


'''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 lowerCamelCase      (      lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					384
	if "tiny" in model_name:
		__magic_name__	:   List[Any]					     =					[3, 3, 9, 3]
		__magic_name__	:   Optional[Any]					     =					[96, 192, 384, 768]
	if "small" in model_name:
		__magic_name__	:   List[Any]					     =					[3, 3, 27, 3]
		__magic_name__	:   Any					     =					[96, 192, 384, 768]
	if "base" in model_name:
		__magic_name__	:   List[str]					     =					[3, 3, 27, 3]
		__magic_name__	:   Dict					     =					[128, 256, 512, 1024]
		__magic_name__	:   int					     =					512
	if "large" in model_name:
		__magic_name__	:   Optional[Any]					     =					[3, 3, 27, 3]
		__magic_name__	:   int					     =					[192, 384, 768, 1536]
		__magic_name__	:   Tuple					     =					768
	if "xlarge" in model_name:
		__magic_name__	:   List[str]					     =					[3, 3, 27, 3]
		__magic_name__	:   Union[str, Any]					     =					[256, 512, 1024, 2048]
		__magic_name__	:   Dict					     =					1024

	# set label information
	__magic_name__	:   Dict					     =					150
	__magic_name__	:   Optional[int]					     =					'huggingface/label-files'
	__magic_name__	:   Optional[Any]					     =					'ade20k-id2label.json'
	__magic_name__	:   str					     =					json.load(open(hf_hub_download(lowerCAmelCase		,		lowerCAmelCase		,		repo_type='dataset'		)		,		'r'		)		)
	__magic_name__	:   Union[str, Any]					     =					{int(lowerCAmelCase		): v for k, v in idalabel.items()}
	__magic_name__	:   List[Any]					     =					{v: k for k, v in idalabel.items()}

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

	return config





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					[]

	# 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 lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					dct.pop(lowerCAmelCase		)
	__magic_name__	:   str					     =					val





def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	__magic_name__	:   str					     =					{
	    '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',
	}
	__magic_name__	:   Dict					     =					model_name_to_url[model_name]
	__magic_name__	:   Dict					     =					torch.hub.load_state_dict_from_url(lowerCAmelCase		,		map_location='cpu'		)['state_dict']

	__magic_name__	:   Optional[int]					     =					get_upernet_config(lowerCAmelCase		)
	__magic_name__	:   Dict					     =					UperNetForSemanticSegmentation(lowerCAmelCase		)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		__magic_name__	:   int					     =					state_dict.pop(lowerCAmelCase		)
		if "bn" in key:
			__magic_name__	:   Any					     =					key.replace('bn'		,		'batch_norm'		)
		__magic_name__	:   str					     =					val

	# rename keys
	__magic_name__	:   str					     =					create_rename_keys(lowerCAmelCase		)
	for src, dest in rename_keys:
		rename_key(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	model.load_state_dict(lowerCAmelCase		)

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

	__magic_name__	:   Any					     =					SegformerImageProcessor()
	__magic_name__	:   Tuple					     =					processor(lowerCAmelCase		,		return_tensors='pt'		).pixel_values

	with torch.no_grad():
		__magic_name__	:   int					     =					model(lowerCAmelCase		)

	if model_name == "upernet-convnext-tiny":
		__magic_name__	:   Any					     =					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":
		__magic_name__	:   Any					     =					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":
		__magic_name__	:   Tuple					     =					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":
		__magic_name__	:   Union[str, 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":
		__magic_name__	:   Optional[int]					     =					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]		,		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__":
							lowerCAmelCase				:Union[str, Any]        =      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.'''
							)

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



'''simple docstring'''

# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase				:Optional[int]        =      abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	config.addinivalue_line(
	    'markers'		,		'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'		)
	config.addinivalue_line(
	    'markers'		,		'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'		)
	config.addinivalue_line('markers'		,		'is_pipeline_test: mark test to run only when pipelines are tested'		)
	config.addinivalue_line('markers'		,		'is_staging_test: mark test to run only in the staging environment'		)
	config.addinivalue_line('markers'		,		'accelerate_tests: mark test that require accelerate'		)
	config.addinivalue_line('markers'		,		'tool_tests: mark the tool tests that are run on their specific schedule'		)





def lowerCamelCase      (      lowerCAmelCase    :     Any		):





	"""simple docstring"""







	from transformers.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	from transformers.testing_utils import pytest_terminal_summary_main

	__magic_name__	:   Tuple					     =					terminalreporter.config.getoption('--make-reports'		)
	if make_reports:
		pytest_terminal_summary_main(lowerCAmelCase		,		id=lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	if exitstatus == 5:
		__magic_name__	:   Any					     =					0


# Doctest custom flag to ignore output.
lowerCAmelCase				:List[str]        =      doctest.register_optionflag('''IGNORE_RESULT''')

lowerCAmelCase				:Union[str, Any]        =      doctest.OutputChecker
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						Tuple  ,					_A		:						Tuple  ,					_A		:						str      )  ->			int:
			if IGNORE_RESULT & optionflags:
				return True
			return OutputChecker.check_output(self  ,					_A  ,					_A  ,					_A      )







lowerCAmelCase				:Optional[Any]        =      CustomOutputChecker
lowerCAmelCase				:int        =      HfDoctestModule





lowerCAmelCase				:Any        =      HfDocTestParser