"""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 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   List[str]					=	["""image_processor""", """tokenizer"""]
				UpperCAmelCase       :   Optional[int]					=	"""BlipImageProcessor"""
				UpperCAmelCase       :   Optional[Any]					=	"""AutoTokenizer"""



				def __init__(self       :   Union[str, Any]			,						_A       :   Optional[int]			,						_A       :   Tuple)					->			int:
					__snake_case    :							str   	=				False
					super().__init__(_A			,						_A)
					__snake_case    :							List[Any]   	=				self.image_processor



				def __call__(self       :   List[str]			,						_A       :   ImageInput = None			,						_A       :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,						_A       :   bool = True			,						_A       :   Union[bool, str, PaddingStrategy] = False			,						_A       :   Union[bool, str, TruncationStrategy] = None			,						_A       :   Optional[int] = None			,						_A       :   int = 0			,						_A       :   Optional[int] = None			,						_A       :   Optional[bool] = None			,						_A       :   bool = False			,						_A       :   bool = False			,						_A       :   bool = False			,						_A       :   bool = False			,						_A       :   bool = False			,						_A       :   bool = True			,						_A       :   Optional[Union[str, TensorType]] = None			,						**_A       :   List[Any]			,						)					->			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:
						__snake_case    :							Any   	=				self.tokenizer
						__snake_case    :							Dict   	=				self.tokenizer(
						    text=_A			,						add_special_tokens=_A			,						padding=_A			,						truncation=_A			,						max_length=_A			,						stride=_A			,						pad_to_multiple_of=_A			,						return_attention_mask=_A			,						return_overflowing_tokens=_A			,						return_special_tokens_mask=_A			,						return_offsets_mapping=_A			,						return_token_type_ids=_A			,						return_length=_A			,						verbose=_A			,						return_tensors=_A			,						**_A			,						)
						return text_encoding

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

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

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

					return encoding_image_processor



				def       _lowercase  (self       :   int			,						*_A       :   Optional[int]			,						**_A       :   Union[str, Any])					->			List[str]:
					return self.tokenizer.batch_decode(*_A			,						**_A)



				def       _lowercase  (self       :   Tuple			,						*_A       :   Tuple			,						**_A       :   Dict)					->			List[Any]:
					return self.tokenizer.decode(*_A			,						**_A)






				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def       _lowercase  (self       :   int)					->			List[Any]:
					__snake_case    :							Any   	=				self.tokenizer.model_input_names
					__snake_case    :							Dict   	=				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_flax_available, is_torch_available


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





