





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
       import PIL

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




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

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

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

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






class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      ['pixel_values']





      def __init__(       self      ,						lowercase = True      ,						lowercase = None      ,						lowercase = PILImageResampling.BILINEAR      ,						lowercase = True      ,						lowercase = None      ,						lowercase = True      ,						lowercase = 1 / 255      ,						lowercase = True      ,						lowercase = True      ,						lowercase = None      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            super().__init__(**lowercase )
            lowerCAmelCase  						=       size if size is not None else {"""shortest_edge""": 256}
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            lowerCAmelCase  						=       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						param_name="""crop_size""" )

            lowerCAmelCase  						=       do_resize
            lowerCAmelCase  						=       size
            lowerCAmelCase  						=       do_center_crop
            lowerCAmelCase  						=       crop_size
            lowerCAmelCase  						=       resample
            lowerCAmelCase  						=       do_rescale
            lowerCAmelCase  						=       rescale_factor
            lowerCAmelCase  						=       offset
            lowerCAmelCase  						=       do_normalize
            lowerCAmelCase  						=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            lowerCAmelCase  						=       image_std if image_std is not None else IMAGENET_STANDARD_STD





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = PILImageResampling.BILINEAR      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            if "shortest_edge" in size:
                  lowerCAmelCase  						=       get_resize_output_image_size(lowercase      ,						size["""shortest_edge"""]      ,						default_to_square=lowercase )
            elif "height" in size and "width" in size:
                  lowerCAmelCase  						=       (size["""height"""], size["""width"""])
            else:
                  raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
            return resize(lowercase      ,						size=lowercase      ,						resample=lowercase      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(lowercase )
            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(lowercase      ,						size=(size["""height"""], size["""width"""])      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = True      ,						lowercase = None      ,						**lowercase      ,						)		->     Tuple:
            lowerCAmelCase  						=       image.astype(np.floataa )
            if offset:
                  lowerCAmelCase  						=       image - (scale / 2)
            return rescale(lowercase      ,						scale=lowercase      ,						data_format=lowercase      ,						**lowercase )





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





      def     _snake_case			(       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 = 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.""" )

            if offset and not do_rescale:
                  raise ValueError("""For offset, do_rescale must also be set to True.""" )

            # All transformations expect numpy arrays.
            lowerCAmelCase  						=       to_numpy_array(lowercase )

            if do_resize:
                  lowerCAmelCase  						=       self.resize(image=lowercase      ,						size=lowercase      ,						resample=lowercase )

            if do_center_crop:
                  lowerCAmelCase  						=       self.center_crop(lowercase      ,						size=lowercase )

            if do_rescale:
                  lowerCAmelCase  						=       self.rescale(image=lowercase      ,						scale=lowercase      ,						offset=lowercase )

            if do_normalize:
                  lowerCAmelCase  						=       self.normalize(image=lowercase      ,						mean=lowercase      ,						std=lowercase )

            lowerCAmelCase  						=       to_channel_dimension_format(lowercase      ,						lowercase )
            return image





      def     _snake_case			(       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:
            lowerCAmelCase  						=       do_resize if do_resize is not None else self.do_resize
            lowerCAmelCase  						=       resample if resample is not None else self.resample
            lowerCAmelCase  						=       do_center_crop if do_center_crop is not None else self.do_center_crop
            lowerCAmelCase  						=       do_rescale if do_rescale is not None else self.do_rescale
            lowerCAmelCase  						=       rescale_factor if rescale_factor is not None else self.rescale_factor
            lowerCAmelCase  						=       offset if offset is not None else self.offset
            lowerCAmelCase  						=       do_normalize if do_normalize is not None else self.do_normalize
            lowerCAmelCase  						=       image_mean if image_mean is not None else self.image_mean
            lowerCAmelCase  						=       image_std if image_std is not None else self.image_std

            lowerCAmelCase  						=       size if size is not None else self.size
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            lowerCAmelCase  						=       crop_size if crop_size is not None else self.crop_size
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						param_name="""crop_size""" )

            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.""" )

            lowerCAmelCase  						=       make_batched(lowercase )

            lowerCAmelCase  						=       [
                [
                    self._preprocess_image(
                        image=lowercase      ,						do_resize=lowercase      ,						size=lowercase      ,						resample=lowercase      ,						do_center_crop=lowercase      ,						crop_size=lowercase      ,						do_rescale=lowercase      ,						rescale_factor=lowercase      ,						offset=lowercase      ,						do_normalize=lowercase      ,						image_mean=lowercase      ,						image_std=lowercase      ,						data_format=lowercase      ,						)
                    for img in video
                ]
                for video in videos
            ]

            lowerCAmelCase  						=       {"""pixel_values""": videos}
            return BatchFeature(data=lowercase      ,						tensor_type=lowercase )









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       )       )



