





'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					Optional[int]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					="AutoTokenizer"






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							Union[str, Any]							,	__A       :							Any	):
        super().__init__(__A							,	__A	)

        # add QFormer tokenizer
        __UpperCamelCase       =  qformer_tokenizer






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

        __UpperCamelCase       =  BatchFeature()

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

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

        return encoding






 def        _lowerCamelCase					(       self       :							List[str]							,	*__A       :							Dict							,	**__A       :							Dict	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	*__A       :							Union[str, Any]							,	**__A       :							Optional[int]	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Union[str, Any]							,	**__A       :							Dict	):
        if os.path.isfile(__A	):
               raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''	)
        os.makedirs(__A							,	exist_ok=__A	)
        __UpperCamelCase       =  os.path.join(__A							,	'qformer_tokenizer'	)
        self.qformer_tokenizer.save_pretrained(__A	)
        return super().save_pretrained(__A							,	**__A	)






 @classmethod
 def        _lowerCamelCase					(       cls       :							List[Any]							,	__A       :							int							,	**__A       :							Dict	):
        __UpperCamelCase       =  AutoTokenizer.from_pretrained(__A							,	subfolder='qformer_tokenizer'	)
        __UpperCamelCase       =  cls._get_arguments_from_pretrained(__A							,	**__A	)
        args.append(__A	)
        return cls(*__A	)







'''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() and is_transformers_version('''>=''', '''4.25.0''')):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
else:
    from .modeling_text_unet import UNetFlatConditionModel
    from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
    from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
    from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
    from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
