



'''simple docstring'''

import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 3_84
				if "tiny" in model_name:
								SCREAMING_SNAKE_CASE  :						Any      					= [3, 3, 9, 3]
								SCREAMING_SNAKE_CASE  :						Dict      					= [96, 1_92, 3_84, 7_68]
				if "small" in model_name:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= [3, 3, 27, 3]
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= [96, 1_92, 3_84, 7_68]
				if "base" in model_name:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [3, 3, 27, 3]
								SCREAMING_SNAKE_CASE  :						Any      					= [1_28, 2_56, 5_12, 10_24]
								SCREAMING_SNAKE_CASE  :						List[str]      					= 5_12
				if "large" in model_name:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [3, 3, 27, 3]
								SCREAMING_SNAKE_CASE  :						List[Any]      					= [1_92, 3_84, 7_68, 15_36]
								SCREAMING_SNAKE_CASE  :						int      					= 7_68
				if "xlarge" in model_name:
								SCREAMING_SNAKE_CASE  :						Dict      					= [3, 3, 27, 3]
								SCREAMING_SNAKE_CASE  :						Dict      					= [2_56, 5_12, 10_24, 20_48]
								SCREAMING_SNAKE_CASE  :						List[Any]      					= 10_24

				# set label information
				SCREAMING_SNAKE_CASE  :						Dict      					= 1_50
				SCREAMING_SNAKE_CASE  :						Any      					= '''huggingface/label-files'''
				SCREAMING_SNAKE_CASE  :						Tuple      					= '''ade20k-id2label.json'''
				SCREAMING_SNAKE_CASE  :						List[str]      					= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	repo_type='''dataset'''       )    ,	'''r'''       )       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE  :						Tuple      					= {v: k for k, v in idalabel.items()}

				SCREAMING_SNAKE_CASE  :						Any      					= ConvNextConfig(
				    depths=_SCREAMING_SNAKE_CASE    ,	hidden_sizes=_SCREAMING_SNAKE_CASE    ,	out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= UperNetConfig(
				    backbone_config=_SCREAMING_SNAKE_CASE    ,	auxiliary_in_channels=_SCREAMING_SNAKE_CASE    ,	num_labels=_SCREAMING_SNAKE_CASE    ,	idalabel=_SCREAMING_SNAKE_CASE    ,	labelaid=_SCREAMING_SNAKE_CASE    ,	)

				return config






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= []

				# fmt: off
				# stem
				rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')       )
				rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')       )
				rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')       )
				rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.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}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')       )
												rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')       )
								if i > 0:
												rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')       )
												rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')       )
												rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')       )
												rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.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       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= dct.pop(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {
				    '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
				    '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
				    '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
				    '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
				    '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
				}
				SCREAMING_SNAKE_CASE  :						List[Any]      					= model_name_to_url[model_name]
				SCREAMING_SNAKE_CASE  :						List[str]      					= torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE    ,	map_location='''cpu'''       )['''state_dict''']

				SCREAMING_SNAKE_CASE  :						List[Any]      					= get_upernet_config(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= UperNetForSemanticSegmentation(_SCREAMING_SNAKE_CASE       )
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								SCREAMING_SNAKE_CASE  :						List[str]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
								if "bn" in key:
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= key.replace('''bn'''    ,	'''batch_norm'''       )
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val

				# rename keys
				SCREAMING_SNAKE_CASE  :						Any      					= create_rename_keys(_SCREAMING_SNAKE_CASE       )
				for src, dest in rename_keys:
								rename_key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= SegformerImageProcessor()
				SCREAMING_SNAKE_CASE  :						Dict      					= processor(_SCREAMING_SNAKE_CASE    ,	return_tensors='''pt'''       ).pixel_values

				with torch.no_grad():
								SCREAMING_SNAKE_CASE  :						List[str]      					= model(_SCREAMING_SNAKE_CASE       )

				if model_name == "upernet-convnext-tiny":
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.tensor(
								    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]       )
				elif model_name == "upernet-convnext-small":
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.tensor(
								    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]       )
				elif model_name == "upernet-convnext-base":
								SCREAMING_SNAKE_CASE  :						List[str]      					= torch.tensor(
								    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]       )
				elif model_name == "upernet-convnext-large":
								SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.tensor(
								    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]       )
				elif model_name == "upernet-convnext-xlarge":
								SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor(
								    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]       )
				print('''Logits:'''    ,	outputs.logits[0, 0, :3, :3]       )
				assert torch.allclose(outputs.logits[0, 0, :3, :3]    ,	_SCREAMING_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(_SCREAMING_SNAKE_CASE       )
								print(F'''Saving processor to {pytorch_dump_folder_path}'''       )
								processor.save_pretrained(_SCREAMING_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__":
	snake_case_			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""upernet-convnext-tiny""",
	    type=str,
	    choices=[f'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
	    help="""Name of the ConvNext 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."""
	)

	snake_case_			=     parser.parse_args()
	convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler


@dataclass
class 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     False
	__magic_name__		:    float	    =     3.0



class 	a__     (				unittest.TestCase	):


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

					"""simple docstring"""

					self.assertDictEqual(MockClass().to_kwargs(),					{}      )
					self.assertDictEqual(MockClass(a=2      ).to_kwargs(),					{'''a''': 2}      )
					self.assertDictEqual(MockClass(a=2,					b=__UpperCAmelCase      ).to_kwargs(),					{'''a''': 2, '''b''': True}      )
					self.assertDictEqual(MockClass(a=2,					c=2.25      ).to_kwargs(),					{'''a''': 2, '''c''': 2.25}      )


	@require_cuda
	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= GradScalerKwargs(init_scale=1024,					growth_factor=2      )
					AcceleratorState._reset_state()
					SCREAMING_SNAKE_CASE  :						Tuple      					= Accelerator(mixed_precision='''fp16''',					kwargs_handlers=[scaler_handler]      )
					print(accelerator.use_fpaa      )
					SCREAMING_SNAKE_CASE  :						str      					= accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale,					1024.0      )
					self.assertEqual(scaler._growth_factor,					2.0      )

					# Check the other values are at the default
					self.assertEqual(scaler._backoff_factor,					0.5      )
					self.assertEqual(scaler._growth_interval,					2000      )
					self.assertEqual(scaler._enabled,					__UpperCAmelCase      )






	@require_multi_gpu
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
					execute_subprocess_async(__UpperCAmelCase,					env=os.environ.copy()      )


if __name__ == "__main__":
	snake_case_			=     DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	snake_case_			=     Accelerator(kwargs_handlers=[ddp_scaler])
	snake_case_			=     torch.nn.Linear(100, 200)
	snake_case_			=     accelerator.prepare(model)

	# Check the values changed in kwargs
	snake_case_			=     """"""
	snake_case_			=     model.bucket_bytes_cap // (1024 * 1024)
	if observed_bucket_cap_map != 15:
		error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
	if model.find_unused_parameters is not True:
		error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

	# Check the values of the defaults
	if model.dim != 0:
		error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
	if model.broadcast_buffers is not True:
		error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
	if model.gradient_as_bucket_view is not False:
		error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)




