





'''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, is_vision_available, logging


if is_vision_available():
      import PIL


__lowerCamelCase    :			List[Any]		= logging.get_logger(__name__)







class 		UpperCAmelCase (					lowercase_):


  """simple docstring"""







  lowerCAmelCase_											=					["""pixel_values"""]


  def __init__(					self     :       Optional[Any]				,						UpperCamelCase__     :       Any = True				,						UpperCamelCase__     :       Union[str, Any] = None				,						UpperCamelCase__     :       Dict = None				,						UpperCamelCase__     :       Tuple = PILImageResampling.BILINEAR				,						UpperCamelCase__     :       List[Any] = True				,						UpperCamelCase__     :       str = 1 / 255				,						UpperCamelCase__     :       int = True				,						UpperCamelCase__     :       str = None				,						UpperCamelCase__     :       Optional[int] = None				,						**UpperCamelCase__     :       int				,						)  ->    None:
    super().__init__(**UpperCamelCase__	)
    _UpperCamelCase							      =size if size is not None else {'''shortest_edge''': 384}
    _UpperCamelCase							      =get_size_dict(UpperCamelCase__				,						default_to_square=UpperCamelCase__	)

    _UpperCamelCase							      =do_resize
    _UpperCamelCase							      =size
    # Default value set here for backwards compatibility where the value in config is None
    _UpperCamelCase							      =crop_pct if crop_pct is not None else 224 / 256
    _UpperCamelCase							      =resample
    _UpperCamelCase							      =do_rescale
    _UpperCamelCase							      =rescale_factor
    _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 	UpperCamelCase__   (					self     :       str				,						UpperCamelCase__     :       Optional[Any]				,						UpperCamelCase__     :       List[Any]				,						UpperCamelCase__     :       Tuple				,						UpperCamelCase__     :       Tuple = PILImageResampling.BICUBIC				,						UpperCamelCase__     :       Dict = None				,						**UpperCamelCase__     :       List[Any]				,						)  ->    np.ndarray:
    _UpperCamelCase							      =get_size_dict(UpperCamelCase__				,						default_to_square=UpperCamelCase__	)
    if "shortest_edge" not in size:
      raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''	)
    _UpperCamelCase							      =size['''shortest_edge''']

    if shortest_edge < 384:
      # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
      _UpperCamelCase							      =int(shortest_edge / crop_pct	)
      _UpperCamelCase							      =get_resize_output_image_size(UpperCamelCase__				,						size=UpperCamelCase__				,						default_to_square=UpperCamelCase__	)
      _UpperCamelCase							      =resize(image=UpperCamelCase__				,						size=UpperCamelCase__				,						resample=UpperCamelCase__				,						data_format=UpperCamelCase__				,						**UpperCamelCase__	)
      # then crop to (shortest_edge, shortest_edge)
      return center_crop(image=UpperCamelCase__				,						size=(shortest_edge, shortest_edge)				,						data_format=UpperCamelCase__				,						**UpperCamelCase__	)
    else:
      # warping (no cropping) when evaluated at 384 or larger
      return resize(
          UpperCamelCase__				,						size=(shortest_edge, shortest_edge)				,						resample=UpperCamelCase__				,						data_format=UpperCamelCase__				,						**UpperCamelCase__	)


  def 	UpperCamelCase__   (					self     :       Optional[int]				,						UpperCamelCase__     :       List[str]				,						UpperCamelCase__     :       Any				,						UpperCamelCase__     :       int = None				,						**UpperCamelCase__     :       Optional[int]				,						)  ->    Any:
    return rescale(UpperCamelCase__				,						scale=UpperCamelCase__				,						data_format=UpperCamelCase__				,						**UpperCamelCase__	)


  def 	UpperCamelCase__   (					self     :       Tuple				,						UpperCamelCase__     :       Any				,						UpperCamelCase__     :       List[str]				,						UpperCamelCase__     :       Union[str, Any]				,						UpperCamelCase__     :       Optional[int] = None				,						**UpperCamelCase__     :       Optional[Any]				,						)  ->    np.ndarray:
    return normalize(UpperCamelCase__				,						mean=UpperCamelCase__				,						std=UpperCamelCase__				,						data_format=UpperCamelCase__				,						**UpperCamelCase__	)




  def 	UpperCamelCase__   (					self     :       Dict				,						UpperCamelCase__     :       int				,						UpperCamelCase__     :       List[str] = None				,						UpperCamelCase__     :       Union[str, Any] = None				,						UpperCamelCase__     :       List[str] = None				,						UpperCamelCase__     :       List[Any] = None				,						UpperCamelCase__     :       str = None				,						UpperCamelCase__     :       Optional[Any] = None				,						UpperCamelCase__     :       Optional[int] = None				,						UpperCamelCase__     :       Optional[int] = None				,						UpperCamelCase__     :       Union[str, Any] = None				,						UpperCamelCase__     :       List[Any] = None				,						UpperCamelCase__     :       List[str] = ChannelDimension.FIRST				,						**UpperCamelCase__     :       List[Any]				,						)  ->    PIL.Image.Image:
    _UpperCamelCase							      =do_resize if do_resize is not None else self.do_resize
    _UpperCamelCase							      =crop_pct if crop_pct is not None else self.crop_pct
    _UpperCamelCase							      =resample if resample is not None else self.resample
    _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							      =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(UpperCamelCase__				,						default_to_square=UpperCamelCase__	)

    _UpperCamelCase							      =make_list_of_images(UpperCamelCase__	)

    if not valid_images(UpperCamelCase__	):
      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_resize and size["shortest_edge"] < 384 and crop_pct is None:
      raise ValueError('''crop_pct must be specified if size < 384.'''	)

    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							      =[to_numpy_array(UpperCamelCase__	) for image in images]

    if do_resize:
      _UpperCamelCase							      =[self.resize(image=UpperCamelCase__				,						size=UpperCamelCase__				,						crop_pct=UpperCamelCase__				,						resample=UpperCamelCase__	) for image in images]

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

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

    _UpperCamelCase							      =[to_channel_dimension_format(UpperCamelCase__				,						UpperCamelCase__	) for image in images]

    _UpperCamelCase							      ={'''pixel_values''': images}
    return BatchFeature(data=UpperCamelCase__				,						tensor_type=UpperCamelCase__	)









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


lowercase_   							=  logging.get_logger(__name__)

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





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """timesformer"""




  def __init__(							self				,  lowerCAmelCase=224				,  lowerCAmelCase=16				,  lowerCAmelCase=3				,  lowerCAmelCase=8				,  lowerCAmelCase=768				,  lowerCAmelCase=12				,  lowerCAmelCase=12				,  lowerCAmelCase=3_072				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.02				,  lowerCAmelCase=1e-6				,  lowerCAmelCase=True				,  lowerCAmelCase="divided_space_time"				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Any:
        '''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


