






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     (		lowerCamelCase_							):






				'''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(lowerCamelCase_						,  lowerCamelCase_						,  repo_type='''dataset'''							)						,  '''r'''							)							)
				lowercase__  					=     {int(lowerCamelCase_							): v for k, v in idalabel.items()}
				lowercase__  					=     {v: k for k, v in idalabel.items()}

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

				return config






def     a     (		lowerCamelCase_							):






				'''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     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     dct.pop(lowerCamelCase_							)
				lowercase__  					=     val






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''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     (		lowerCamelCase_							):






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






def     a     (		lowerCamelCase_							):






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

				return x






def     a     (		lowerCamelCase_							):






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






def     a     (		lowerCamelCase_							):






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






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''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(lowerCamelCase_						,  map_location='''cpu'''						,  file_name=lowerCamelCase_							)[
				    '''state_dict'''
				]

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

				lowercase__  					=     get_upernet_config(lowerCamelCase_							)
				lowercase__  					=     UperNetForSemanticSegmentation(lowerCamelCase_							)
				model.eval()

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

				# rename keys
				lowercase__  					=     create_rename_keys(lowerCamelCase_							)
				for src, dest in rename_keys:
								rename_key(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)
				read_in_q_k_v(lowerCamelCase_						,  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(lowerCamelCase_							)
												if "norm" in key:
																lowercase__  					=     reverse_correct_unfold_norm_order(lowerCamelCase_							)

				model.load_state_dict(lowerCamelCase_							)

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

				lowercase__  					=     SegformerImageProcessor()
				lowercase__  					=     processor(lowerCamelCase_						,  return_tensors='''pt'''							).pixel_values

				with torch.no_grad():
								lowercase__  					=     model(lowerCamelCase_							)
								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(
								    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]							)
				elif model_name == "upernet-swin-small":
								lowercase__  					=     torch.tensor(
								    [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]							)
				elif model_name == "upernet-swin-base":
								lowercase__  					=     torch.tensor(
								    [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]							)
				elif model_name == "upernet-swin-large":
								lowercase__  					=     torch.tensor(
								    [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]							)
				print('''Logits:'''						,  outputs.logits[0, 0, :3, :3]							)
				assert torch.allclose(outputs.logits[0, 0, :3, :3]						,  lowerCamelCase_						,  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(lowerCamelCase_							)
								print(F"""Saving processor to {pytorch_dump_folder_path}"""							)
								processor.save_pretrained(lowerCamelCase_							)

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












# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def     a     (		lowerCamelCase_=None							):






				'''simple docstring'''
				if subparsers is not None:
								lowercase__  					=     subparsers.add_parser('''test'''							)
				else:
								lowercase__  					=     argparse.ArgumentParser('''Accelerate test command'''							)

				parser.add_argument(
				    '''--config_file'''						,  default=lowerCamelCase_						,  help=(
				        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
				        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
				        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
				        '''with \'huggingface\'.'''
				    )						,  )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase_							)
				return parser






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     os.path.sep.join(__file__.split(os.path.sep							)[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']							)

				if args.config_file is None:
								lowercase__  					=     script_name
				else:
								lowercase__  					=     F"""--config_file={args.config_file} {script_name}"""

				lowercase__  					=     ['''accelerate-launch'''] + test_args.split()
				lowercase__  					=     execute_subprocess_async(lowerCamelCase_						,  env=os.environ.copy()							)
				if result.returncode == 0:
								print('''Test is a success! You are ready for your distributed training!'''							)






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     test_command_parser()
				lowercase__  					=     parser.parse_args()
				test_command(lowerCamelCase_							)


if __name__ == "__main__":
							main()




