"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


_a				:		int	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[str]		 =		["pixel_values"]






     def __init__(		self    ,					a__ = True    ,					a__ = None    ,					a__ = 0.9    ,					a__ = PILImageResampling.BICUBIC    ,					a__ = True    ,					a__ = None    ,					a__ = 1 / 255    ,					a__ = True    ,					a__ = True    ,					a__ = None    ,					a__ = None    ,					**a__    ,					):
       super().__init__(**a__  )
       _lowerCAmelCase						:	Any        =					size if size is not None else {"""shortest_edge""": 224}
       _lowerCAmelCase						:	Optional[int]        =					get_size_dict(a__    ,					default_to_square=a__  )
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase						:	Union[str, Any]        =					get_size_dict(a__    ,					param_name="""crop_size"""  )

       _lowerCAmelCase						:	int        =					do_resize
       _lowerCAmelCase						:	Tuple        =					size
       _lowerCAmelCase						:	Union[str, Any]        =					crop_pct
       _lowerCAmelCase						:	Optional[Any]        =					resample
       _lowerCAmelCase						:	str        =					do_center_crop
       _lowerCAmelCase						:	Optional[int]        =					crop_size
       _lowerCAmelCase						:	Any        =					do_rescale
       _lowerCAmelCase						:	Dict        =					rescale_factor
       _lowerCAmelCase						:	Optional[Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       _lowerCAmelCase						:	Union[str, Any]        =					image_std if image_std is not None else IMAGENET_DEFAULT_STD






     def    __A  (		self    ,					a__    ,					a__    ,					a__ = None    ,					a__ = PILImageResampling.BICUBIC    ,					a__ = None    ,					**a__    ,					):
       _lowerCAmelCase						:	Any        =					get_size_dict(a__    ,					default_to_square=a__  )
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
       if crop_pct is not None:
         if "shortest_edge" in size:
           _lowerCAmelCase						:	List[Any]        =					int(size["""shortest_edge"""] / crop_pct  )
         elif "height" in size and "width" in size:
           if size["height"] == size["width"]:
             _lowerCAmelCase						:	str        =					int(size["""height"""] / crop_pct  )
           else:
             _lowerCAmelCase						:	Dict        =					(int(size["""height"""] / crop_pct  ), int(size["""width"""] / crop_pct  ))
         else:
           raise ValueError("""Invalid size for resize: {}""".format(a__  )  )

         _lowerCAmelCase						:	str        =					get_resize_output_image_size(a__    ,					size=a__    ,					default_to_square=a__  )
       else:
         if "shortest_edge" in size:
           _lowerCAmelCase						:	Optional[int]        =					get_resize_output_image_size(a__    ,					size=size["""shortest_edge"""]    ,					default_to_square=a__  )
         elif "height" in size and "width" in size:
           _lowerCAmelCase						:	str        =					(size["""height"""], size["""width"""])
         else:
           raise ValueError("""Invalid size for resize: {}""".format(a__  )  )

       return resize(a__    ,					size=a__    ,					resample=a__    ,					data_format=a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__ = None    ,					**a__    ,					):
       _lowerCAmelCase						:	str        =					get_size_dict(a__  )
       if "height" not in size or "width" not in size:
         raise ValueError(F"size must contain 'height' and 'width' as keys. Got {size.keys()}"  )
       return center_crop(a__    ,					size=(size["""height"""], size["""width"""])    ,					data_format=a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__ = None    ,					**a__    ,					):
       return rescale(a__    ,					scale=a__    ,					data_format=a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__ = None    ,					**a__    ,					):
       return normalize(a__    ,					mean=a__    ,					std=a__    ,					data_format=a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = ChannelDimension.FIRST    ,					**a__    ,					):
       _lowerCAmelCase						:	int        =					do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase						:	Tuple        =					crop_pct if crop_pct is not None else self.crop_pct
       _lowerCAmelCase						:	str        =					resample if resample is not None else self.resample
       _lowerCAmelCase						:	Tuple        =					do_center_crop if do_center_crop is not None else self.do_center_crop
       _lowerCAmelCase						:	Tuple        =					do_rescale if do_rescale is not None else self.do_rescale
       _lowerCAmelCase						:	int        =					rescale_factor if rescale_factor is not None else self.rescale_factor
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize if do_normalize is not None else self.do_normalize
       _lowerCAmelCase						:	str        =					image_mean if image_mean is not None else self.image_mean
       _lowerCAmelCase						:	Tuple        =					image_std if image_std is not None else self.image_std

       _lowerCAmelCase						:	Optional[int]        =					size if size is not None else self.size
       _lowerCAmelCase						:	Union[str, Any]        =					get_size_dict(a__    ,					default_to_square=a__  )
       _lowerCAmelCase						:	List[Any]        =					crop_size if crop_size is not None else self.crop_size
       _lowerCAmelCase						:	Any        =					get_size_dict(a__    ,					param_name="""crop_size"""  )

       _lowerCAmelCase						:	List[str]        =					make_list_of_images(a__  )

       if not valid_images(a__  ):
         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_center_crop and crop_pct is None:
         raise ValueError("""Crop_pct 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.
       _lowerCAmelCase						:	List[str]        =					[to_numpy_array(a__  ) for image in images]

       if do_resize:
         _lowerCAmelCase						:	int        =					[self.resize(image=a__    ,					size=a__    ,					crop_pct=a__    ,					resample=a__  ) for image in images]

       if do_center_crop:
         _lowerCAmelCase						:	List[Any]        =					[self.center_crop(image=a__    ,					size=a__  ) for image in images]

       if do_rescale:
         _lowerCAmelCase						:	Union[str, Any]        =					[self.rescale(image=a__    ,					scale=a__  ) for image in images]

       if do_normalize:
         _lowerCAmelCase						:	List[Any]        =					[self.normalize(image=a__    ,					mean=a__    ,					std=a__  ) for image in images]

       _lowerCAmelCase						:	Union[str, Any]        =					[to_channel_dimension_format(a__    ,					a__  ) for image in images]

       _lowerCAmelCase						:	int        =					{"""pixel_values""": images}
       return BatchFeature(data=a__    ,					tensor_type=a__  )





"""simple docstring"""




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


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		"maskformer-swin"

     _UpperCamelCase							:      Union[str, Any]		 =		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self    ,					a__=224    ,					a__=4    ,					a__=3    ,					a__=96    ,					a__=[2, 2, 6, 2]    ,					a__=[3, 6, 12, 24]    ,					a__=7    ,					a__=4.0    ,					a__=True    ,					a__=0.0    ,					a__=0.0    ,					a__=0.1    ,					a__="gelu"    ,					a__=False    ,					a__=0.0_2    ,					a__=1e-5    ,					a__=None    ,					a__=None    ,					**a__    ,					):
       super().__init__(**a__  )

       _lowerCAmelCase						:	Dict        =					image_size
       _lowerCAmelCase						:	List[str]        =					patch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	int        =					embed_dim
       _lowerCAmelCase						:	Optional[Any]        =					depths
       _lowerCAmelCase						:	List[str]        =					len(a__  )
       _lowerCAmelCase						:	List[Any]        =					num_heads
       _lowerCAmelCase						:	Tuple        =					window_size
       _lowerCAmelCase						:	List[Any]        =					mlp_ratio
       _lowerCAmelCase						:	Optional[Any]        =					qkv_bias
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Union[str, Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					drop_path_rate
       _lowerCAmelCase						:	Optional[Any]        =					hidden_act
       _lowerCAmelCase						:	Tuple        =					use_absolute_embeddings
       _lowerCAmelCase						:	str        =					layer_norm_eps
       _lowerCAmelCase						:	Any        =					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
       _lowerCAmelCase						:	Union[str, Any]        =					int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:	int        =					["""stem"""] + [F"stage{idx}" for idx in range(1    ,					len(a__  ) + 1  )]
       _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					get_aligned_output_features_output_indices(
           out_features=a__    ,					out_indices=a__    ,					stage_names=self.stage_names  )




