAMING_SNAKE_CASE     ,     return_length=__SCREAMING_SNAKE_CASE     ,     verbose=__SCREAMING_SNAKE_CASE     ,     return_tensors=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE     ,     )
										else:
																__SCREAMING_SNAKE_CASE		:	List[str]						     =						None

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

										return encoding_image_processor


				def 						__magic_name__(			self     :List[str]     ,     *lowerCAmelCase__     :List[Any]     ,     **lowerCAmelCase__     :List[str]      )						->     Union[str, Any]:
										return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE      )


				def 						__magic_name__(			self     :Optional[Any]     ,     *lowerCAmelCase__     :Union[str, Any]     ,     **lowerCAmelCase__     :int      )						->     Tuple:
										return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE      )



				@property
				def 						__magic_name__(			self     :Dict      )						->     Optional[Any]:
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						self.tokenizer.model_input_names
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SEWForCTC',
		    'SEWForSequenceClassification',
		    'SEWModel',
		    'SEWPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




