'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Any							)  ->    List[str]:
  super().__init__()

  # make sure scheduler can always be converted to DDIM
  __lowerCAmelCase											=						DDIMScheduler.from_config(scheduler.config							)

  self.register_modules(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)





 @torch.no_grad()
 def __call__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,       SCREAMING_SNAKE_CASE__   :	float = 0.0      ,       SCREAMING_SNAKE_CASE__   :	int = 50      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[str] = "pil"      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       )  ->    Union[ImagePipelineOutput, Tuple]:

  # Sample gaussian noise to begin loop
  if isinstance(self.unet.config.sample_size      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						(
       batch_size,
       self.unet.config.in_channels,
       self.unet.config.sample_size,
       self.unet.config.sample_size,
   )
  else:
   __lowerCAmelCase											=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) and len(SCREAMING_SNAKE_CASE__							) != batch_size:
   raise ValueError(
       f"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__							)}, but requested an effective batch"""
       f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

  __lowerCAmelCase											=						randn_tensor(SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__      ,       device=self.device      ,       dtype=self.unet.dtype							)

  # set step values
  self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							)

  for t in self.progress_bar(self.scheduler.timesteps							):
   # 1. predict noise model_output
   __lowerCAmelCase											=						self.unet(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							).sample

   # 2. predict previous mean of image x_t-1 and add variance depending on eta
   # eta corresponds to η in paper and should be between [0, 1]
   # do x_t -> x_t-1
   __lowerCAmelCase											=						self.scheduler.step(
       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       eta=SCREAMING_SNAKE_CASE__      ,       use_clipped_model_output=SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__							).prev_sample

  __lowerCAmelCase											=						(image / 2 + 0.5).clamp(0      ,       1							)
  __lowerCAmelCase											=						image.cpu().permute(0      ,       2      ,       3      ,       1							).numpy()
  if output_type == "pil":
   __lowerCAmelCase											=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__							)

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation







def      UpperCamelCase_						(					snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						3_84
 __lowerCAmelCase											=						7
 if "tiny" in model_name:
  __lowerCAmelCase											=						96
  __lowerCAmelCase											=						(2, 2, 6, 2)
  __lowerCAmelCase											=						(3, 6, 12, 24)
 elif "small" in model_name:
  __lowerCAmelCase											=						96
  __lowerCAmelCase											=						(2, 2, 18, 2)
  __lowerCAmelCase											=						(3, 6, 12, 24)
 elif "base" in model_name:
  __lowerCAmelCase											=						1_28
  __lowerCAmelCase											=						(2, 2, 18, 2)
  __lowerCAmelCase											=						(4, 8, 16, 32)
  __lowerCAmelCase											=						12
  __lowerCAmelCase											=						5_12
 elif "large" in model_name:
  __lowerCAmelCase											=						1_92
  __lowerCAmelCase											=						(2, 2, 18, 2)
  __lowerCAmelCase											=						(6, 12, 24, 48)
  __lowerCAmelCase											=						12
  __lowerCAmelCase											=						7_68

 # set label information
 __lowerCAmelCase											=						1_50
 __lowerCAmelCase											=						"""huggingface/label-files"""
 __lowerCAmelCase											=						"""ade20k-id2label.json"""
 __lowerCAmelCase											=						json.load(open(hf_hub_download(snake_case_						,		snake_case_						,		repo_type="""dataset"""      )						,		"""r"""      )      )
 __lowerCAmelCase											=						{int(snake_case_      ): v for k, v in idalabel.items()}
 __lowerCAmelCase											=						{v: k for k, v in idalabel.items()}

 __lowerCAmelCase											=						SwinConfig(
     embed_dim=snake_case_						,		depths=snake_case_						,		num_heads=snake_case_						,		window_size=snake_case_						,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						,		)
 __lowerCAmelCase											=						UperNetConfig(
     backbone_config=snake_case_						,		auxiliary_in_channels=snake_case_						,		num_labels=snake_case_						,		idalabel=snake_case_						,		labelaid=snake_case_						,		)

 return config







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						[]

 # fmt: off
 # stem
 rename_keys.append(("""backbone.patch_embed.projection.weight""", """backbone.embeddings.patch_embeddings.projection.weight""")      )
 rename_keys.append(("""backbone.patch_embed.projection.bias""", """backbone.embeddings.patch_embeddings.projection.bias""")      )
 rename_keys.append(("""backbone.patch_embed.norm.weight""", """backbone.embeddings.norm.weight""")      )
 rename_keys.append(("""backbone.patch_embed.norm.bias""", """backbone.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.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")      )
   rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")      )

  if i < 3:
   rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")      )
   rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")      )
   rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")      )
  rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")      )
  rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")      )

 # decode head
 rename_keys.extend(
     [
         ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
         ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
         ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
         ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
     ]      )
 # fmt: on

 return rename_keys







def      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       List[str]						,		snake_case_       :       Any      )					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						dct.pop(snake_case_      )
 __lowerCAmelCase											=						val







def      UpperCamelCase_						(					snake_case_       :       str						,		snake_case_       :       str      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
 for i in range(len(backbone_config.depths      )      ):
  __lowerCAmelCase											=						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)
   __lowerCAmelCase											=						state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""      )
   __lowerCAmelCase											=						state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""      )
   # next, add query, keys and values (in that order) to the state dict
   __lowerCAmelCase											=						in_proj_weight[:dim, :]
   __lowerCAmelCase											=						in_proj_bias[: dim]
   __lowerCAmelCase											=						in_proj_weight[
       dim : dim * 2, :
   ]
   __lowerCAmelCase											=						in_proj_bias[
       dim : dim * 2
   ]
   __lowerCAmelCase											=						in_proj_weight[
       -dim :, :
   ]
   __lowerCAmelCase											=						in_proj_bias[-dim :]
   # fmt: on







def      UpperCamelCase_						(					snake_case_       :       str      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						x.shape
 __lowerCAmelCase											=						x.reshape(snake_case_						,		4						,		in_channel // 4      )
 __lowerCAmelCase											=						x[:, [0, 2, 1, 3], :].transpose(1						,		2      ).reshape(snake_case_						,		snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						x.shape
 __lowerCAmelCase											=						x.reshape(snake_case_						,		in_channel // 4						,		4      )
 __lowerCAmelCase											=						x[:, :, [0, 2, 1, 3]].transpose(1						,		2      ).reshape(snake_case_						,		snake_case_      )

 return x







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     int:






 '''simple docstring'''

 __lowerCAmelCase											=						x.shape[0]
 __lowerCAmelCase											=						x.reshape(4						,		in_channel // 4      )
 __lowerCAmelCase											=						x[[0, 2, 1, 3], :].transpose(0						,		1      ).reshape(snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						x.shape[0]
 __lowerCAmelCase											=						x.reshape(in_channel // 4						,		4      )
 __lowerCAmelCase											=						x[:, [0, 2, 1, 3]].transpose(0						,		1      ).reshape(snake_case_      )
 return x







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Tuple						,		snake_case_       :       Dict      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						{
     """upernet-swin-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth""",
     """upernet-swin-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth""",
     """upernet-swin-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth""",
     """upernet-swin-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth""",
 }
 __lowerCAmelCase											=						model_name_to_url[model_name]
 __lowerCAmelCase											=						torch.hub.load_state_dict_from_url(snake_case_						,		map_location="""cpu"""						,		file_name=snake_case_      )[
     """state_dict"""
 ]

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

 __lowerCAmelCase											=						get_upernet_config(snake_case_      )
 __lowerCAmelCase											=						UperNetForSemanticSegmentation(snake_case_      )
 model.eval()

 # replace "bn" => "batch_norm"
 for key in state_dict.copy().keys():
  __lowerCAmelCase											=						state_dict.pop(snake_case_      )
  if "bn" in key:
   __lowerCAmelCase											=						key.replace("""bn"""						,		"""batch_norm"""      )
  __lowerCAmelCase											=						val

 # rename keys
 __lowerCAmelCase											=						create_rename_keys(snake_case_      )
 for src, dest in rename_keys:
  rename_key(snake_case_						,		snake_case_						,		snake_case_      )
 read_in_q_k_v(snake_case_						,		config.backbone_config      )

 # fix downsample parameters
 for key, value in state_dict.items():
  if "downsample" in key:
   if "reduction" in key:
    __lowerCAmelCase											=						reverse_correct_unfold_reduction_order(snake_case_      )
   if "norm" in key:
    __lowerCAmelCase											=						reverse_correct_unfold_norm_order(snake_case_      )

 model.load_state_dict(snake_case_      )

 # verify on image
 __lowerCAmelCase											=						"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
 __lowerCAmelCase											=						Image.open(requests.get(snake_case_						,		stream=snake_case_      ).raw      ).convert("""RGB"""      )

 __lowerCAmelCase											=						SegformerImageProcessor()
 __lowerCAmelCase											=						processor(snake_case_						,		return_tensors="""pt"""      ).pixel_values

 with torch.no_grad():
  __lowerCAmelCase											=						model(snake_case_      )
  __lowerCAmelCase											=						outputs.logits

 print(logits.shape      )
 print("""First values of logits:"""						,		logits[0, 0, :3, :3]      )
 # assert values
 if model_name == "upernet-swin-tiny":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]      )
 elif model_name == "upernet-swin-small":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]      )
 elif model_name == "upernet-swin-base":
  __lowerCAmelCase											=						torch.tensor(
      [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]      )
 elif model_name == "upernet-swin-large":
  __lowerCAmelCase											=						torch.tensor(
      [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]      )
 print("""Logits:"""						,		outputs.logits[0, 0, :3, :3]      )
 assert torch.allclose(outputs.logits[0, 0, :3, :3]						,		snake_case_						,		atol=1E-4      )
 print("""Looks ok!"""      )

 if pytorch_dump_folder_path is not None:
  print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""      )
  model.save_pretrained(snake_case_      )
  print(f"""Saving processor to {pytorch_dump_folder_path}"""      )
  processor.save_pretrained(snake_case_      )

 if push_to_hub:
  print(f"""Pushing model and processor for {model_name} to hub"""      )
  model.push_to_hub(f"""openmmlab/{model_name}"""      )
  processor.push_to_hub(f"""openmmlab/{model_name}"""      )


if __name__ == "__main__":
 _A			:      Dict					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''upernet-swin-tiny''',
     type=str,
     choices=[f'upernet-swin-{size}' for size in ['''tiny''', '''small''', '''base''', '''large''']],
     help='''Name of the Swin + UperNet model you\'d like to convert.''',
 )
 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.'''
 )

 _A			:      int					      =				parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






