

'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def __init__(   self		:   Tuple			,				a__		:   Union[str, Any]			,				a__		:   Union[str, Any]=7			,				a__		:   Dict=3			,				a__		:   Optional[Any]=18			,				a__		:   Optional[Any]=30			,				a__		:   Tuple=400			,				a__		:   Optional[int]=True			,				a__		:   int=None			,				a__		:   Union[str, Any]=True			,				a__		:   Optional[Any]=None			,				a__		:   str=True			,				a__		:   List[Any]=[0.5, 0.5, 0.5]			,				a__		:   Tuple=[0.5, 0.5, 0.5]			,				a__		:   Union[str, Any]=False			,				):
      __magic_name__						       =    size if size is not None else {'''height''': 20, '''width''': 20}
      __magic_name__						       =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      __magic_name__						       =    parent
      __magic_name__						       =    batch_size
      __magic_name__						       =    num_channels
      __magic_name__						       =    image_size
      __magic_name__						       =    min_resolution
      __magic_name__						       =    max_resolution
      __magic_name__						       =    do_resize
      __magic_name__						       =    size
      __magic_name__						       =    do_center_crop
      __magic_name__						       =    crop_size
      __magic_name__						       =    do_normalize
      __magic_name__						       =    image_mean
      __magic_name__						       =    image_std
      __magic_name__						       =    do_reduce_labels



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }




def 						UpperCamelCase	(			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(dataset[0]['''file''']			)
     __magic_name__						       =    Image.open(dataset[1]['''file''']			)

     return image, map




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(ds[0]['''file''']			)
     __magic_name__						       =    Image.open(ds[1]['''file''']			)
     __magic_name__						       =    Image.open(ds[2]['''file''']			)
     __magic_name__						       =    Image.open(ds[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					BeitImageProcessor if is_vision_available() else None



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    BeitImageProcessingTester(self      )



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return self.image_processor_tester.prepare_image_processor_dict()



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(a__			,				'''do_resize'''      )      )
      self.assertTrue(hasattr(a__			,				'''size'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_normalize'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_mean'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_std'''      )      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size			,				{'''height''': 20, '''width''': 20}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 18, '''width''': 18}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )

      __magic_name__						       =    self.image_processing_class.from_dict(
          self.image_processor_dict			,				size=42			,				crop_size=84			,				reduce_labels=a__      )
      self.assertEqual(image_processor.size			,				{'''height''': 42, '''width''': 42}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 84, '''width''': 84}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      pass



 def 			snake_case__  (   self		:   Dict      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				Image.Image      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Optional[int]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				numpify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				np.ndarray      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   List[str]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      __magic_name__						       =    []
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )
           maps.append(torch.zeros(image.shape[-2:]      ).long()      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				maps[0]			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test not batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_batch_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              2,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )



 def 			snake_case__  (   self		:   Any      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 150      )

      __magic_name__						       =    True
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )









'''simple docstring'''




import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    SwinConfig.from_pretrained(
         '''microsoft/swin-tiny-patch4-window7-224'''		,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
     __magic_name__						       =    MaskFormerConfig(backbone_config=a			)

     __magic_name__						       =    '''huggingface/label-files'''
     if "ade20k-full" in model_name:
          # this should be ok
          __magic_name__						       =    847
          __magic_name__						       =    '''maskformer-ade20k-full-id2label.json'''
     elif "ade" in model_name:
          # this should be ok
          __magic_name__						       =    150
          __magic_name__						       =    '''ade20k-id2label.json'''
     elif "coco-stuff" in model_name:
          # this should be ok
          __magic_name__						       =    171
          __magic_name__						       =    '''maskformer-coco-stuff-id2label.json'''
     elif "coco" in model_name:
          # TODO
          __magic_name__						       =    133
          __magic_name__						       =    '''coco-panoptic-id2label.json'''
     elif "cityscapes" in model_name:
          # this should be ok
          __magic_name__						       =    19
          __magic_name__						       =    '''cityscapes-id2label.json'''
     elif "vistas" in model_name:
          # this should be ok
          __magic_name__						       =    65
          __magic_name__						       =    '''mapillary-vistas-id2label.json'''

     __magic_name__						       =    json.load(open(hf_hub_download(a		,						a		,						repo_type='''dataset'''			)		,						'''r'''			)			)
     __magic_name__						       =    {int(a			): v for k, v in idalabel.items()}

     return config




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    []
     # stem
     # fmt: off
     rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')			)
     rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')			)
     rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')			)
     rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')			)
     # stages
     for i in range(len(config.backbone_config.depths			)			):
          for j in range(config.backbone_config.depths[i]			):
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')			)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')			)

          if i < 3:
               rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')			)
               rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')			)
               rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')			)
          rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')			)
          rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')			)

     # FPN
     rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')			)
     rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')			)
     rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')			)
     for source_index, target_index in zip(range(3		,						0		,						-1			)		,						range(0		,						3			)			):
          rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')			)
          rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')			)
          rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')			)
          rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')			)
          rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')			)
          rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')			)
     rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')			)
     rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')			)

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers			):
          # self-attention out projection
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')			)
          # cross-attention out projection
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')			)
          # MLP 1
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')			)
          # MLP 2
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')			)
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')			)
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')			)
          # layernorm 3 (final layernorm)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')			)

     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')			)
     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')			)

     # heads on top
     rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')			)

     rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')			)
     rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')			)

     rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')			)
     rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')			)

     for i in range(3			):
          rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')			)
          rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')			)
     # fmt: on

     return rename_keys




def 						UpperCamelCase	(			a		,						a		,						a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    dct.pop(a			)
     __magic_name__						       =    val




def 						UpperCamelCase	(			a		,						a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
     for i in range(len(backbone_config.depths			)			):
          __magic_name__						       =    num_features[i]
          for j in range(backbone_config.depths[i]			):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               __magic_name__						       =    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
               __magic_name__						       =    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''			)
               # next, add query, keys and values (in that order) to the state dict
               __magic_name__						       =    in_proj_weight[:dim, :]
               __magic_name__						       =    in_proj_bias[: dim]
               __magic_name__						       =    in_proj_weight[
                   dim : dim * 2, :
               ]
               __magic_name__						       =    in_proj_bias[
                   dim : dim * 2
               ]
               __magic_name__						       =    in_proj_weight[
                   -dim :, :
               ]
               __magic_name__						       =    in_proj_bias[-dim :]
               # fmt: on




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     # fmt: off
     __magic_name__						       =    config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers			):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
          __magic_name__						       =    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __magic_name__						       =    in_proj_weight[: hidden_size, :]
          __magic_name__						       =    in_proj_bias[:config.hidden_size]
          __magic_name__						       =    in_proj_weight[hidden_size : hidden_size * 2, :]
          __magic_name__						       =    in_proj_bias[hidden_size : hidden_size * 2]
          __magic_name__						       =    in_proj_weight[-hidden_size :, :]
          __magic_name__						       =    in_proj_bias[-hidden_size :]
     # fmt: on




def 						UpperCamelCase	(			)				->     torch.Tensor:






     '''simple docstring'''
     __magic_name__						       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __magic_name__						       =    Image.open(requests.get(a		,						stream=a			).raw			)
     return im




@torch.no_grad()
def 						UpperCamelCase	(			a		,						a		,						a		,						a = False			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    get_maskformer_config(a			)

     # load original state_dict
     with open(a		,						'''rb'''			) as f:
          __magic_name__						       =    pickle.load(a			)
     __magic_name__						       =    data['''model''']

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     __magic_name__						       =    create_rename_keys(a			)
     for src, dest in rename_keys:
          rename_key(a		,						a		,						a			)
     read_in_swin_q_k_v(a		,						config.backbone_config			)
     read_in_decoder_q_k_v(a		,						a			)

     # update to torch tensors
     for key, value in state_dict.items():
          __magic_name__						       =    torch.from_numpy(a			)

     # load 🤗 model
     __magic_name__						       =    MaskFormerForInstanceSegmentation(a			)
     model.eval()

     for name, param in model.named_parameters():
          print(a		,						param.shape			)

     __magic_name__       ,  __magic_name__						       =    model.load_state_dict(a		,						strict=a			)
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(a			) == 0, F'''Unexpected keys: {unexpected_keys}'''

     # verify results
     __magic_name__						       =    prepare_img()
     if "vistas" in model_name:
          __magic_name__						       =    65
     elif "cityscapes" in model_name:
          __magic_name__						       =    6_5535
     else:
          __magic_name__						       =    255
     __magic_name__						       =    True if '''ade''' in model_name else False
     __magic_name__						       =    MaskFormerImageProcessor(ignore_index=a		,						reduce_labels=a			)

     __magic_name__						       =    image_processor(a		,						return_tensors='''pt'''			)

     __magic_name__						       =    model(**a			)

     print('''Logits:'''		,						outputs.class_queries_logits[0, :3, :3]			)

     if model_name == "maskformer-swin-tiny-ade":
          __magic_name__						       =    torch.tensor(
              [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]			)
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,						a		,						atol=1e-4			)
     print('''Looks ok!'''			)

     if pytorch_dump_folder_path is not None:
          print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''			)
          Path(a			).mkdir(exist_ok=a			)
          model.save_pretrained(a			)
          image_processor.save_pretrained(a			)

     if push_to_hub:
          print('''Pushing model and image processor to the hub...'''			)
          model.push_to_hub(F'''nielsr/{model_name}'''			)
          image_processor.push_to_hub(F'''nielsr/{model_name}'''			)


if __name__ == "__main__":
      _lowerCAmelCase         =						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="maskformer-swin-tiny-ade",
          type=str,
          help=("Name of the MaskFormer model you'd like to convert",),
      )
      parser.add_argument(
          "--checkpoint_path",
          default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
          type=str,
          help="Path to the original state dict (.pth file).",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
      )

      _lowerCAmelCase         =						parser.parse_args()
      convert_maskformer_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )






