


'''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

_lowercase      :      Tuple            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      List[str]					):




						'''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__(       lowerCAmelCase	):
				__magic_name__    :				Dict  		=	["pixel_values"]




				def __init__(		self      :			str				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Dict[str, int] = None				,   lowerCAmelCase      :			PILImageResampling = PILImageResampling.BILINEAR				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Dict[str, int] = None				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Union[int, float] = 1 / 255				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Optional[Union[float, List[float]]] = None				,   lowerCAmelCase      :			Optional[Union[float, List[float]]] = None				,   **lowerCAmelCase      :			Optional[int]				,   )->					None:

										"""simple docstring"""




										super().__init__(**lowerCAmelCase				)
										UpperCAmelCase					    =     size if size is not None else {'''shortest_edge''': 256}
										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   default_to_square=lowerCAmelCase				)
										UpperCAmelCase					    =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   param_name='''crop_size'''				)

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




				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			np.ndarray				,   lowerCAmelCase      :			Dict[str, int]				,   lowerCAmelCase      :			PILImageResampling = PILImageResampling.BILINEAR				,   lowerCAmelCase      :			Optional[Union[str, ChannelDimension]] = None				,   **lowerCAmelCase      :			Any				,   )->					np.ndarray:

										"""simple docstring"""




										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				,   default_to_square=lowerCAmelCase				)
										if "shortest_edge" in size:
																UpperCAmelCase					    =     get_resize_output_image_size(lowerCAmelCase				,   size['''shortest_edge''']				,   default_to_square=lowerCAmelCase				)
										elif "height" in size and "width" in size:
																UpperCAmelCase					    =     (size['''height'''], size['''width'''])
										else:
																raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
										return resize(lowerCAmelCase				,   size=lowerCAmelCase				,   resample=lowerCAmelCase				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			np.ndarray				,   lowerCAmelCase      :			Dict[str, int]				,   lowerCAmelCase      :			Optional[Union[str, ChannelDimension]] = None				,   **lowerCAmelCase      :			Any				,   )->					np.ndarray:

										"""simple docstring"""




										UpperCAmelCase					    =     get_size_dict(lowerCAmelCase				)
										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(lowerCAmelCase				,   size=(size['''height'''], size['''width'''])				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			np.ndarray				,   lowerCAmelCase      :			Union[int, float]				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Optional[Union[str, ChannelDimension]] = None				,   **lowerCAmelCase      :			int				,   )->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     image.astype(np.floataa				)
										if offset:
																UpperCAmelCase					    =     image - (scale / 2)
										return rescale(lowerCAmelCase				,   scale=lowerCAmelCase				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			int				,   lowerCAmelCase      :			np.ndarray				,   lowerCAmelCase      :			Union[float, List[float]]				,   lowerCAmelCase      :			Union[float, List[float]]				,   lowerCAmelCase      :			Optional[Union[str, ChannelDimension]] = None				,   **lowerCAmelCase      :			str				,   )->					np.ndarray:

										"""simple docstring"""




										return normalize(lowerCAmelCase				,   mean=lowerCAmelCase				,   std=lowerCAmelCase				,   data_format=lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			ImageInput				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			Dict[str, int] = None				,   lowerCAmelCase      :			PILImageResampling = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			Dict[str, int] = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			float = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			Optional[Union[float, List[float]]] = None				,   lowerCAmelCase      :			Optional[Union[float, List[float]]] = None				,   lowerCAmelCase      :			Optional[ChannelDimension] = ChannelDimension.FIRST				,   )->					np.ndarray:

										"""simple docstring"""




										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.
										UpperCAmelCase					    =     to_numpy_array(lowerCAmelCase				)

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

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

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

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

										UpperCAmelCase					    =     to_channel_dimension_format(lowerCAmelCase				,   lowerCAmelCase				)
										return image





				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			ImageInput				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			Dict[str, int] = None				,   lowerCAmelCase      :			PILImageResampling = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			Dict[str, int] = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			float = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			bool = None				,   lowerCAmelCase      :			Optional[Union[float, List[float]]] = None				,   lowerCAmelCase      :			Optional[Union[float, List[float]]] = None				,   lowerCAmelCase      :			Optional[Union[str, TensorType]] = None				,   lowerCAmelCase      :			ChannelDimension = ChannelDimension.FIRST				,   **lowerCAmelCase      :			Dict				,   )->					PIL.Image.Image:

										"""simple docstring"""




										UpperCAmelCase					    =     do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase					    =     resample if resample is not None else self.resample
										UpperCAmelCase					    =     do_center_crop if do_center_crop is not None else self.do_center_crop
										UpperCAmelCase					    =     do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase					    =     rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase					    =     offset if offset is not None else self.offset
										UpperCAmelCase					    =     do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase					    =     image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase					    =     image_std if image_std is not None else self.image_std

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

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

										UpperCAmelCase					    =     make_batched(lowerCAmelCase				)

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

										UpperCAmelCase					    =     {'''pixel_values''': videos}
										return BatchFeature(data=lowerCAmelCase				,   tensor_type=lowerCAmelCase				)









'''simple docstring'''



from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)





