


'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__    	=     {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'AutoformerForPrediction',
						    'AutoformerModel',
						    'AutoformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_autoformer import (
						    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    AutoformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_autoformer import (
												    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    AutoformerForPrediction,
												    AutoformerModel,
												    AutoformerPreTrainedModel,
												)

else:
						import sys

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







'''simple docstring'''


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__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Any          =     ["image_processor", "tokenizer"]
			lowerCAmelCase :	List[Any]          =     "BlipImageProcessor"
			lowerCAmelCase :	Union[str, Any]          =     "AutoTokenizer"




			def __init__(		self  :     Any	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     int )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      False
					super().__init__(lowerCamelCase__	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      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__  :     Tuple	,  )   ->BatchEncoding:



					'''simple docstring'''







					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:
							_UpperCAmelCase		:       Optional[int]								=      self.tokenizer
							_UpperCAmelCase		:       List[Any]								=      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
					_UpperCAmelCase		:       Optional[int]								=      self.image_processor(lowerCamelCase__	,  return_tensors=lowerCamelCase__ )

					if text is not None:
							_UpperCAmelCase		:       Dict								=      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:
							_UpperCAmelCase		:       int								=      None

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

					return encoding_image_processor




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  *lowerCamelCase__  :     Optional[int]	,  **lowerCamelCase__  :     Dict )   ->Optional[Any]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     int	,  *lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







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






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 							lowerCAmelCase__    (		self  :     Any )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.tokenizer.model_input_names
					_UpperCAmelCase		:       Dict								=      self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



