





'''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  (      _A       ):
					snake_case_      =   ["image_processor", "tokenizer"]
					snake_case_      =   "BlipImageProcessor"
					snake_case_      =   ("BertTokenizer", "BertTokenizerFast")



					def __init__(   self		,      a_		,      a_	):
							lowerCAmelCase     :    Tuple						=   False
							super().__init__(a_		,      a_	)
							lowerCAmelCase     :    Tuple						=   self.image_processor



					def __call__(   self		,      a_ = None		,      a_ = None		,      a_ = True		,      a_ = False		,      a_ = None		,      a_ = None		,      a_ = 0		,      a_ = None		,      a_ = None		,      a_ = False		,      a_ = False		,      a_ = False		,      a_ = False		,      a_ = False		,      a_ = True		,      a_ = None		,      **a_		,      ):
							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:
									lowerCAmelCase     :    Union[str, Any]						=   self.tokenizer
									lowerCAmelCase     :    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_		,      )
									return text_encoding

							# add pixel_values
							lowerCAmelCase     :    Union[str, Any]						=   self.image_processor(a_		,      return_tensors=a_	)

							if text is not None:
									lowerCAmelCase     :    List[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:
									lowerCAmelCase     :    str						=   None

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

							return encoding_image_processor



					def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
							return self.tokenizer.batch_decode(*a_		,      **a_	)



					def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
							return self.tokenizer.decode(*a_		,      **a_	)





					@property
					def   _lowerCamelCase    (   self	):
							lowerCAmelCase     :    Tuple						=   self.tokenizer.model_input_names
							lowerCAmelCase     :    List[str]						=   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)










'''simple docstring'''






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP



