

'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor





class 							_snake_case (       unittest.TestCase						):
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[Any]=7     ,SCREAMING_SNAKE_CASE__			:      Any=3     ,SCREAMING_SNAKE_CASE__			:      List[str]=30     ,SCREAMING_SNAKE_CASE__			:      Tuple=400     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=True     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      List[Any]=True     ,SCREAMING_SNAKE_CASE__			:      List[str]=[0.5, 0.5, 0.5]     ,SCREAMING_SNAKE_CASE__			:      List[Any]=[0.5, 0.5, 0.5]     ,SCREAMING_SNAKE_CASE__			:      Dict=True     ,SCREAMING_SNAKE_CASE__			:      str=1 / 255     ,SCREAMING_SNAKE_CASE__			:      Tuple=True     ,):
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
     SCREAMING_SNAKE_CASE:Tuple						      =					parent
     SCREAMING_SNAKE_CASE:Optional[int]						      =					batch_size
     SCREAMING_SNAKE_CASE:List[str]						      =					num_channels
     SCREAMING_SNAKE_CASE:Tuple						      =					min_resolution
     SCREAMING_SNAKE_CASE:str						      =					max_resolution
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize
     SCREAMING_SNAKE_CASE:Dict						      =					size
     SCREAMING_SNAKE_CASE:int						      =					do_normalize
     SCREAMING_SNAKE_CASE:Dict						      =					image_mean
     SCREAMING_SNAKE_CASE:int						      =					image_std
     SCREAMING_SNAKE_CASE:int						      =					do_rescale
     SCREAMING_SNAKE_CASE:List[str]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:List[Any]						      =					do_pad
   def     __UpperCamelCase						( self			:      Dict							):
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }







   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[str]=False							):
     if not batched:
       SCREAMING_SNAKE_CASE:List[Any]						      =					image_inputs[0]
       if isinstance(SCREAMING_SNAKE_CASE__     ,Image.Image							):
         SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					image.size
       else:
         SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					image.shape[1], image.shape[2]
       if w < h:
         SCREAMING_SNAKE_CASE:Tuple						      =					int(self.size["shortest_edge"] * h / w							)
         SCREAMING_SNAKE_CASE:Dict						      =					self.size["shortest_edge"]
       elif w > h:
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.size["shortest_edge"]
         SCREAMING_SNAKE_CASE:str						      =					int(self.size["shortest_edge"] * w / h							)
       else:
         SCREAMING_SNAKE_CASE:str						      =					self.size["shortest_edge"]
         SCREAMING_SNAKE_CASE:Tuple						      =					self.size["shortest_edge"]

     else:
       SCREAMING_SNAKE_CASE:Tuple						      =					[]
       for image in image_inputs:
         SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_expected_values([image]							)
         expected_values.append((expected_height, expected_width)							)
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					max(SCREAMING_SNAKE_CASE__     ,key=lambda SCREAMING_SNAKE_CASE__							: item[0]							)[0]
       SCREAMING_SNAKE_CASE:int						      =					max(SCREAMING_SNAKE_CASE__     ,key=lambda SCREAMING_SNAKE_CASE__							: item[1]							)[1]

     return expected_height, expected_width





@require_torch
@require_vision
class 							_snake_case (       _a	,  unittest.TestCase						):
   _A       :     Tuple       =					DeformableDetrImageProcessor if is_vision_available() else None
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:str						      =					DeformableDetrImageProcessingTester(self							)
   @property
   def     __UpperCamelCase						( self			:      Tuple							):
     return self.image_processor_tester.prepare_image_processor_dict()
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"image_mean"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"image_std"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"do_normalize"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"do_resize"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"do_rescale"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"do_pad"							)							)
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"size"							)							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					self.image_processing_class.from_dict(self.image_processor_dict							)
     self.assertEqual(image_processor.size     ,{"shortest_edge": 18, "longest_edge": 1_333}							)
     self.assertEqual(image_processor.do_pad     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Dict						      =					self.image_processing_class.from_dict(
         self.image_processor_dict     ,size=42     ,max_size=84     ,pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__							)
     self.assertEqual(image_processor.size     ,{"shortest_edge": 42, "longest_edge": 84}							)
     self.assertEqual(image_processor.do_pad     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple							):
     pass
   def     __UpperCamelCase						( self			:      List[Any]							):
     # Initialize image_processing
     SCREAMING_SNAKE_CASE:Tuple						      =					self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     SCREAMING_SNAKE_CASE:Any						      =					prepare_image_inputs(self.image_processor_tester     ,equal_resolution=SCREAMING_SNAKE_CASE__							)
     for image in image_inputs:
       self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,Image.Image							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_processing(image_inputs[0]     ,return_tensors="pt"							).pixel_values

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

     self.assertEqual(
         encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

     # Test batched
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     ,batched=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					image_processing(SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape     ,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )     ,)
   def     __UpperCamelCase						( self			:      Dict							):
     # Initialize image_processing
     SCREAMING_SNAKE_CASE:List[str]						      =					self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE:List[Any]						      =					prepare_image_inputs(self.image_processor_tester     ,equal_resolution=SCREAMING_SNAKE_CASE__     ,numpify=SCREAMING_SNAKE_CASE__							)
     for image in image_inputs:
       self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,np.ndarray							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_processing(image_inputs[0]     ,return_tensors="pt"							).pixel_values

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

     self.assertEqual(
         encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

     # Test batched
     SCREAMING_SNAKE_CASE:Tuple						      =					image_processing(SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).pixel_values

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     ,batched=SCREAMING_SNAKE_CASE__							)

     self.assertEqual(
         encoded_images.shape     ,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )     ,)
   def     __UpperCamelCase						( self			:      List[Any]							):
     # Initialize image_processing
     SCREAMING_SNAKE_CASE:Tuple						      =					self.image_processing_class(**self.image_processor_dict							)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					prepare_image_inputs(self.image_processor_tester     ,equal_resolution=SCREAMING_SNAKE_CASE__     ,torchify=SCREAMING_SNAKE_CASE__							)
     for image in image_inputs:
       self.assertIsInstance(SCREAMING_SNAKE_CASE__     ,torch.Tensor							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:Tuple						      =					image_processing(image_inputs[0]     ,return_tensors="pt"							).pixel_values

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

     self.assertEqual(
         encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

     # Test batched
     SCREAMING_SNAKE_CASE:Any						      =					image_processing(SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).pixel_values

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     ,batched=SCREAMING_SNAKE_CASE__							)

     self.assertEqual(
         encoded_images.shape     ,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )     ,)
   @slow
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     # prepare image and target
     SCREAMING_SNAKE_CASE:int						      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,"r"							) as f:
       SCREAMING_SNAKE_CASE:str						      =					json.loads(f.read()							)

     SCREAMING_SNAKE_CASE:Tuple						      =					{"image_id": 39_769, "annotations": target}

     # encode them
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					DeformableDetrImageProcessor()
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_processing(images=SCREAMING_SNAKE_CASE__     ,annotations=SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							)

     # verify pixel values
     SCREAMING_SNAKE_CASE:Any						      =					torch.Size([1, 3, 800, 1_066]							)
     self.assertEqual(encoding["pixel_values"].shape     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-4							)							)

     # verify area
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify boxes
     SCREAMING_SNAKE_CASE:Dict						      =					torch.Size([6, 4]							)
     self.assertEqual(encoding["labels"][0]["boxes"].shape     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-3							)							)
     # verify image_id
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor([39_769]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify is_crowd
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor([0, 0, 0, 0, 0, 0]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify class_labels
     SCREAMING_SNAKE_CASE:Dict						      =					torch.tensor([75, 75, 63, 65, 17, 17]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify orig_size
     SCREAMING_SNAKE_CASE:str						      =					torch.tensor([480, 640]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify size
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.tensor([800, 1_066]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,SCREAMING_SNAKE_CASE__							)							)







   @slow
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     # prepare image, target and masks_path
     SCREAMING_SNAKE_CASE:Tuple						      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,"r"							) as f:
       SCREAMING_SNAKE_CASE:str						      =					json.loads(f.read()							)

     SCREAMING_SNAKE_CASE:Tuple						      =					{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

     SCREAMING_SNAKE_CASE:Optional[int]						      =					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"							)

     # encode them
     SCREAMING_SNAKE_CASE:Dict						      =					DeformableDetrImageProcessor(format="coco_panoptic"							)
     SCREAMING_SNAKE_CASE:int						      =					image_processing(images=SCREAMING_SNAKE_CASE__     ,annotations=SCREAMING_SNAKE_CASE__     ,masks_path=SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							)

     # verify pixel values
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.Size([1, 3, 800, 1_066]							)
     self.assertEqual(encoding["pixel_values"].shape     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-4							)							)

     # verify area
     SCREAMING_SNAKE_CASE:Dict						      =					torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify boxes
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.Size([6, 4]							)
     self.assertEqual(encoding["labels"][0]["boxes"].shape     ,SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-3							)							)
     # verify image_id
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.tensor([39_769]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify is_crowd
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.tensor([0, 0, 0, 0, 0, 0]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify class_labels
     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.tensor([17, 17, 63, 75, 75, 93]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify masks
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					822_873
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,SCREAMING_SNAKE_CASE__							)
     # verify orig_size
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.tensor([480, 640]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,SCREAMING_SNAKE_CASE__							)							)
     # verify size
     SCREAMING_SNAKE_CASE:Tuple						      =					torch.tensor([800, 1_066]							)
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,SCREAMING_SNAKE_CASE__							)							)









'''simple docstring'''

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 							_snake_case (       _a						):
   _A       :     List[Any]       =					'''mask2former'''
   _A       :     str       =					['''swin''']
   _A       :     Tuple       =					{'''hidden_size''': '''hidden_dim'''}
   def __init__( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Dict] = None     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 256     ,SCREAMING_SNAKE_CASE__			:      int = 1_024     ,SCREAMING_SNAKE_CASE__			:      str = "relu"     ,SCREAMING_SNAKE_CASE__			:      int = 6     ,SCREAMING_SNAKE_CASE__			:      int = 10     ,SCREAMING_SNAKE_CASE__			:      int = 8     ,SCREAMING_SNAKE_CASE__			:      float = 0.0     ,SCREAMING_SNAKE_CASE__			:      int = 2_048     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,SCREAMING_SNAKE_CASE__			:      int = 4     ,SCREAMING_SNAKE_CASE__			:      int = 255     ,SCREAMING_SNAKE_CASE__			:      int = 100     ,SCREAMING_SNAKE_CASE__			:      float = 0.1     ,SCREAMING_SNAKE_CASE__			:      float = 2.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      float = 5.0     ,SCREAMING_SNAKE_CASE__			:      int = 12_544     ,SCREAMING_SNAKE_CASE__			:      float = 3.0     ,SCREAMING_SNAKE_CASE__			:      float = 0.75     ,SCREAMING_SNAKE_CASE__			:      float = 0.02     ,SCREAMING_SNAKE_CASE__			:      float = 1.0     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      List[int] = [4, 8, 16, 32]     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     if backbone_config is None:
       logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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=SCREAMING_SNAKE_CASE__     ,out_features=["stage1", "stage2", "stage3", "stage4"]     ,)

     if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Any						      =					backbone_config.pop("model_type"							)
       SCREAMING_SNAKE_CASE:List[str]						      =					CONFIG_MAPPING[backbone_model_type]
       SCREAMING_SNAKE_CASE:List[str]						      =					config_class.from_dict(SCREAMING_SNAKE_CASE__							)

     # 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							)}'''							)

     SCREAMING_SNAKE_CASE:List[str]						      =					backbone_config
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_size
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					mask_feature_size
     SCREAMING_SNAKE_CASE:str						      =					hidden_dim
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_feedforward_dim
     SCREAMING_SNAKE_CASE:Tuple						      =					activation_function
     SCREAMING_SNAKE_CASE:Optional[int]						      =					encoder_layers
     SCREAMING_SNAKE_CASE:List[Any]						      =					decoder_layers
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_attention_heads
     SCREAMING_SNAKE_CASE:Dict						      =					dropout
     SCREAMING_SNAKE_CASE:List[str]						      =					dim_feedforward
     SCREAMING_SNAKE_CASE:int						      =					pre_norm
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					enforce_input_projection
     SCREAMING_SNAKE_CASE:Any						      =					common_stride
     SCREAMING_SNAKE_CASE:int						      =					ignore_value
     SCREAMING_SNAKE_CASE:List[Any]						      =					num_queries
     SCREAMING_SNAKE_CASE:Dict						      =					no_object_weight
     SCREAMING_SNAKE_CASE:str						      =					class_weight
     SCREAMING_SNAKE_CASE:Tuple						      =					mask_weight
     SCREAMING_SNAKE_CASE:Optional[int]						      =					dice_weight
     SCREAMING_SNAKE_CASE:int						      =					train_num_points
     SCREAMING_SNAKE_CASE:str						      =					oversample_ratio
     SCREAMING_SNAKE_CASE:str						      =					importance_sample_ratio
     SCREAMING_SNAKE_CASE:str						      =					init_std
     SCREAMING_SNAKE_CASE:Any						      =					init_xavier_std
     SCREAMING_SNAKE_CASE:List[Any]						      =					use_auxiliary_loss
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					feature_strides
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					output_auxiliary_logits
     SCREAMING_SNAKE_CASE:Dict						      =					decoder_layers

     super().__init__(**SCREAMING_SNAKE_CASE__							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      PretrainedConfig     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return cls(
         backbone_config=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,)







   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE:str						      =					self.backbone_config.to_dict()
     SCREAMING_SNAKE_CASE:List[str]						      =					self.__class__.model_type
     return output






