"""simple docstring"""






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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


__lowerCAmelCase					: Union[str, Any]        =logging.get_logger(__name__)





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[Any]													=			['pixel_values']

      def __init__(		self	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, __lowerCAmelCase = PILImageResampling.BICUBIC	, __lowerCAmelCase = True	, __lowerCAmelCase = 1 / 255	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = True	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)
            lowercase														=			size if size is not None else {"""height""": 384, """width""": 384}
            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)

            lowercase														=			do_resize
            lowercase														=			size
            lowercase														=			resample
            lowercase														=			do_rescale
            lowercase														=			rescale_factor
            lowercase														=			do_normalize
            lowercase														=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            lowercase														=			image_std if image_std is not None else OPENAI_CLIP_STD
            lowercase														=			do_convert_rgb

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = PILImageResampling.BICUBIC	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'		)
            lowercase														=			(size["""height"""], size["""width"""])
            return resize(__lowerCAmelCase	, size=__lowerCAmelCase	, resample=__lowerCAmelCase	, data_format=__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            return rescale(__lowerCAmelCase	, scale=__lowerCAmelCase	, data_format=__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



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







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = ChannelDimension.FIRST	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			do_resize if do_resize is not None else self.do_resize
            lowercase														=			resample if resample is not None else self.resample
            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														=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            lowercase														=			size if size is not None else self.size
            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)

            lowercase														=			make_list_of_images(__lowerCAmelCase		)

            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."""		)

            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_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."""		)

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                  lowercase														=			[convert_to_rgb(__lowerCAmelCase		) for image in images]

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

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

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

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

            lowercase														=			[to_channel_dimension_format(__lowerCAmelCase	, __lowerCAmelCase		) for image in images]

            lowercase														=			BatchFeature(data={"""pixel_values""": images}	, tensor_type=__lowerCAmelCase		)

            return encoded_outputs




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					: Any        =logging.get_logger(__name__)

__lowerCAmelCase					: Union[str, Any]        ={
    """facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[Any]													=			'timesformer'

      def __init__(		self	, __lowerCAmelCase=224	, __lowerCAmelCase=16	, __lowerCAmelCase=3	, __lowerCAmelCase=8	, __lowerCAmelCase=768	, __lowerCAmelCase=12	, __lowerCAmelCase=12	, __lowerCAmelCase=3072	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=1E-6	, __lowerCAmelCase=True	, __lowerCAmelCase="divided_space_time"	, __lowerCAmelCase=0	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)

            lowercase														=			image_size
            lowercase														=			patch_size
            lowercase														=			num_channels
            lowercase														=			num_frames

            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			initializer_range
            lowercase														=			layer_norm_eps
            lowercase														=			qkv_bias

            lowercase														=			attention_type
            lowercase														=			drop_path_rate



