

'''simple docstring'''

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
							SCREAMING_SNAKE_CASE							:				Any     	=			{
							    "linear": PIL.Image.Resampling.BILINEAR,
							    "bilinear": PIL.Image.Resampling.BILINEAR,
							    "bicubic": PIL.Image.Resampling.BICUBIC,
							    "lanczos": PIL.Image.Resampling.LANCZOS,
							    "nearest": PIL.Image.Resampling.NEAREST,
							}
else:
							SCREAMING_SNAKE_CASE							:				Any     	=			{
							    "linear": PIL.Image.LINEAR,
							    "bilinear": PIL.Image.BILINEAR,
							    "bicubic": PIL.Image.BICUBIC,
							    "lanczos": PIL.Image.LANCZOS,
							    "nearest": PIL.Image.NEAREST,
							}




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[int]   )      -> Union[str, Any]:
							SCREAMING_SNAKE_CASE_				=				(images / 2 + 0.5).clamp(0		,1   )
							SCREAMING_SNAKE_CASE_				=				images.cpu().permute(0		,2		,3		,1   ).float().numpy()
							SCREAMING_SNAKE_CASE_				=				numpy_to_pil(lowerCAmelCase__   )
							return images




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict   )      -> Optional[int]:
							if images.ndim == 3:
														SCREAMING_SNAKE_CASE_				=				images[None, ...]
							SCREAMING_SNAKE_CASE_				=				(images * 255).round().astype('uint8'   )
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														SCREAMING_SNAKE_CASE_				=				[Image.fromarray(image.squeeze()		,mode='L'   ) for image in images]
							else:
														SCREAMING_SNAKE_CASE_				=				[Image.fromarray(lowerCAmelCase__   ) for image in images]

							return pil_images








'''simple docstring'''

import warnings

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






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

											SCREAMING_SNAKE_CASE_				=				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__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'       )

											if text is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if text is not None and images is not None:
																		SCREAMING_SNAKE_CASE_				=				image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_lowercase       ),     tensor_type=_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





