
'''simple docstring'''







import warnings

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






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					['''image_processor''', '''tokenizer''']
					lowerCAmelCase_					       =					'''ViTImageProcessor'''
					lowerCAmelCase_					       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''    ,							__SCREAMING_SNAKE_CASE    ,							)
																	lowercase_     :					int													=	kwargs.pop('''feature_extractor'''		)

											lowercase_     :					Any													=	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__(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def __call__(       self    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											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:
																	lowercase_     :					Dict													=	self.tokenizer(__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)

											if visual_prompt is not None:
																	lowercase_     :					Any													=	self.image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)

											if images is not None:
																	lowercase_     :					int													=	self.image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)

											if visual_prompt is not None and images is not None:
																	lowercase_     :					Optional[Any]													=	{
																	    '''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:
																	lowercase_     :					Tuple													=	image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											elif visual_prompt is not None:
																	lowercase_     :					Optional[int]													=	{
																	    '''conditional_pixel_values''': prompt_features.pixel_values,
																	}
																	return encoding
											else:
																	return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE		)    ,							tensor_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def      _snake_case   (       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,							__SCREAMING_SNAKE_CASE    ,							)
											return self.image_processor_class






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,							__SCREAMING_SNAKE_CASE    ,							)
											return self.image_processor





'''simple docstring'''







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





						if version.parse(hfh.__version__							).release < version.parse('''0.11.0'''							).release:
												# old versions of hfh don't url-encode the file path
												lowercase_     :					int													=	quote(__SCREAMING_SNAKE_CASE							)
						return hfh.hf_hub_url(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''				,				revision=__SCREAMING_SNAKE_CASE							)



