'''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()
UpperCamelCase__     :	List[str]				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  SwinConfig.from_pretrained(
       '''microsoft/swin-tiny-patch4-window7-224'''  ,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
   UpperCAmelCase__							:				Dict   				=  MaskFormerConfig(backbone_config=_A    )

   UpperCAmelCase__							:				List[str]   				=  '''huggingface/label-files'''
   if "ade20k-full" in model_name:
      # this should be ok
      UpperCAmelCase__							:				Optional[int]   				=  8_47
      UpperCAmelCase__							:				Optional[Any]   				=  '''maskformer-ade20k-full-id2label.json'''
   elif "ade" in model_name:
      # this should be ok
      UpperCAmelCase__							:				List[str]   				=  1_50
      UpperCAmelCase__							:				List[Any]   				=  '''ade20k-id2label.json'''
   elif "coco-stuff" in model_name:
      # this should be ok
      UpperCAmelCase__							:				Any   				=  1_71
      UpperCAmelCase__							:				List[Any]   				=  '''maskformer-coco-stuff-id2label.json'''
   elif "coco" in model_name:
      # TODO
      UpperCAmelCase__							:				Tuple   				=  1_33
      UpperCAmelCase__							:				Any   				=  '''coco-panoptic-id2label.json'''
   elif "cityscapes" in model_name:
      # this should be ok
      UpperCAmelCase__							:				int   				=  19
      UpperCAmelCase__							:				Dict   				=  '''cityscapes-id2label.json'''
   elif "vistas" in model_name:
      # this should be ok
      UpperCAmelCase__							:				List[Any]   				=  65
      UpperCAmelCase__							:				Optional[int]   				=  '''mapillary-vistas-id2label.json'''

   UpperCAmelCase__							:				int   				=  json.load(open(hf_hub_download(_A  ,			_A  ,			repo_type='''dataset'''    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Optional[Any]   				=  {int(_A    ): v for k, v in idalabel.items()}

   return config







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  []
   # 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					:				List[Any]  ,			_A					:				str  ,			_A					:				Dict    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  dct.pop(_A    )
   UpperCAmelCase__							:				int   				=  val







def __UpperCamelCase(   _A					:				Union[str, Any]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
   for i in range(len(backbone_config.depths    )    ):
      UpperCAmelCase__							:				Any   				=  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)
         UpperCAmelCase__							:				Any   				=  state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''    )
         UpperCAmelCase__							:				str   				=  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
         UpperCAmelCase__							:				List[Any]   				=  in_proj_weight[:dim, :]
         UpperCAmelCase__							:				Optional[int]   				=  in_proj_bias[: dim]
         UpperCAmelCase__							:				Optional[Any]   				=  in_proj_weight[
             dim : dim * 2, :
         ]
         UpperCAmelCase__							:				Union[str, Any]   				=  in_proj_bias[
             dim : dim * 2
         ]
         UpperCAmelCase__							:				Tuple   				=  in_proj_weight[
             -dim :, :
         ]
         UpperCAmelCase__							:				Tuple   				=  in_proj_bias[-dim :]
         # fmt: on







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   # fmt: off
   UpperCAmelCase__							:				Union[str, Any]   				=  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)
      UpperCAmelCase__							:				int   				=  state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''    )
      UpperCAmelCase__							:				Optional[Any]   				=  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
      UpperCAmelCase__							:				Optional[int]   				=  in_proj_weight[: hidden_size, :]
      UpperCAmelCase__							:				Any   				=  in_proj_bias[:config.hidden_size]
      UpperCAmelCase__							:				List[Any]   				=  in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase__							:				int   				=  in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase__							:				Tuple   				=  in_proj_weight[-hidden_size :, :]
      UpperCAmelCase__							:				Tuple   				=  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)
      UpperCAmelCase__							:				Tuple   				=  state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''    )
      UpperCAmelCase__							:				Tuple   				=  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
      UpperCAmelCase__							:				Dict   				=  in_proj_weight[: hidden_size, :]
      UpperCAmelCase__							:				Optional[Any]   				=  in_proj_bias[:config.hidden_size]
      UpperCAmelCase__							:				int   				=  in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase__							:				Any   				=  in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase__							:				Tuple   				=  in_proj_weight[-hidden_size :, :]
      UpperCAmelCase__							:				Dict   				=  in_proj_bias[-hidden_size :]
   # fmt: on







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__							:				Dict   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    )
   return im







@torch.no_grad()
def __UpperCamelCase(   _A					:				str  ,			_A					:				str  ,			_A					:				str  ,			_A					:				bool = False    ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  get_maskformer_config(_A    )

   # load original state_dict
   with open(_A  ,			'''rb'''    ) as f:
      UpperCAmelCase__							:				List[Any]   				=  pickle.load(_A    )
   UpperCAmelCase__							:				Optional[Any]   				=  data['''model''']

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

   # rename keys
   UpperCAmelCase__							:				List[str]   				=  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():
      UpperCAmelCase__							:				List[Any]   				=  torch.from_numpy(_A    )

   # load 🤗 model
   UpperCAmelCase__							:				Union[str, Any]   				=  MaskFormerForInstanceSegmentation(_A    )
   model.eval()

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

   UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  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
   UpperCAmelCase__							:				Any   				=  prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase__							:				Tuple   				=  65
   elif "cityscapes" in model_name:
      UpperCAmelCase__							:				Tuple   				=  6_55_35
   else:
      UpperCAmelCase__							:				int   				=  2_55
   UpperCAmelCase__							:				int   				=  True if '''ade''' in model_name else False
   UpperCAmelCase__							:				int   				=  MaskFormerImageProcessor(ignore_index=_A  ,			reduce_labels=_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  image_processor(_A  ,			return_tensors='''pt'''    )

   UpperCAmelCase__							:				int   				=  model(**_A    )

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

   if model_name == "maskformer-swin-tiny-ade":
      UpperCAmelCase__							:				Dict   				=  torch.tensor(
          [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]    )
   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__":
  UpperCamelCase__     :	str				=			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.'
  )

  UpperCamelCase__     :	Dict				=			parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )





'''simple docstring'''

import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

  from transformers import MaskFormerSwinBackbone
  from transformers.models.maskformer import MaskFormerSwinModel


class 				_lowercase	:
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=13					,lowerCamelCase_=32					,lowerCamelCase_=2					,lowerCamelCase_=3					,lowerCamelCase_=16					,lowerCamelCase_=[1, 2, 1]					,lowerCamelCase_=[2, 2, 4]					,lowerCamelCase_=2					,lowerCamelCase_=2.0					,lowerCamelCase_=True					,lowerCamelCase_=0.0					,lowerCamelCase_=0.0					,lowerCamelCase_=0.1					,lowerCamelCase_="gelu"					,lowerCamelCase_=False					,lowerCamelCase_=True					,lowerCamelCase_=0.02					,lowerCamelCase_=1e-5					,lowerCamelCase_=True					,lowerCamelCase_=None					,lowerCamelCase_=True					,lowerCamelCase_=10					,lowerCamelCase_=8					,lowerCamelCase_=["stage1", "stage2", "stage3"]					,lowerCamelCase_=[1, 2, 3]					,)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  parent
         UpperCAmelCase__							:				Optional[Any]   				=  batch_size
         UpperCAmelCase__							:				Tuple   				=  image_size
         UpperCAmelCase__							:				Tuple   				=  patch_size
         UpperCAmelCase__							:				int   				=  num_channels
         UpperCAmelCase__							:				str   				=  embed_dim
         UpperCAmelCase__							:				List[str]   				=  depths
         UpperCAmelCase__							:				List[Any]   				=  num_heads
         UpperCAmelCase__							:				Optional[Any]   				=  window_size
         UpperCAmelCase__							:				Tuple   				=  mlp_ratio
         UpperCAmelCase__							:				List[str]   				=  qkv_bias
         UpperCAmelCase__							:				List[str]   				=  hidden_dropout_prob
         UpperCAmelCase__							:				str   				=  attention_probs_dropout_prob
         UpperCAmelCase__							:				Any   				=  drop_path_rate
         UpperCAmelCase__							:				List[Any]   				=  hidden_act
         UpperCAmelCase__							:				Optional[int]   				=  use_absolute_embeddings
         UpperCAmelCase__							:				int   				=  patch_norm
         UpperCAmelCase__							:				Dict   				=  layer_norm_eps
         UpperCAmelCase__							:				Tuple   				=  initializer_range
         UpperCAmelCase__							:				Optional[int]   				=  is_training
         UpperCAmelCase__							:				List[Any]   				=  scope
         UpperCAmelCase__							:				Any   				=  use_labels
         UpperCAmelCase__							:				Any   				=  type_sequence_label_size
         UpperCAmelCase__							:				Any   				=  encoder_stride
         UpperCAmelCase__							:				Optional[int]   				=  out_features
         UpperCAmelCase__							:				Optional[Any]   				=  out_indices




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCAmelCase__							:				Any   				=  None
         if self.use_labels:
            UpperCAmelCase__							:				List[str]   				=  ids_tensor([self.batch_size]					,self.type_sequence_label_size							)

         UpperCAmelCase__							:				Tuple   				=  self.get_config()

         return config, pixel_values, labels




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         return MaskFormerSwinConfig(
             image_size=self.image_size					,patch_size=self.patch_size					,num_channels=self.num_channels					,embed_dim=self.embed_dim					,depths=self.depths					,num_heads=self.num_heads					,window_size=self.window_size					,mlp_ratio=self.mlp_ratio					,qkv_bias=self.qkv_bias					,hidden_dropout_prob=self.hidden_dropout_prob					,attention_probs_dropout_prob=self.attention_probs_dropout_prob					,drop_path_rate=self.drop_path_rate					,hidden_act=self.hidden_act					,use_absolute_embeddings=self.use_absolute_embeddings					,path_norm=self.patch_norm					,layer_norm_eps=self.layer_norm_eps					,initializer_range=self.initializer_range					,encoder_stride=self.encoder_stride					,out_features=self.out_features					,out_indices=self.out_indices					,)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  MaskFormerSwinModel(config=lowerCamelCase_							)
         model.to(lowerCamelCase_							)
         model.eval()
         UpperCAmelCase__							:				Union[str, Any]   				=  model(lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
         UpperCAmelCase__							:				Optional[int]   				=  int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

         self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, expected_seq_len, expected_dim)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  MaskFormerSwinBackbone(config=lowerCamelCase_							)
         model.to(lowerCamelCase_							)
         model.eval()
         UpperCAmelCase__							:				Optional[Any]   				=  model(lowerCamelCase_							)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps							)					,len(config.out_features							)							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,[13, 16, 16, 16]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)					,len(config.out_features							)							)
         self.parent.assertListEqual(model.channels					,[16, 32, 64]							)

         # verify ValueError
         with self.parent.assertRaises(lowerCamelCase_							):
            UpperCAmelCase__							:				List[Any]   				=  ['''stem''']
            UpperCAmelCase__							:				int   				=  MaskFormerSwinBackbone(config=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.prepare_config_and_inputs()
         UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  config_and_inputs
         UpperCAmelCase__							:				str   				=  {'''pixel_values''': pixel_values}
         return config, inputs_dict




@require_torch
class 				_lowercase	(      lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      str      							=   (
          (
              MaskFormerSwinModel,
              MaskFormerSwinBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_      :      Union[str, Any]      							=   {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
      UpperCAmelCase_      :      Optional[Any]      							=   False
      UpperCAmelCase_      :      Any      							=   False
      UpperCAmelCase_      :      str      							=   False
      UpperCAmelCase_      :      Union[str, Any]      							=   False
      UpperCAmelCase_      :      List[str]      							=   False




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  MaskFormerSwinModelTester(self							)
         UpperCAmelCase__							:				List[str]   				=  ConfigTester(self					,config_class=lowerCamelCase_					,embed_dim=37							)




      @require_torch_multi_gpu
      @unittest.skip(
          reason=(
              '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
              ''' `nn.DataParallel`'''
          )							)
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()




      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         return




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase_							)




      @unittest.skip('''Swin does not use inputs_embeds'''							)
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         pass




      @unittest.skip('''Swin does not support feedforward chunking'''							)
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[Any]   				=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase__							:				Optional[int]   				=  model_class(lowerCamelCase_							)
            self.assertIsInstance(model.get_input_embeddings()					,(nn.Module)							)
            UpperCAmelCase__							:				int   				=  model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowerCamelCase_					,nn.Linear							)							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



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

         for model_class in self.all_model_classes:
            UpperCAmelCase__							:				List[Any]   				=  model_class(lowerCamelCase_							)
            UpperCAmelCase__							:				Optional[Any]   				=  inspect.signature(model.forward							)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase__							:				Dict   				=  [*signature.parameters.keys()]

            UpperCAmelCase__							:				Optional[int]   				=  ['''pixel_values''']
            self.assertListEqual(arg_names[:1]					,lowerCamelCase_							)




      @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''							)
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         pass




      @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''							)
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  model_class(lowerCamelCase_							)
         model.to(lowerCamelCase_							)
         model.eval()

         with torch.no_grad():
            UpperCAmelCase__							:				List[Any]   				=  model(**self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_							)							)

         UpperCAmelCase__							:				Optional[int]   				=  outputs.hidden_states

         UpperCAmelCase__							:				Union[str, Any]   				=  getattr(
             self.model_tester					,'''expected_num_hidden_layers'''					,len(self.model_tester.depths							) + 1							)
         self.assertEqual(len(lowerCamelCase_							)					,lowerCamelCase_							)

         # Swin has a different seq_length
         UpperCAmelCase__							:				Tuple   				=  (
             config.patch_size
             if isinstance(config.patch_size					,collections.abc.Iterable							)
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase__							:				Union[str, Any]   				=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]							)					,[num_patches, self.model_tester.embed_dim]					,)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase__							:				Optional[int]   				=  (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size					,collections.abc.Iterable							)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
            UpperCAmelCase__							:				Tuple   				=  True
            self.check_hidden_states_output(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

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

            self.check_hidden_states_output(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[Any]   				=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__							:				List[Any]   				=  3

         UpperCAmelCase__							:				List[Any]   				=  (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size					,collections.abc.Iterable							)
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         UpperCAmelCase__							:				Optional[Any]   				=  (
             config.patch_size
             if isinstance(config.patch_size					,collections.abc.Iterable							)
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase__							:				List[Any]   				=  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCAmelCase__							:				Optional[int]   				=  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
            UpperCAmelCase__							:				Optional[int]   				=  True
            self.check_hidden_states_output(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,(padded_height, padded_width)							)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCAmelCase__							:				int   				=  True
            self.check_hidden_states_output(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,(padded_height, padded_width)							)




      @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''							)
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         pass




      @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''							)
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         pass




      @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''							)
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(lowerCamelCase_							):
            UpperCAmelCase__							:				int   				=  0
            return t

         def check_equivalence(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_={}							):
            with torch.no_grad():
               UpperCAmelCase__							:				Any   				=  model(**lowerCamelCase_					,return_dict=lowerCamelCase_					,**lowerCamelCase_							)
               UpperCAmelCase__							:				Optional[int]   				=  model(**lowerCamelCase_					,return_dict=lowerCamelCase_					,**lowerCamelCase_							).to_tuple()

               def recursive_check(lowerCamelCase_					,lowerCamelCase_							):
                  if isinstance(lowerCamelCase_					,(List, Tuple)							):
                     for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase_					,lowerCamelCase_							):
                        recursive_check(lowerCamelCase_					,lowerCamelCase_							)
                  elif isinstance(lowerCamelCase_					,lowerCamelCase_							):
                     for tuple_iterable_value, dict_iterable_value in zip(
                         tuple_object.values()					,dict_object.values()							):
                        recursive_check(lowerCamelCase_					,lowerCamelCase_							)
                  elif tuple_object is None:
                     return
                  else:
                     self.assertTrue(
                         torch.allclose(
                             set_nan_tensor_to_zero(lowerCamelCase_							)					,set_nan_tensor_to_zero(lowerCamelCase_							)					,atol=1e-5							)					,msg=(
                             '''Tuple and dict output are not equal. Difference:'''
                             f''' {torch.max(torch.abs(tuple_object - dict_object							)							)}. Tuple has `nan`:'''
                             f''' {torch.isnan(lowerCamelCase_							).any()} and `inf`: {torch.isinf(lowerCamelCase_							)}. Dict has'''
                             f''' `nan`: {torch.isnan(lowerCamelCase_							).any()} and `inf`: {torch.isinf(lowerCamelCase_							)}.'''
                         )					,)

               recursive_check(lowerCamelCase_					,lowerCamelCase_							)

         for model_class in self.all_model_classes:
            UpperCAmelCase__							:				List[str]   				=  model_class(lowerCamelCase_							)
            model.to(lowerCamelCase_							)
            model.eval()

            UpperCAmelCase__							:				Optional[int]   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				Any   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_							)
            check_equivalence(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				List[str]   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_					,return_labels=lowerCamelCase_							)
            UpperCAmelCase__							:				Tuple   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_					,return_labels=lowerCamelCase_							)
            check_equivalence(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

            UpperCAmelCase__							:				int   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				Optional[Any]   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_							)
            check_equivalence(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,{'''output_hidden_states''': True}							)

            UpperCAmelCase__							:				List[Any]   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_					,return_labels=lowerCamelCase_							)
            UpperCAmelCase__							:				List[str]   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_					,return_labels=lowerCamelCase_							)
            check_equivalence(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,{'''output_hidden_states''': True}							)




@require_torch
class 				_lowercase	(      unittest.TestCase		,lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Dict      							=   (MaskFormerSwinBackbone,) if is_torch_available() else ()
      UpperCAmelCase_      :      Optional[int]      							=   MaskFormerSwinConfig




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  MaskFormerSwinModelTester(self							)




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__							:				List[str]   				=  inputs_dict['''pixel_values'''].shape[0]

         for backbone_class in self.all_model_classes:
            UpperCAmelCase__							:				List[str]   				=  backbone_class(lowerCamelCase_							)
            backbone.to(lowerCamelCase_							)
            backbone.eval()

            UpperCAmelCase__							:				List[Any]   				=  backbone(**lowerCamelCase_							)

            # Test default outputs and verify feature maps
            self.assertIsInstance(outputs.feature_maps					,lowerCamelCase_							)
            self.assertTrue(len(outputs.feature_maps							) == len(backbone.channels							)							)
            for feature_map, n_channels in zip(outputs.feature_maps					,backbone.channels							):
               self.assertTrue(feature_map.shape[:2]					,(batch_size, n_channels)							)
            self.assertIsNone(outputs.hidden_states							)
            self.assertIsNone(outputs.attentions							)

            # Test output_hidden_states=True
            UpperCAmelCase__							:				str   				=  backbone(**lowerCamelCase_					,output_hidden_states=lowerCamelCase_							)
            self.assertIsNotNone(outputs.hidden_states							)
            self.assertTrue(len(outputs.hidden_states							)					,len(backbone.stage_names							)							)
            # We skip the stem layer
            for hidden_states, n_channels in zip(outputs.hidden_states[1:]					,backbone.channels							):
               for hidden_state in hidden_states:
                  # Hidden states are in the format (batch_size, (height * width), n_channels)
                  UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  hidden_state.shape
                  self.assertTrue((h_batch_size, h_n_channels)					,(batch_size, n_channels)							)

            # Test output_attentions=True
            if self.has_attentions:
               UpperCAmelCase__							:				List[str]   				=  backbone(**lowerCamelCase_					,output_attentions=lowerCamelCase_							)
               self.assertIsNotNone(outputs.attentions							)




