

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_								=     logging.get_logger(__name__)
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Optional[int]     		=["""pixel_values"""]




				def __init__(						self					:						int,					__lowercase					:						bool = True,					__lowercase					:						Optional[Dict[str, int]] = None,					__lowercase					:						PILImageResampling = PILImageResampling.BILINEAR,					__lowercase					:						bool = True,					__lowercase					:						Dict[str, int] = None,					__lowercase					:						bool = True,					__lowercase					:						Union[int, float] = 1 / 255,					__lowercase					:						bool = True,					__lowercase					:						Optional[Union[float, List[float]]] = None,					__lowercase					:						Optional[Union[float, List[float]]] = None,					**__lowercase					:						List[str],					):
							super().__init__(**__lowercase						)
							lowercase__					  =				size if size is not None else {"shortest_edge": 256}
							lowercase__					  =				get_size_dict(__lowercase,					default_to_square=__lowercase						)
							lowercase__					  =				crop_size if crop_size is not None else {"height": 224, "width": 224}
							lowercase__					  =				get_size_dict(__lowercase						)
							lowercase__					  =				do_resize
							lowercase__					  =				size
							lowercase__					  =				resample
							lowercase__					  =				do_center_crop
							lowercase__					  =				crop_size
							lowercase__					  =				do_rescale
							lowercase__					  =				rescale_factor
							lowercase__					  =				do_normalize
							lowercase__					  =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase__					  =				image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 							A__      (						self					:						Tuple,					__lowercase					:						np.ndarray,					__lowercase					:						Dict[str, int],					__lowercase					:						PILImageResampling = PILImageResampling.BICUBIC,					__lowercase					:						Optional[Union[str, ChannelDimension]] = None,					**__lowercase					:						Optional[int],					):
							lowercase__					  =				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()}'''						)
							lowercase__					  =				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					:						List[Any],					__lowercase					:						np.ndarray,					__lowercase					:						Dict[str, int],					__lowercase					:						Optional[Union[str, ChannelDimension]] = None,					**__lowercase					:						List[Any],					):
							lowercase__					  =				get_size_dict(__lowercase						)
							return center_crop(__lowercase,					size=(size["height"], size["width"]),					data_format=__lowercase,					**__lowercase						)




				def 							A__      (						self					:						Optional[int],					__lowercase					:						np.ndarray,					__lowercase					:						float,					__lowercase					:						Optional[Union[str, ChannelDimension]] = None,					**__lowercase					:						Dict						):
							return rescale(__lowercase,					scale=__lowercase,					data_format=__lowercase,					**__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						np.ndarray,					__lowercase					:						Union[float, List[float]],					__lowercase					:						Union[float, List[float]],					__lowercase					:						Optional[Union[str, ChannelDimension]] = None,					**__lowercase					:						Union[str, Any],					):
							return normalize(__lowercase,					mean=__lowercase,					std=__lowercase,					data_format=__lowercase,					**__lowercase						)




				def 							A__      (						self					:						Tuple,					__lowercase					:						ImageInput,					__lowercase					:						Optional[bool] = None,					__lowercase					:						Dict[str, int] = None,					__lowercase					:						PILImageResampling = None,					__lowercase					:						bool = None,					__lowercase					:						Dict[str, int] = None,					__lowercase					:						Optional[bool] = None,					__lowercase					:						Optional[float] = None,					__lowercase					:						Optional[bool] = None,					__lowercase					:						Optional[Union[float, List[float]]] = None,					__lowercase					:						Optional[Union[float, List[float]]] = None,					__lowercase					:						Optional[Union[str, TensorType]] = None,					__lowercase					:						Union[str, ChannelDimension] = ChannelDimension.FIRST,					**__lowercase					:						Any,					):
							lowercase__					  =				do_resize if do_resize is not None else self.do_resize
							lowercase__					  =				size if size is not None else self.size
							lowercase__					  =				get_size_dict(__lowercase,					default_to_square=__lowercase						)
							lowercase__					  =				resample if resample is not None else self.resample
							lowercase__					  =				do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase__					  =				crop_size if crop_size is not None else self.crop_size
							lowercase__					  =				get_size_dict(__lowercase						)
							lowercase__					  =				do_rescale if do_rescale is not None else self.do_rescale
							lowercase__					  =				rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase__					  =				do_normalize if do_normalize is not None else self.do_normalize
							lowercase__					  =				image_mean if image_mean is not None else self.image_mean
							lowercase__					  =				image_std if image_std is not None else self.image_std

							lowercase__					  =				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."						)

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

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

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

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

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

							lowercase__					  =				[to_channel_dimension_format(__lowercase,					__lowercase						) for image in images]

							lowercase__					  =				{"pixel_values": images}
							return BatchFeature(data=__lowercase,					tensor_type=__lowercase						)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




