





import copy
from typing import Dict, List, Optional

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


snake_case_           =      {
    '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
}

snake_case_           =      logging.get_logger(__name__)

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
						A_						:    List[Any]							=   'mask2former'
						A_						:    int							=   ['swin']
						A_						:    Optional[Any]							=   {'hidden_size': 'hidden_dim'}


						def __init__(self	:				Tuple    ,      a__	:				Optional[Dict] = None    ,      a__	:				int = 256    ,      a__	:				int = 256    ,      a__	:				int = 256    ,      a__	:				int = 1024    ,      a__	:				str = "relu"    ,      a__	:				int = 6    ,      a__	:				int = 10    ,      a__	:				int = 8    ,      a__	:				float = 0.0    ,      a__	:				int = 2048    ,      a__	:				bool = False    ,      a__	:				bool = False    ,      a__	:				int = 4    ,      a__	:				int = 255    ,      a__	:				int = 100    ,      a__	:				float = 0.1    ,      a__	:				float = 2.0    ,      a__	:				float = 5.0    ,      a__	:				float = 5.0    ,      a__	:				int = 1_2544    ,      a__	:				float = 3.0    ,      a__	:				float = 0.7_5    ,      a__	:				float = 0.0_2    ,      a__	:				float = 1.0    ,      a__	:				bool = True    ,      a__	:				List[int] = [4, 8, 16, 32]    ,      a__	:				bool = None    ,      **a__	:				Optional[int]    ,      ):





									"""simple docstring"""



									if backbone_config is None:
												logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''				)
												__snake_case								=       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=a__    ,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    ,      )

									if isinstance(a__    ,      a__				):
												__snake_case								=       backbone_config.pop('''model_type'''				)
												__snake_case								=       CONFIG_MAPPING[backbone_model_type]
												__snake_case								=       config_class.from_dict(a__				)

									# 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				)}"""				)

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

									super().__init__(**a__				)


						@classmethod
						def        a			(cls	:				Optional[int]    ,      a__	:				PretrainedConfig    ,      **a__	:				Optional[int]				):





									"""simple docstring"""



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


						def        a			(self	:				Any				):





									"""simple docstring"""



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








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



         return super().__call__(a__    ,      **a__				)


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

