

'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE							:				Optional[int]     	=			logging.get_logger(__name__)


if is_vision_available():
							import PIL






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""pixel_values"""]



				def __init__(    self,     _lowercase = True,     _lowercase = None,     _lowercase = PILImageResampling.BICUBIC,     _lowercase = True,     _lowercase = None,     _lowercase = True,     _lowercase = 1 / 255,     _lowercase = True,     _lowercase = None,     _lowercase = None,     _lowercase = True,     **_lowercase,     )     ->							None:
											super().__init__(**_lowercase       )
											SCREAMING_SNAKE_CASE_				=				size if size is not None else {'shortest_edge': 224}
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     default_to_square=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     default_to_square=_lowercase,     param_name='crop_size'       )

											SCREAMING_SNAKE_CASE_				=				do_resize
											SCREAMING_SNAKE_CASE_				=				size
											SCREAMING_SNAKE_CASE_				=				resample
											SCREAMING_SNAKE_CASE_				=				do_center_crop
											SCREAMING_SNAKE_CASE_				=				crop_size
											SCREAMING_SNAKE_CASE_				=				do_rescale
											SCREAMING_SNAKE_CASE_				=				rescale_factor
											SCREAMING_SNAKE_CASE_				=				do_normalize
											SCREAMING_SNAKE_CASE_				=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											SCREAMING_SNAKE_CASE_				=				image_std if image_std is not None else OPENAI_CLIP_STD
											SCREAMING_SNAKE_CASE_				=				do_convert_rgb



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = PILImageResampling.BICUBIC,     _lowercase = None,     **_lowercase,     )     ->							np.ndarray:
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     default_to_square=_lowercase       )
											if "shortest_edge" not in size:
																		raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
											SCREAMING_SNAKE_CASE_				=				get_resize_output_image_size(_lowercase,     size=size['shortest_edge'],     default_to_square=_lowercase       )
											return resize(_lowercase,     size=_lowercase,     resample=_lowercase,     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = None,     **_lowercase,     )     ->							np.ndarray:
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase       )
											if "height" not in size or "width" not in size:
																		raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""       )
											return center_crop(_lowercase,     size=(size['height'], size['width']),     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = None,     **_lowercase,     )     ->							Any:
											return rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase = None,     **_lowercase,     )     ->							np.ndarray:
											return normalize(_lowercase,     mean=_lowercase,     std=_lowercase,     data_format=_lowercase,     **_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = ChannelDimension.FIRST,     **_lowercase,     )     ->							PIL.Image.Image:
											SCREAMING_SNAKE_CASE_				=				do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE_				=				size if size is not None else self.size
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     param_name='size',     default_to_square=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE_				=				do_center_crop if do_center_crop is not None else self.do_center_crop
											SCREAMING_SNAKE_CASE_				=				crop_size if crop_size is not None else self.crop_size
											SCREAMING_SNAKE_CASE_				=				get_size_dict(_lowercase,     param_name='crop_size',     default_to_square=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE_				=				rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE_				=				do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE_				=				image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE_				=				image_std if image_std is not None else self.image_std
											SCREAMING_SNAKE_CASE_				=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											SCREAMING_SNAKE_CASE_				=				make_list_of_images(_lowercase       )

											if not valid_images(_lowercase       ):
																		raise ValueError(
																		    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																		    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

											if do_resize and size is None:
																		raise ValueError('Size must be specified if do_resize is True.'       )

											if do_center_crop and crop_size is None:
																		raise ValueError('Crop size must be specified if do_center_crop is True.'       )

											if do_rescale and rescale_factor is None:
																		raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('Image mean and std must be specified if do_normalize is True.'       )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																		SCREAMING_SNAKE_CASE_				=				[convert_to_rgb(_lowercase       ) for image in images]

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE_				=				[to_numpy_array(_lowercase       ) for image in images]

											if do_resize:
																		SCREAMING_SNAKE_CASE_				=				[self.resize(image=_lowercase,     size=_lowercase,     resample=_lowercase       ) for image in images]

											if do_center_crop:
																		SCREAMING_SNAKE_CASE_				=				[self.center_crop(image=_lowercase,     size=_lowercase       ) for image in images]

											if do_rescale:
																		SCREAMING_SNAKE_CASE_				=				[self.rescale(image=_lowercase,     scale=_lowercase       ) for image in images]

											if do_normalize:
																		SCREAMING_SNAKE_CASE_				=				[self.normalize(image=_lowercase,     mean=_lowercase,     std=_lowercase       ) for image in images]

											SCREAMING_SNAKE_CASE_				=				[to_channel_dimension_format(_lowercase,     _lowercase       ) for image in images]

											SCREAMING_SNAKE_CASE_				=				{'pixel_values': images}
											return BatchFeature(data=_lowercase,     tensor_type=_lowercase       )








'''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       )       )





