

'''simple docstring'''

import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor





class 							_snake_case (       _a						):
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.config_class(**self.inputs_dict							)
     self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     ,"width_multiplier"							)							)





class 							_snake_case :
   def __init__( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=13     ,SCREAMING_SNAKE_CASE__			:      int=64     ,SCREAMING_SNAKE_CASE__			:      int=2     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=3     ,SCREAMING_SNAKE_CASE__			:      Tuple="swish"     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=3     ,SCREAMING_SNAKE_CASE__			:      Dict=32     ,SCREAMING_SNAKE_CASE__			:      Any=0.1     ,SCREAMING_SNAKE_CASE__			:      Tuple=0.02     ,SCREAMING_SNAKE_CASE__			:      List[Any]=True     ,SCREAMING_SNAKE_CASE__			:      List[str]=True     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=10     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      Tuple=0.25     ,SCREAMING_SNAKE_CASE__			:      Dict=0.0     ,SCREAMING_SNAKE_CASE__			:      Dict=0.0     ,):
     SCREAMING_SNAKE_CASE:List[str]						      =					parent
     SCREAMING_SNAKE_CASE:str						      =					batch_size
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_size
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					patch_size
     SCREAMING_SNAKE_CASE:Tuple						      =					num_channels
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					make_divisible(512 * width_multiplier     ,divisor=8							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					hidden_act
     SCREAMING_SNAKE_CASE:List[Any]						      =					conv_kernel_size
     SCREAMING_SNAKE_CASE:List[Any]						      =					output_stride
     SCREAMING_SNAKE_CASE:List[Any]						      =					classifier_dropout_prob
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					use_labels
     SCREAMING_SNAKE_CASE:Optional[int]						      =					is_training
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_labels
     SCREAMING_SNAKE_CASE:List[str]						      =					initializer_range
     SCREAMING_SNAKE_CASE:List[Any]						      =					scope
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					width_multiplier
     SCREAMING_SNAKE_CASE:List[Any]						      =					ffn_dropout
     SCREAMING_SNAKE_CASE:List[str]						      =					attn_dropout
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:str						      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

     SCREAMING_SNAKE_CASE:str						      =					None
     SCREAMING_SNAKE_CASE:List[Any]						      =					None
     if self.use_labels:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					ids_tensor([self.batch_size]     ,self.num_labels							)
       SCREAMING_SNAKE_CASE:Any						      =					ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.get_config()

     return config, pixel_values, labels, pixel_labels
   def     __UpperCamelCase						( self			:      List[Any]							):
     return MobileViTVaConfig(
         image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_act=self.hidden_act     ,conv_kernel_size=self.conv_kernel_size     ,output_stride=self.output_stride     ,classifier_dropout_prob=self.classifier_dropout_prob     ,initializer_range=self.initializer_range     ,width_multiplier=self.width_multiplier     ,ffn_dropout=self.ffn_dropout_prob     ,attn_dropout=self.attn_dropout_prob     ,)
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      List[str]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					MobileViTVaModel(config=SCREAMING_SNAKE_CASE__							)
     model.to(SCREAMING_SNAKE_CASE__							)
     model.eval()
     SCREAMING_SNAKE_CASE:Any						      =					model(SCREAMING_SNAKE_CASE__							)
     self.parent.assertEqual(
         result.last_hidden_state.shape     ,(
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )     ,)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      int							):
     SCREAMING_SNAKE_CASE:int						      =					self.num_labels
     SCREAMING_SNAKE_CASE:List[str]						      =					MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE__							)
     model.to(SCREAMING_SNAKE_CASE__							)
     model.eval()
     SCREAMING_SNAKE_CASE:Any						      =					model(SCREAMING_SNAKE_CASE__     ,labels=SCREAMING_SNAKE_CASE__							)
     self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)							)
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      str							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.num_labels
     SCREAMING_SNAKE_CASE:Tuple						      =					MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__							)
     model.to(SCREAMING_SNAKE_CASE__							)
     model.eval()
     SCREAMING_SNAKE_CASE:int						      =					model(SCREAMING_SNAKE_CASE__							)
     self.parent.assertEqual(
         result.logits.shape     ,(
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )     ,)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					model(SCREAMING_SNAKE_CASE__     ,labels=SCREAMING_SNAKE_CASE__							)
     self.parent.assertEqual(
         result.logits.shape     ,(
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )     ,)







   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:int						      =					self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					config_and_inputs
     SCREAMING_SNAKE_CASE:List[Any]						      =					{"pixel_values": pixel_values}
     return config, inputs_dict





@require_torch
class 							_snake_case (       _a	,  _a	,  unittest.TestCase						):
   _A       :     Union[str, Any]       =					(
       (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
       if is_torch_available()
       else ()
   )

   _A       :     Union[str, Any]       =					(
       {
           '''feature-extraction''': MobileViTVaModel,
           '''image-classification''': MobileViTVaForImageClassification,
           '''image-segmentation''': MobileViTVaForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   _A       :     List[Any]       =					False
   _A       :     Union[str, Any]       =					False
   _A       :     Any       =					False
   _A       :     Optional[int]       =					False
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:int						      =					MobileViTVaModelTester(self							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					MobileViTVaConfigTester(self     ,config_class=SCREAMING_SNAKE_CASE__     ,has_text_modality=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     self.config_tester.run_common_tests()
   @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"							)
   def     __UpperCamelCase						( self			:      int							):
     pass
   @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"							)
   def     __UpperCamelCase						( self			:      Dict							):
     pass
   @unittest.skip(reason="MobileViTV2 does not output attentions"							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     pass
   @require_torch_multi_gpu
   @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."							)
   def     __UpperCamelCase						( self			:      Tuple							):
     pass
   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
   def     __UpperCamelCase						( self			:      str							):
     pass
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model_class(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:List[Any]						      =					inspect.signature(model.forward							)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       SCREAMING_SNAKE_CASE:Tuple						      =					[*signature.parameters.keys()]

       SCREAMING_SNAKE_CASE:List[str]						      =					["pixel_values"]
       self.assertListEqual(arg_names[:1]     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:int						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple							):
     def check_hidden_states_output(SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Tuple							):
       SCREAMING_SNAKE_CASE:Dict						      =					model_class(SCREAMING_SNAKE_CASE__							)
       model.to(SCREAMING_SNAKE_CASE__							)
       model.eval()

       with torch.no_grad():
         SCREAMING_SNAKE_CASE:int						      =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)							)

       SCREAMING_SNAKE_CASE:List[Any]						      =					outputs.hidden_states

       SCREAMING_SNAKE_CASE:List[Any]						      =					5
       self.assertEqual(len(SCREAMING_SNAKE_CASE__							)     ,SCREAMING_SNAKE_CASE__							)

       # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
       # with the width and height being successively divided by 2.
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					2
       for i in range(len(SCREAMING_SNAKE_CASE__							)							):
         self.assertListEqual(
             list(hidden_states[i].shape[-2:]							)     ,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]     ,)
         divisor *= 2

       self.assertEqual(self.model_tester.output_stride     ,divisor // 2							)

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					True
       check_hidden_states_output(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       SCREAMING_SNAKE_CASE:List[Any]						      =					True

       check_hidden_states_output(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Dict						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE__							)







   @slow
   def     __UpperCamelCase						( self			:      str							):
     for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					MobileViTVaModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
       self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)





def    A_  ( ):
  SCREAMING_SNAKE_CASE:str						      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
  return image





@require_torch
@require_vision
class 							_snake_case (       unittest.TestCase						):
   @cached_property
   def     __UpperCamelCase						( self			:      Tuple							):
     return (
         MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"							)
         if is_vision_available()
         else None
     )
   @slow
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Tuple						      =					MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"							).to(
         SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					self.default_image_processor
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					prepare_img()
     SCREAMING_SNAKE_CASE:Any						      =					image_processor(images=SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).to(SCREAMING_SNAKE_CASE__							)

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Tuple						      =					model(**SCREAMING_SNAKE_CASE__							)

     # verify the logits
     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Size((1, 1_000)							)
     self.assertEqual(outputs.logits.shape     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]							).to(SCREAMING_SNAKE_CASE__							)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-4							)							)
   @slow
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:int						      =					MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"							)
     SCREAMING_SNAKE_CASE:str						      =					model.to(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"							)

     SCREAMING_SNAKE_CASE:Tuple						      =					prepare_img()
     SCREAMING_SNAKE_CASE:Tuple						      =					image_processor(images=SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).to(SCREAMING_SNAKE_CASE__							)

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Optional[int]						      =					model(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					outputs.logits

     # verify the logits
     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Size((1, 21, 32, 32)							)
     self.assertEqual(logits.shape     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.tensor(
         [
             [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
             [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
             [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
         ]     ,device=SCREAMING_SNAKE_CASE__     ,)

     self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,SCREAMING_SNAKE_CASE__     ,atol=1e-4							)							)







   @slow
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"							)
     SCREAMING_SNAKE_CASE:str						      =					model.to(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"							)

     SCREAMING_SNAKE_CASE:Tuple						      =					prepare_img()
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_processor(images=SCREAMING_SNAKE_CASE__     ,return_tensors="pt"							).to(SCREAMING_SNAKE_CASE__							)

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					model(**SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:int						      =					outputs.logits.detach().cpu()

     SCREAMING_SNAKE_CASE:Any						      =					image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE__     ,target_sizes=[(50, 60)]							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Size((50, 60)							)
     self.assertEqual(segmentation[0].shape     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					torch.Size((32, 32)							)
     self.assertEqual(segmentation[0].shape     ,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






