


import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a					  =     {
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__UpperCAmelCase   ):
					__SCREAMING_SNAKE_CASE       :  Dict						=   "mask2former"
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   ["swin"]
					__SCREAMING_SNAKE_CASE       :  Optional[Any]						=   {"hidden_size": "hidden_dim"}

					def __init__(     self       :   Tuple							,					UpperCamelCase__       :   Optional[Dict] = None							,					UpperCamelCase__       :   int = 256							,					UpperCamelCase__       :   int = 256							,					UpperCamelCase__       :   int = 256							,					UpperCamelCase__       :   int = 1_024							,					UpperCamelCase__       :   str = "relu"							,					UpperCamelCase__       :   int = 6							,					UpperCamelCase__       :   int = 10							,					UpperCamelCase__       :   int = 8							,					UpperCamelCase__       :   float = 0.0							,					UpperCamelCase__       :   int = 2_048							,					UpperCamelCase__       :   bool = False							,					UpperCamelCase__       :   bool = False							,					UpperCamelCase__       :   int = 4							,					UpperCamelCase__       :   int = 255							,					UpperCamelCase__       :   int = 100							,					UpperCamelCase__       :   float = 0.1							,					UpperCamelCase__       :   float = 2.0							,					UpperCamelCase__       :   float = 5.0							,					UpperCamelCase__       :   float = 5.0							,					UpperCamelCase__       :   int = 12_544							,					UpperCamelCase__       :   float = 3.0							,					UpperCamelCase__       :   float = 0.75							,					UpperCamelCase__       :   float = 0.02							,					UpperCamelCase__       :   float = 1.0							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   List[int] = [4, 8, 16, 32]							,					UpperCamelCase__       :   bool = None							,					**UpperCamelCase__       :   Union[str, Any]							,					):






										'''simple docstring'''






										if backbone_config is None:
															logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""       )
															lowercase_  							=			CONFIG_MAPPING["""swin"""](
															    image_size=224							,					in_channels=3							,					patch_size=4							,					embed_dim=96							,					depths=[2, 2, 18, 2]							,					num_heads=[3, 6, 12, 24]							,					window_size=7							,					drop_path_rate=0.3							,					use_absolute_embeddings=_lowerCamelCase							,					out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							,					)

										if isinstance(_lowerCamelCase							,					_lowerCamelCase       ):
															lowercase_  							=			backbone_config.pop("""model_type"""       )
															lowercase_  							=			CONFIG_MAPPING[backbone_model_type]
															lowercase_  							=			config_class.from_dict(_lowerCamelCase       )

										# verify that the backbone is supported
										if backbone_config.model_type not in self.backbones_supported:
															logger.warning_once(
															    F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
															    F'''Supported model types: {",".join(self.backbones_supported       )}'''       )

										lowercase_  							=			backbone_config
										lowercase_  							=			feature_size
										lowercase_  							=			mask_feature_size
										lowercase_  							=			hidden_dim
										lowercase_  							=			encoder_feedforward_dim
										lowercase_  							=			activation_function
										lowercase_  							=			encoder_layers
										lowercase_  							=			decoder_layers
										lowercase_  							=			num_attention_heads
										lowercase_  							=			dropout
										lowercase_  							=			dim_feedforward
										lowercase_  							=			pre_norm
										lowercase_  							=			enforce_input_projection
										lowercase_  							=			common_stride
										lowercase_  							=			ignore_value
										lowercase_  							=			num_queries
										lowercase_  							=			no_object_weight
										lowercase_  							=			class_weight
										lowercase_  							=			mask_weight
										lowercase_  							=			dice_weight
										lowercase_  							=			train_num_points
										lowercase_  							=			oversample_ratio
										lowercase_  							=			importance_sample_ratio
										lowercase_  							=			init_std
										lowercase_  							=			init_xavier_std
										lowercase_  							=			use_auxiliary_loss
										lowercase_  							=			feature_strides
										lowercase_  							=			output_auxiliary_logits
										lowercase_  							=			decoder_layers

										super().__init__(**_lowerCamelCase       )

					@classmethod
					def   UpperCAmelCase__       (     cls       :   Dict							,					UpperCamelCase__       :   PretrainedConfig							,					**UpperCamelCase__       :   Tuple       ):






										'''simple docstring'''






										return cls(
										    backbone_config=_lowerCamelCase							,					**_lowerCamelCase							,					)







					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			copy.deepcopy(self.__dict__       )
										lowercase_  							=			self.backbone_config.to_dict()
										lowercase_  							=			self.__class__.model_type
										return output






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   ['pixel_values']

					def __init__(     self       :   List[Any]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   int = 8							,					**UpperCamelCase__       :   Optional[Any]							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )

										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_pad
										lowercase_  							=			pad_size

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )
										lowercase_  							=			(old_height // size + 1) * size - old_height
										lowercase_  							=			(old_width // size + 1) * size - old_width

										return pad(UpperCamelCase__							,					((0, pad_height), (0, pad_width))							,					mode="""symmetric"""							,					data_format=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   Union[str, ChannelDimension] = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										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_pad if do_pad is not None else self.do_pad
										lowercase_  							=			pad_size if pad_size is not None else self.pad_size

										lowercase_  							=			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_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

										# All transformations expect numpy arrays.
										lowercase_  							=			[to_numpy_array(UpperCamelCase__       ) for image in images]

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

										if do_pad:
															lowercase_  							=			[self.pad(UpperCamelCase__							,					size=UpperCamelCase__       ) for image in images]

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

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


