




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


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: str										=["""pixel_values"""]



				def __init__( self       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = PILImageResampling.BILINEAR       , UpperCAmelCase_ = True       , UpperCAmelCase_ = 1 / 2_55       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)
											lowerCAmelCase   =		size if size is not None else {'''shortest_edge''': 3_84}
											lowerCAmelCase   =		get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_						)

											lowerCAmelCase   =		do_resize
											lowerCAmelCase   =		size
											# Default value set here for backwards compatibility where the value in config is None
											lowerCAmelCase   =		crop_pct if crop_pct is not None else 2_24 / 2_56
											lowerCAmelCase   =		resample
											lowerCAmelCase   =		do_rescale
											lowerCAmelCase   =		rescale_factor
											lowerCAmelCase   =		do_normalize
											lowerCAmelCase   =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowerCAmelCase   =		image_std if image_std is not None else IMAGENET_STANDARD_STD



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = PILImageResampling.BICUBIC       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		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()}"""						)
											lowerCAmelCase   =		size['''shortest_edge''']

											if shortest_edge < 3_84:
																		# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																		lowerCAmelCase   =		int(shortest_edge / crop_pct						)
																		lowerCAmelCase   =		get_resize_output_image_size(UpperCAmelCase_       , size=UpperCAmelCase_       , default_to_square=UpperCAmelCase_						)
																		lowerCAmelCase   =		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       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											return rescale(UpperCAmelCase_       , scale=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											return normalize(UpperCAmelCase_       , mean=UpperCAmelCase_       , std=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = ChannelDimension.FIRST       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		do_resize if do_resize is not None else self.do_resize
											lowerCAmelCase   =		crop_pct if crop_pct is not None else self.crop_pct
											lowerCAmelCase   =		resample if resample is not None else self.resample
											lowerCAmelCase   =		do_rescale if do_rescale is not None else self.do_rescale
											lowerCAmelCase   =		rescale_factor if rescale_factor is not None else self.rescale_factor
											lowerCAmelCase   =		do_normalize if do_normalize is not None else self.do_normalize
											lowerCAmelCase   =		image_mean if image_mean is not None else self.image_mean
											lowerCAmelCase   =		image_std if image_std is not None else self.image_std

											lowerCAmelCase   =		size if size is not None else self.size
											lowerCAmelCase   =		get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_						)

											lowerCAmelCase   =		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"] < 3_84 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.
											lowerCAmelCase   =		[to_numpy_array(UpperCAmelCase_						) for image in images]

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

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

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

											lowerCAmelCase   =		[to_channel_dimension_format(UpperCAmelCase_       , UpperCAmelCase_						) for image in images]

											lowerCAmelCase   =		{'''pixel_values''': images}
											return BatchFeature(data=UpperCAmelCase_       , tensor_type=UpperCAmelCase_						)









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


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										="""maskformer-swin"""

				__a							: Optional[int]										={
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}



				def __init__( self       , UpperCAmelCase_=2_24       , UpperCAmelCase_=4       , UpperCAmelCase_=3       , UpperCAmelCase_=96       , UpperCAmelCase_=[2, 2, 6, 2]       , UpperCAmelCase_=[3, 6, 12, 24]       , UpperCAmelCase_=7       , UpperCAmelCase_=4.0       , UpperCAmelCase_=True       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.1       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=False       , UpperCAmelCase_=0.02       , UpperCAmelCase_=1E-5       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)

											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		patch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		embed_dim
											lowerCAmelCase   =		depths
											lowerCAmelCase   =		len(UpperCAmelCase_						)
											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		window_size
											lowerCAmelCase   =		mlp_ratio
											lowerCAmelCase   =		qkv_bias
											lowerCAmelCase   =		hidden_dropout_prob
											lowerCAmelCase   =		attention_probs_dropout_prob
											lowerCAmelCase   =		drop_path_rate
											lowerCAmelCase   =		hidden_act
											lowerCAmelCase   =		use_absolute_embeddings
											lowerCAmelCase   =		layer_norm_eps
											lowerCAmelCase   =		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   =		int(embed_dim * 2 ** (len(UpperCAmelCase_						) - 1)						)
											lowerCAmelCase   =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       , len(UpperCAmelCase_						) + 1						)]
											lowerCAmelCase   ,     lowerCAmelCase   =		get_aligned_output_features_output_indices(
											    out_features=UpperCAmelCase_       , out_indices=UpperCAmelCase_       , stage_names=self.stage_names						)



