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						:   int         =			logging.get_logger(__name__)
def  snake_case						(__lowercase						)	->	List[List[ImageInput]]:

      '''simple docstring'''



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

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

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

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



class        lowercase_   (					__snake_case		):
       _lowerCamelCase						=		['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_				,    ):
             super().__init__(**lowercase_    )
             _snake_case						:					List[Any]    =   size if size is not None else {"shortest_edge": 256}
             _snake_case						:					str    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
             _snake_case						:					List[Any]    =   crop_size if crop_size is not None else {"height": 224, "width": 224}
             _snake_case						:					int    =   get_size_dict(lowercase_				,    param_name="crop_size"    )

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



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = PILImageResampling.BILINEAR				,    lowercase_ = None				,    **lowercase_				,    ):
             _snake_case						:					List[Any]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
             if "shortest_edge" in size:
                   _snake_case						:					Any    =   get_resize_output_image_size(lowercase_				,    size["shortest_edge"]				,    default_to_square=lowercase_    )
             elif "height" in size and "width" in size:
                   _snake_case						:					Optional[Any]    =   (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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
             _snake_case						:					Dict    =   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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = True				,    lowercase_ = None				,    **lowercase_				,    ):
             _snake_case						:					int    =   image.astype(np.floataa    )
             if offset:
                   _snake_case						:					str    =   image - (scale / 2)
             return rescale(lowercase_				,    scale=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
             return normalize(lowercase_				,    mean=lowercase_				,    std=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



       def 	UpperCamelCase	(						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				,    ):
             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.
             _snake_case						:					List[Any]    =   to_numpy_array(lowercase_    )

             if do_resize:
                   _snake_case						:					List[str]    =   self.resize(image=lowercase_				,    size=lowercase_				,    resample=lowercase_    )

             if do_center_crop:
                   _snake_case						:					Union[str, Any]    =   self.center_crop(lowercase_				,    size=lowercase_    )

             if do_rescale:
                   _snake_case						:					Union[str, Any]    =   self.rescale(image=lowercase_				,    scale=lowercase_				,    offset=lowercase_    )

             if do_normalize:
                   _snake_case						:					Union[str, Any]    =   self.normalize(image=lowercase_				,    mean=lowercase_				,    std=lowercase_    )

             _snake_case						:					Optional[int]    =   to_channel_dimension_format(lowercase_				,    lowercase_    )
             return image



       def 	UpperCamelCase	(						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_				,    ):
             _snake_case						:					List[Any]    =   do_resize if do_resize is not None else self.do_resize
             _snake_case						:					Union[str, Any]    =   resample if resample is not None else self.resample
             _snake_case						:					str    =   do_center_crop if do_center_crop is not None else self.do_center_crop
             _snake_case						:					Union[str, Any]    =   do_rescale if do_rescale is not None else self.do_rescale
             _snake_case						:					int    =   rescale_factor if rescale_factor is not None else self.rescale_factor
             _snake_case						:					List[str]    =   offset if offset is not None else self.offset
             _snake_case						:					Tuple    =   do_normalize if do_normalize is not None else self.do_normalize
             _snake_case						:					Union[str, Any]    =   image_mean if image_mean is not None else self.image_mean
             _snake_case						:					List[Any]    =   image_std if image_std is not None else self.image_std

             _snake_case						:					List[Any]    =   size if size is not None else self.size
             _snake_case						:					Union[str, Any]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
             _snake_case						:					str    =   crop_size if crop_size is not None else self.crop_size
             _snake_case						:					Dict    =   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."    )

             _snake_case						:					Optional[int]    =   make_batched(lowercase_    )

             _snake_case						:					Dict    =   [
                 [
                     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
             ]

             _snake_case						:					Optional[Any]    =   {"pixel_values": videos}
             return BatchFeature(data=lowercase_				,    tensor_type=lowercase_    )
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)