

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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			str	)			->     List[List[ImageInput]]:

					"""simple docstring"""

					if isinstance(__a		,(list, tuple)	) and isinstance(videos[0]		,(list, tuple)	) and is_valid_image(videos[0][0]	):
										return videos

					elif isinstance(__a		,(list, tuple)	) and is_valid_image(videos[0]	):
										return [videos]

					elif is_valid_image(__a	):
										return [[videos]]

					raise ValueError(F"""Could not make batched video from {videos}"""	)







class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    ["pixel_values"]


							def __init__(			self       ,							_a = True       ,							_a = None       ,							_a = PILImageResampling.BILINEAR       ,							_a = True       ,							_a = None       ,							_a = True       ,							_a = 1 / 2_5_5       ,							_a = True       ,							_a = None       ,							_a = None       ,							**_a       ,							)    ->    None:
												super().__init__(**_a       )
												_a	:    Optional[int]         =		size if size is not None else {'''shortest_edge''': 2_2_4}
												_a	:    int         =		get_size_dict(_a       ,							default_to_square=_a       )
												_a	:    Union[str, Any]         =		crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
												_a	:    List[str]         =		get_size_dict(_a       ,							param_name='''crop_size'''       )

												_a	:    List[str]         =		do_resize
												_a	:    Dict         =		size
												_a	:    Tuple         =		do_center_crop
												_a	:    Any         =		crop_size
												_a	:    List[str]         =		resample
												_a	:    Union[str, Any]         =		do_rescale
												_a	:    Tuple         =		rescale_factor
												_a	:    List[Any]         =		do_normalize
												_a	:    Dict         =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_a	:    Union[str, Any]         =		image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = PILImageResampling.BILINEAR       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												_a	:    int         =		get_size_dict(_a       ,							default_to_square=_a       )
												if "shortest_edge" in size:
																	_a	:    List[str]         =		get_resize_output_image_size(_a       ,							size['''shortest_edge''']       ,							default_to_square=_a       )
												elif "height" in size and "width" in size:
																	_a	:    Optional[int]         =		(size['''height'''], size['''width'''])
												else:
																	raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""       )
												return resize(_a       ,							size=_a       ,							resample=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												_a	:    Tuple         =		get_size_dict(_a       )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""       )
												return center_crop(_a       ,							size=(size['''height'''], size['''width'''])       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    Union[str, Any]:
												return rescale(_a       ,							scale=_a       ,							data_format=_a       ,							**_a       )


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


							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = ChannelDimension.FIRST       ,							)    ->    np.ndarray:
												if do_resize and size is None or resample is None:
																	raise ValueError('''Size and resample 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.'''       )

												# All transformations expect numpy arrays.
												_a	:    Any         =		to_numpy_array(_a       )

												if do_resize:
																	_a	:    int         =		self.resize(image=_a       ,							size=_a       ,							resample=_a       )

												if do_center_crop:
																	_a	:    Optional[int]         =		self.center_crop(_a       ,							size=_a       )

												if do_rescale:
																	_a	:    Tuple         =		self.rescale(image=_a       ,							scale=_a       )

												if do_normalize:
																	_a	:    List[str]         =		self.normalize(image=_a       ,							mean=_a       ,							std=_a       )

												_a	:    Optional[Any]         =		to_channel_dimension_format(_a       ,							_a       )
												return image




							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = ChannelDimension.FIRST       ,							**_a       ,							)    ->    PIL.Image.Image:
												_a	:    List[str]         =		do_resize if do_resize is not None else self.do_resize
												_a	:    List[str]         =		resample if resample is not None else self.resample
												_a	:    str         =		do_center_crop if do_center_crop is not None else self.do_center_crop
												_a	:    Optional[int]         =		do_rescale if do_rescale is not None else self.do_rescale
												_a	:    List[Any]         =		rescale_factor if rescale_factor is not None else self.rescale_factor
												_a	:    Dict         =		do_normalize if do_normalize is not None else self.do_normalize
												_a	:    Any         =		image_mean if image_mean is not None else self.image_mean
												_a	:    List[str]         =		image_std if image_std is not None else self.image_std

												_a	:    Tuple         =		size if size is not None else self.size
												_a	:    Dict         =		get_size_dict(_a       ,							default_to_square=_a       )
												_a	:    int         =		crop_size if crop_size is not None else self.crop_size
												_a	:    List[str]         =		get_size_dict(_a       ,							param_name='''crop_size'''       )

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

												_a	:    Any         =		make_batched(_a       )

												_a	:    Optional[int]         =		[
												    [
												        self._preprocess_image(
												            image=_a       ,							do_resize=_a       ,							size=_a       ,							resample=_a       ,							do_center_crop=_a       ,							crop_size=_a       ,							do_rescale=_a       ,							rescale_factor=_a       ,							do_normalize=_a       ,							image_mean=_a       ,							image_std=_a       ,							data_format=_a       ,							)
												        for img in video
												    ]
												    for video in videos
												]

												_a	:    List[Any]         =		{'''pixel_values''': videos}
												return BatchFeature(data=_a       ,							tensor_type=_a       )






import warnings

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Optional[int]	    =    "CLIPImageProcessor"
							UpperCAmelCase__       :						Tuple	    =    ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


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

												_a	:    int         =		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__(_a       ,							_a       )


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    List[Any]:

												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:
																	_a	:    str         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    List[Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[int]:
												return self.tokenizer.decode(*_a       ,							**_a       )




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



