

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


lowercase_   							=  logging.get_logger(__name__)





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    ["""pixel_values"""]




  def __init__(							self				,  lowerCAmelCase = True				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = PILImageResampling.BILINEAR				,  lowerCAmelCase = True				,  lowerCAmelCase = 1 / 255				,  lowerCAmelCase = True				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  **lowerCAmelCase				,  )    ->					None:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )
        _lowercase      			=size if size is not None else {'shortest_edge': 384}
        _lowercase      			=get_size_dict(lowerCAmelCase				,  default_to_square=lowerCAmelCase   )

        _lowercase      			=do_resize
        _lowercase      			=size
        # Default value set here for backwards compatibility where the value in config is None
        _lowercase      			=crop_pct if crop_pct is not None else 224 / 256
        _lowercase      			=resample
        _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				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = PILImageResampling.BICUBIC				,  lowerCAmelCase = None				,  **lowerCAmelCase				,  )    ->					np.ndarray:
        '''simple docstring'''






        _lowercase      			=get_size_dict(lowerCAmelCase				,  default_to_square=lowerCAmelCase   )
        if "shortest_edge" not in size:
              raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
        _lowercase      			=size['shortest_edge']

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




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = None				,  **lowerCAmelCase				,  )    ->					Any:
        '''simple docstring'''






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




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = None				,  **lowerCAmelCase				,  )    ->					np.ndarray:
        '''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				,  )    ->					PIL.Image.Image:
        '''simple docstring'''






        _lowercase      			=do_resize if do_resize is not None else self.do_resize
        _lowercase      			=crop_pct if crop_pct is not None else self.crop_pct
        _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      			=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_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.
        _lowercase      			=[to_numpy_array(lowerCAmelCase   ) for image in images]

        if do_resize:
              _lowercase      			=[self.resize(image=lowerCAmelCase				,  size=lowerCAmelCase				,  crop_pct=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      			={'pixel_values': images}
        return BatchFeature(data=lowerCAmelCase				,  tensor_type=lowerCAmelCase   )





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


