

'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase :       Optional[int]			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      ["pixel_values"]
 def __init__(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							__SCREAMING_SNAKE_CASE					:       int = 32					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=PILImageResampling.BILINEAR					,							__SCREAMING_SNAKE_CASE					:       bool = True					,							**__SCREAMING_SNAKE_CASE					:       Tuple					,							)  -> None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      do_resize
      __SCREAMING_SNAKE_CASE     =      do_rescale
      __SCREAMING_SNAKE_CASE     =      size_divisor
      __SCREAMING_SNAKE_CASE     =      resample
      super().__init__(**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       np.ndarray					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Optional[ChannelDimension] = None					,							**__SCREAMING_SNAKE_CASE					:       Any			)  -> np.ndarray:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_image_size(__SCREAMING_SNAKE_CASE			)
      # Rounds the height and width down to the closest multiple of size_divisor
      __SCREAMING_SNAKE_CASE     =      height // size_divisor * size_divisor
      __SCREAMING_SNAKE_CASE     =      width // size_divisor * size_divisor
      __SCREAMING_SNAKE_CASE     =      resize(__SCREAMING_SNAKE_CASE					,							(new_h, new_w)					,							resample=__SCREAMING_SNAKE_CASE					,							data_format=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)
      return image
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       np.ndarray					,							__SCREAMING_SNAKE_CASE					:       float					,							__SCREAMING_SNAKE_CASE					:       Optional[ChannelDimension] = None					,							**__SCREAMING_SNAKE_CASE					:       Optional[Any]			)  -> np.ndarray:


      """simple docstring"""




      return rescale(image=__SCREAMING_SNAKE_CASE					,							scale=__SCREAMING_SNAKE_CASE					,							data_format=__SCREAMING_SNAKE_CASE					,							**__SCREAMING_SNAKE_CASE			)


 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]					,							__SCREAMING_SNAKE_CASE					:       Optional[bool] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[int] = None					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       Optional[bool] = None					,							__SCREAMING_SNAKE_CASE					:       Optional[Union[TensorType, str]] = None					,							__SCREAMING_SNAKE_CASE					:       ChannelDimension = ChannelDimension.FIRST					,							**__SCREAMING_SNAKE_CASE					:       List[Any]					,							)  -> BatchFeature:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE     =      do_rescale if do_rescale is not None else self.do_rescale
      __SCREAMING_SNAKE_CASE     =      size_divisor if size_divisor is not None else self.size_divisor
      __SCREAMING_SNAKE_CASE     =      resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
           raise ValueError("""size_divisor is required for resizing"""			)

      __SCREAMING_SNAKE_CASE     =      make_list_of_images(__SCREAMING_SNAKE_CASE			)

      if not valid_images(__SCREAMING_SNAKE_CASE			):
           raise ValueError("""Invalid image(s)"""			)

      # All transformations expect numpy arrays.
      __SCREAMING_SNAKE_CASE     =      [to_numpy_array(__SCREAMING_SNAKE_CASE			) for img in images]

      if do_resize:
           __SCREAMING_SNAKE_CASE     =      [self.resize(__SCREAMING_SNAKE_CASE					,							size_divisor=__SCREAMING_SNAKE_CASE					,							resample=__SCREAMING_SNAKE_CASE			) for image in images]

      if do_rescale:
           __SCREAMING_SNAKE_CASE     =      [self.rescale(__SCREAMING_SNAKE_CASE					,							scale=1 / 255			) for image in images]

      __SCREAMING_SNAKE_CASE     =      [to_channel_dimension_format(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			) for image in images]

      __SCREAMING_SNAKE_CASE     =      {"""pixel_values""": images}
      return BatchFeature(data=__SCREAMING_SNAKE_CASE					,							tensor_type=__SCREAMING_SNAKE_CASE			)









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase :       List[str]			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "maskformer-swin"

 lowerCAmelCase__  					=      {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }
 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple=224					,							__SCREAMING_SNAKE_CASE					:       str=4					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=3					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=96					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=[2, 2, 6, 2]					,							__SCREAMING_SNAKE_CASE					:       Any=[3, 6, 12, 24]					,							__SCREAMING_SNAKE_CASE					:       Dict=7					,							__SCREAMING_SNAKE_CASE					:       Dict=4.0					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							__SCREAMING_SNAKE_CASE					:       str=0.0					,							__SCREAMING_SNAKE_CASE					:       int=0.0					,							__SCREAMING_SNAKE_CASE					:       str=0.1					,							__SCREAMING_SNAKE_CASE					:       List[Any]="gelu"					,							__SCREAMING_SNAKE_CASE					:       str=False					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0.02					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=1E-5					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							**__SCREAMING_SNAKE_CASE					:       Tuple					,							)  -> Tuple:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      embed_dim
      __SCREAMING_SNAKE_CASE     =      depths
      __SCREAMING_SNAKE_CASE     =      len(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      num_heads
      __SCREAMING_SNAKE_CASE     =      window_size
      __SCREAMING_SNAKE_CASE     =      mlp_ratio
      __SCREAMING_SNAKE_CASE     =      qkv_bias
      __SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
      __SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      use_absolute_embeddings
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      __SCREAMING_SNAKE_CASE     =      int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE			) - 1)			)
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(__SCREAMING_SNAKE_CASE			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






