
'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       ["""image_processor""", """tokenizer"""]
							__lowercase      		=       """BlipImageProcessor"""
							__lowercase      		=       """AutoTokenizer"""


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(lowerCAmelCase_    ,   lowerCAmelCase_  )

								# add QFormer tokenizer
								_snake_case   						=		qformer_tokenizer


							def __call__(       self    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 0    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								if images is None and text is None:
									raise ValueError('You have to specify at least images or text.'  )

								_snake_case   						=		BatchFeature()

								if text is not None:
									_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_    ,   )
									encoding.update(lowerCAmelCase_  )
									_snake_case   						=		self.qformer_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_    ,   )
									_snake_case   						=		qformer_text_encoding.pop('input_ids'  )
									_snake_case   						=		qformer_text_encoding.pop('attention_mask'  )

								if images is not None:
									_snake_case   						=		self.image_processor(lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_  )
									encoding.update(lowerCAmelCase_  )

								return encoding


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






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


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





								"""simple docstring"""






								_snake_case   						=		self.tokenizer.model_input_names
								_snake_case   						=		self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								if os.path.isfile(lowerCAmelCase_  ):
									raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'  )
								os.makedirs(lowerCAmelCase_    ,   exist_ok=lowerCAmelCase_  )
								_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'qformer_tokenizer'  )
								self.qformer_tokenizer.save_pretrained(lowerCAmelCase_  )
								return super().save_pretrained(lowerCAmelCase_    ,   **lowerCAmelCase_  )




							@classmethod
							def  lowerCamelCase				(       cls    ,   lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   subfolder='qformer_tokenizer'  )
								_snake_case   						=		cls._get_arguments_from_pretrained(lowerCAmelCase_    ,   **lowerCAmelCase_  )
								args.append(lowerCAmelCase_  )
								return cls(*lowerCAmelCase_  )



'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

