




"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     ['''image_processor''', '''tokenizer''']
 UpperCAmelCase   :					Any				  =     '''ViTImageProcessor'''
 UpperCAmelCase   :					Optional[Any]				  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(			self	:	List[Any]   ,							_UpperCAmelCase	:	str=None   ,							_UpperCAmelCase	:	str=None   ,							**_UpperCAmelCase	:	Tuple		):
       _A        =       None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'   ,							_UpperCAmelCase   ,							)
             _A        =       kwargs.pop('feature_extractor'		)

       _A        =       image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'		)
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'		)

       super().__init__(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def __call__(			self	:	str   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	List[Any]=None   ,							_UpperCAmelCase	:	Dict=None   ,							_UpperCAmelCase	:	Dict=None   ,							**_UpperCAmelCase	:	List[Any]		):
       if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.'		)

       if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'		)

       if text is not None:
             _A        =       self.tokenizer(_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase		)

       if visual_prompt is not None:
             _A        =       self.image_processor(_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase		)

       if images is not None:
             _A        =       self.image_processor(_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase		)

       if visual_prompt is not None and images is not None:
             _A        =       {
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             _A        =       image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A        =       {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase		)   ,							tensor_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int   ,							*_UpperCAmelCase	:	str   ,							**_UpperCAmelCase	:	Dict		):
       return self.tokenizer.batch_decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							*_UpperCAmelCase	:	Union[str, Any]   ,							**_UpperCAmelCase	:	Tuple		):
       return self.tokenizer.decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

 @property
 def       lowerCAmelCase_							(			self	:	Tuple		):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,							_UpperCAmelCase   ,							)
       return self.image_processor_class



 @property
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,							_UpperCAmelCase   ,							)
       return self.image_processor







"""simple docstring"""

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


a         =      HUGGINGFACE_HUB_CACHE


a         =      '''config.json'''
a         =      '''diffusion_pytorch_model.bin'''
a         =      '''diffusion_flax_model.msgpack'''
a         =      '''model.onnx'''
a         =      '''diffusion_pytorch_model.safetensors'''
a         =      '''weights.pb'''
a         =      '''https://huggingface.co'''
a         =      default_cache_path
a         =      '''diffusers_modules'''
a         =      os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a         =      ['''fp16''', '''non-ema''']
a         =      '''.self_attn'''

