'''simple docstring'''
import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase__  :      List[str]        =					{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

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







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''maskformer'''
  lowerCAmelCase__										=	{'''hidden_size''': '''mask_feature_size'''}
  lowerCAmelCase__										=	['''resnet''', '''swin''']
  lowerCAmelCase__										=	['''detr''']






  def __init__(      self			:       List[str]  ,							UpperCAmelCase__			:       int = 256  ,							UpperCAmelCase__			:       int = 256  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       Optional[Dict] = None  ,							UpperCAmelCase__			:       Optional[Dict] = None  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       float = 1.0  ,							UpperCAmelCase__			:       float = 1.0  ,							UpperCAmelCase__			:       float = 1.0  ,							UpperCAmelCase__			:       float = 20.0  ,							UpperCAmelCase__			:       Optional[bool] = None  ,							**UpperCAmelCase__			:       Optional[Any]  ,							)				->str:
        if backbone_config is None:
              # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
              UpperCAmelCase_            =  SwinConfig(
                  image_size=384  ,							in_channels=3  ,							patch_size=4  ,							embed_dim=128  ,							depths=[2, 2, 18, 2]  ,							num_heads=[4, 8, 16, 32]  ,							window_size=12  ,							drop_path_rate=0.3  ,							out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']  ,							)

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

        # 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 MaskFormer. """
                  f"""Supported model types: {",".join(self.backbones_supported  )}"""  )

        if decoder_config is None:
              # fall back to https://huggingface.co/facebook/detr-resnet-50
              UpperCAmelCase_            =  DetrConfig()
        else:
              # verify that the decoder is supported
              UpperCAmelCase_            =  (
                  decoder_config.pop('''model_type'''  ) if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ) else decoder_config.model_type
              )
              if decoder_type not in self.decoders_supported:
                    raise ValueError(
                        f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                        f""" {",".join(self.decoders_supported  )}"""  )
              if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ):
                    UpperCAmelCase_            =  CONFIG_MAPPING[decoder_type]
                    UpperCAmelCase_            =  config_class.from_dict(UpperCAmelCase__  )

        UpperCAmelCase_            =  backbone_config
        UpperCAmelCase_            =  decoder_config
        # main feature dimension for the model
        UpperCAmelCase_            =  fpn_feature_size
        UpperCAmelCase_            =  mask_feature_size
        # initializer
        UpperCAmelCase_            =  init_std
        UpperCAmelCase_            =  init_xavier_std
        # Hungarian matcher && loss
        UpperCAmelCase_            =  cross_entropy_weight
        UpperCAmelCase_            =  dice_weight
        UpperCAmelCase_            =  mask_weight
        UpperCAmelCase_            =  use_auxiliary_loss
        UpperCAmelCase_            =  no_object_weight
        UpperCAmelCase_            =  output_auxiliary_logits

        UpperCAmelCase_            =  self.decoder_config.encoder_attention_heads
        UpperCAmelCase_            =  self.decoder_config.num_hidden_layers
        super().__init__(**UpperCAmelCase__  )






  @classmethod
  def  lowerCAmelCase__ (      cls			:       Tuple  ,							UpperCAmelCase__			:       PretrainedConfig  ,							UpperCAmelCase__			:       PretrainedConfig  ,							**UpperCAmelCase__			:       List[Any]  )				->Dict:
        return cls(
            backbone_config=UpperCAmelCase__  ,							decoder_config=UpperCAmelCase__  ,							**UpperCAmelCase__  ,							)






  def  lowerCAmelCase__ (      self			:       Dict  )				->Dict[str, any]:
        UpperCAmelCase_            =  copy.deepcopy(self.__dict__  )
        UpperCAmelCase_            =  self.backbone_config.to_dict()
        UpperCAmelCase_            =  self.decoder_config.to_dict()
        UpperCAmelCase_            =  self.__class__.model_type
        return output






'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor







class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def __init__(      self			:       int  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[str, Any]=7  ,							UpperCAmelCase__			:       Optional[int]=30  ,							UpperCAmelCase__			:       Union[str, Any]=400  ,							UpperCAmelCase__			:       List[Any]=3  ,							)				->Dict:
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size if size is not None else {'''shortest_edge''': 288}
        UpperCAmelCase_            =  size_divisor
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  do_center_crop
        UpperCAmelCase_            =  image_mean
        UpperCAmelCase_            =  image_std
        UpperCAmelCase_            =  do_pad
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  min_resolution
        UpperCAmelCase_            =  max_resolution






  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Dict=False  )				->Any:
        if not batched:
              UpperCAmelCase_            =  self.size['''shortest_edge''']
              UpperCAmelCase_            =  image_inputs[0]
              if isinstance(UpperCAmelCase__  ,							Image.Image  ):
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.size
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.shape[1], image.shape[2]
              UpperCAmelCase_            =  size / min(UpperCAmelCase__  ,							UpperCAmelCase__  )
              if h < w:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  size, scale * w
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  scale * h, size

              UpperCAmelCase_            =  int((1333 / 800) * size  )
              if max(UpperCAmelCase__  ,							UpperCAmelCase__  ) > max_size:
                    UpperCAmelCase_            =  max_size / max(UpperCAmelCase__  ,							UpperCAmelCase__  )
                    UpperCAmelCase_            =  newh * scale
                    UpperCAmelCase_            =  neww * scale

              UpperCAmelCase_      ,		UpperCAmelCase_            =  int(newh + 0.5  ), int(neww + 0.5  )
              UpperCAmelCase_      ,		UpperCAmelCase_            =  (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

        else:
              UpperCAmelCase_            =  []
              for image in image_inputs:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  self.get_expected_values([image]  )
                    expected_values.append((expected_height, expected_width)  )
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[0]  )[0]
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[1]  )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	BridgeTowerImageProcessor if is_vision_available() else None






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->str:
        UpperCAmelCase_            =  BridgeTowerImageProcessingTester(self  )






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->Dict:
        return self.image_processor_tester.prepare_image_processor_dict()






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->int:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''image_mean'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''image_std'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_normalize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_resize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''size'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''size_divisor'''  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Union[str, Any]:
        pass






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Union[str, Any]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							Image.Image  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							numpify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							np.ndarray  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							torchify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							torch.Tensor  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)





