'''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 (
    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


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					["""pixel_values"""]







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

    '''simple docstring'''







    super().__init__(**snake_case_					)
    __UpperCAmelCase:					int		= size if size is not None else {"""shortest_edge""": 256}
    __UpperCAmelCase:					Dict		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					str		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    __UpperCAmelCase:					Dict		= get_size_dict(snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= do_resize
    __UpperCAmelCase:					List[str]		= size
    __UpperCAmelCase:					int		= resample
    __UpperCAmelCase:					int		= do_center_crop
    __UpperCAmelCase:					Dict		= crop_size
    __UpperCAmelCase:					int		= do_rescale
    __UpperCAmelCase:					Optional[int]		= rescale_factor
    __UpperCAmelCase:					Tuple		= do_normalize
    __UpperCAmelCase:					str		= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __UpperCAmelCase:					Dict		= image_std if image_std is not None else IMAGENET_STANDARD_STD







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = PILImageResampling.BICUBIC   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)
    if "shortest_edge" not in size:
      raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
    __UpperCAmelCase:					List[Any]		= get_resize_output_image_size(snake_case_   ,    size=size["""shortest_edge"""]   ,    default_to_square=snake_case_					)
    return resize(snake_case_   ,    size=snake_case_   ,    resample=snake_case_   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= get_size_dict(snake_case_					)
    return center_crop(snake_case_   ,    size=(size["""height"""], size["""width"""])   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = ChannelDimension.FIRST   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= do_resize if do_resize is not None else self.do_resize
    __UpperCAmelCase:					Union[str, Any]		= size if size is not None else self.size
    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					str		= resample if resample is not None else self.resample
    __UpperCAmelCase:					Optional[Any]		= do_center_crop if do_center_crop is not None else self.do_center_crop
    __UpperCAmelCase:					List[str]		= crop_size if crop_size is not None else self.crop_size
    __UpperCAmelCase:					int		= get_size_dict(snake_case_					)
    __UpperCAmelCase:					str		= do_rescale if do_rescale is not None else self.do_rescale
    __UpperCAmelCase:					Optional[Any]		= rescale_factor if rescale_factor is not None else self.rescale_factor
    __UpperCAmelCase:					Tuple		= do_normalize if do_normalize is not None else self.do_normalize
    __UpperCAmelCase:					Optional[int]		= image_mean if image_mean is not None else self.image_mean
    __UpperCAmelCase:					Dict		= image_std if image_std is not None else self.image_std

    __UpperCAmelCase:					List[Any]		= make_list_of_images(snake_case_					)

    if not valid_images(snake_case_					):
      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.
    __UpperCAmelCase:					Any		= [to_numpy_array(snake_case_					) for image in images]

    if do_resize:
      __UpperCAmelCase:					List[str]		= [self.resize(image=snake_case_   ,    size=snake_case_   ,    resample=snake_case_					) for image in images]

    if do_center_crop:
      __UpperCAmelCase:					Optional[Any]		= [self.center_crop(image=snake_case_   ,    size=snake_case_					) for image in images]

    if do_rescale:
      __UpperCAmelCase:					Tuple		= [self.rescale(image=snake_case_   ,    scale=snake_case_					) for image in images]

    if do_normalize:
      __UpperCAmelCase:					Union[str, Any]		= [self.normalize(image=snake_case_   ,    mean=snake_case_   ,    std=snake_case_					) for image in images]

    __UpperCAmelCase:					Optional[int]		= [to_channel_dimension_format(snake_case_   ,    snake_case_					) for image in images]

    __UpperCAmelCase:					List[str]		= {"""pixel_values""": images}
    return BatchFeature(data=snake_case_   ,    tensor_type=snake_case_					)
'''simple docstring'''






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


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""timesformer"""







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

    '''simple docstring'''







    super().__init__(**snake_case_					)

    __UpperCAmelCase:					Tuple		= image_size
    __UpperCAmelCase:					List[Any]		= patch_size
    __UpperCAmelCase:					Optional[Any]		= num_channels
    __UpperCAmelCase:					int		= num_frames

    __UpperCAmelCase:					List[str]		= hidden_size
    __UpperCAmelCase:					List[str]		= num_hidden_layers
    __UpperCAmelCase:					Dict		= num_attention_heads
    __UpperCAmelCase:					List[str]		= intermediate_size
    __UpperCAmelCase:					Union[str, Any]		= hidden_act
    __UpperCAmelCase:					Dict		= hidden_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Union[str, Any]		= initializer_range
    __UpperCAmelCase:					List[Any]		= layer_norm_eps
    __UpperCAmelCase:					int		= qkv_bias

    __UpperCAmelCase:					str		= attention_type
    __UpperCAmelCase:					Tuple		= drop_path_rate