
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 							a		(				snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   384
				lowercase_	    =   7
				if "tiny" in model_name:
								lowercase_	    =   96
								lowercase_	    =   (2, 2, 6, 2)
								lowercase_	    =   (3, 6, 12, 24)
				elif "small" in model_name:
								lowercase_	    =   96
								lowercase_	    =   (2, 2, 18, 2)
								lowercase_	    =   (3, 6, 12, 24)
				elif "base" in model_name:
								lowercase_	    =   128
								lowercase_	    =   (2, 2, 18, 2)
								lowercase_	    =   (4, 8, 16, 32)
								lowercase_	    =   12
								lowercase_	    =   512
				elif "large" in model_name:
								lowercase_	    =   192
								lowercase_	    =   (2, 2, 18, 2)
								lowercase_	    =   (6, 12, 24, 48)
								lowercase_	    =   12
								lowercase_	    =   768

				# set label information
				lowercase_	    =   150
				lowercase_	    =   '''huggingface/label-files'''
				lowercase_	    =   '''ade20k-id2label.json'''
				lowercase_	    =   json.load(open(hf_hub_download(snake_case__		,  snake_case__		,  repo_type='''dataset'''   )		,  '''r'''   )   )
				lowercase_	    =   {int(snake_case__   ): v for k, v in idalabel.items()}
				lowercase_	    =   {v: k for k, v in idalabel.items()}

				lowercase_	    =   SwinConfig(
				    embed_dim=snake_case__		,  depths=snake_case__		,  num_heads=snake_case__		,  window_size=snake_case__		,  out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		,  )
				lowercase_	    =   UperNetConfig(
				    backbone_config=snake_case__		,  auxiliary_in_channels=snake_case__		,  num_labels=snake_case__		,  idalabel=snake_case__		,  labelaid=snake_case__		,  )

				return config




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   []

				# 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 							a		(				snake_case__:   Optional[Any]		,  snake_case__:   Dict		,  snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   dct.pop(snake_case__   )
				lowercase_	    =   val




def 							a		(				snake_case__:   Dict		,  snake_case__:   Optional[int]   ):


				'''simple docstring'''
				lowercase_	    =   [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
				for i in range(len(backbone_config.depths   )   ):
								lowercase_	    =   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)
												lowercase_	    =   state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''   )
												lowercase_	    =   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
												lowercase_	    =   in_proj_weight[:dim, :]
												lowercase_	    =   in_proj_bias[: dim]
												lowercase_	    =   in_proj_weight[
												    dim : dim * 2, :
												]
												lowercase_	    =   in_proj_bias[
												    dim : dim * 2
												]
												lowercase_	    =   in_proj_weight[
												    -dim :, :
												]
												lowercase_	    =   in_proj_bias[-dim :]
												# fmt: on




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   x.shape
				lowercase_	    =   x.reshape(snake_case__		,  4		,  in_channel // 4   )
				lowercase_	    =   x[:, [0, 2, 1, 3], :].transpose(1		,  2   ).reshape(snake_case__		,  snake_case__   )
				return x




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_	    =   x.shape
				lowercase_	    =   x.reshape(snake_case__		,  in_channel // 4		,  4   )
				lowercase_	    =   x[:, :, [0, 2, 1, 3]].transpose(1		,  2   ).reshape(snake_case__		,  snake_case__   )

				return x




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				lowercase_	    =   x.shape[0]
				lowercase_	    =   x.reshape(4		,  in_channel // 4   )
				lowercase_	    =   x[[0, 2, 1, 3], :].transpose(0		,  1   ).reshape(snake_case__   )
				return x




def 							a		(				snake_case__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   x.shape[0]
				lowercase_	    =   x.reshape(in_channel // 4		,  4   )
				lowercase_	    =   x[:, [0, 2, 1, 3]].transpose(0		,  1   ).reshape(snake_case__   )
				return x




def 							a		(				snake_case__:   Dict		,  snake_case__:   Dict		,  snake_case__:   str   ):


				'''simple docstring'''
				lowercase_	    =   {
				    '''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''',
				}
				lowercase_	    =   model_name_to_url[model_name]
				lowercase_	    =   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   )

				lowercase_	    =   get_upernet_config(snake_case__   )
				lowercase_	    =   UperNetForSemanticSegmentation(snake_case__   )
				model.eval()

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

				# rename keys
				lowercase_	    =   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:
																lowercase_	    =   reverse_correct_unfold_reduction_order(snake_case__   )
												if "norm" in key:
																lowercase_	    =   reverse_correct_unfold_norm_order(snake_case__   )

				model.load_state_dict(snake_case__   )

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

				lowercase_	    =   SegformerImageProcessor()
				lowercase_	    =   processor(snake_case__		,  return_tensors='''pt'''   ).pixel_values

				with torch.no_grad():
								lowercase_	    =   model(snake_case__   )
								lowercase_	    =   outputs.logits

				print(logits.shape   )
				print('''First values of logits:'''		,  logits[0, 0, :3, :3]   )
				# assert values
				if model_name == "upernet-swin-tiny":
								lowercase_	    =   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":
								lowercase_	    =   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":
								lowercase_	    =   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":
								lowercase_	    =   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         =   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         =   parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int | float]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int | float:
       if len(UpperCAmelCase_				) == 0:
              raise ValueError('''find_max() arg is an empty sequence'''				)
       if (
           left >= len(UpperCAmelCase_				)
           or left < -len(UpperCAmelCase_				)
           or right >= len(UpperCAmelCase_				)
           or right < -len(UpperCAmelCase_				)
       ):
              raise IndexError('''list index out of range'''				)
       if left == right:
              return nums[left]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=(left + right) >> 1  # the middle
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)  # find max in range[left, mid]
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							mid + 1			,							UpperCAmelCase_				)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
