
"""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_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     384
							if "tiny" in model_name:
														SCREAMING_SNAKE_CASE__		: str     =     [3, 3, 9, 3]
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     [96, 192, 384, 768]
							if "small" in model_name:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [3, 3, 27, 3]
														SCREAMING_SNAKE_CASE__		: Any     =     [96, 192, 384, 768]
							if "base" in model_name:
														SCREAMING_SNAKE_CASE__		: List[Any]     =     [3, 3, 27, 3]
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     [128, 256, 512, 1_024]
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     512
							if "large" in model_name:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     [3, 3, 27, 3]
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [192, 384, 768, 1_536]
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     768
							if "xlarge" in model_name:
														SCREAMING_SNAKE_CASE__		: List[Any]     =     [3, 3, 27, 3]
														SCREAMING_SNAKE_CASE__		: Tuple     =     [256, 512, 1_024, 2_048]
														SCREAMING_SNAKE_CASE__		: int     =     1_024

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

							SCREAMING_SNAKE_CASE__		: Dict     =     ConvNextConfig(
							    depths=_A     ,hidden_sizes=_A     ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     UperNetConfig(
							    backbone_config=_A     ,auxiliary_in_channels=_A     ,num_labels=_A     ,idalabel=_A     ,labelaid=_A     ,)

							return config

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: int     =     []

							# 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_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dct.pop(_A		)
							SCREAMING_SNAKE_CASE__		: Dict     =     val

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, 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__		: Union[str, Any]     =     model_name_to_url[model_name]
							SCREAMING_SNAKE_CASE__		: Tuple     =     torch.hub.load_state_dict_from_url(_A     ,map_location="""cpu"""		)["state_dict"]

							SCREAMING_SNAKE_CASE__		: Dict     =     get_upernet_config(_A		)
							SCREAMING_SNAKE_CASE__		: Dict     =     UperNetForSemanticSegmentation(_A		)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     state_dict.pop(_A		)
														if "bn" in key:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     key.replace("""bn"""     ,"""batch_norm"""		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     val

							# rename keys
							SCREAMING_SNAKE_CASE__		: int     =     create_rename_keys(_A		)
							for src, dest in rename_keys:
														rename_key(_A     ,_A     ,_A		)

							model.load_state_dict(_A		)

							# verify on image
							SCREAMING_SNAKE_CASE__		: int     =     "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
							SCREAMING_SNAKE_CASE__		: List[Any]     =     Image.open(requests.get(_A     ,stream=_A		).raw		).convert("""RGB"""		)

							SCREAMING_SNAKE_CASE__		: Tuple     =     SegformerImageProcessor()
							SCREAMING_SNAKE_CASE__		: List[Any]     =     processor(_A     ,return_tensors="""pt"""		).pixel_values

							with torch.no_grad():
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     model(_A		)

							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__		: Tuple     =     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__		: 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__		: 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":
														SCREAMING_SNAKE_CASE__		: Tuple     =     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]     ,_A     ,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(_A		)
														print(f'''Saving processor to {pytorch_dump_folder_path}'''		)
														processor.save_pretrained(_A		)

							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__":
					UpperCAmelCase__   :					int												=				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.'
					)

					UpperCAmelCase__   :					Union[str, Any]												=				parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""




import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


UpperCAmelCase__   :					Any												=				logging.getLogger()



@unittest.skip('''Temporarily disable the doc tests.'''						)
@require_torch
@require_tf
@slow
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     [file for file in os.listdir(SCREAMING_SNAKE_CASE__					) if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)]

											if identifier is not None:
																		SCREAMING_SNAKE_CASE__		: int     =     [file for file in files if identifier in file]

											if n_identifier is not None:
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									for n_ in n_identifier:
																																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [file for file in files if n_ not in file]
																		else:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     [file for file in files if n_identifier not in file]

											SCREAMING_SNAKE_CASE__		: int     =     ignore_files or []
											ignore_files.append("""__init__.py"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [file for file in files if file not in ignore_files]

											for file in files:
																		# Open all files
																		print("""Testing"""     , SCREAMING_SNAKE_CASE__					)

																		if only_modules:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     file.split("""."""					)[0]
																									try:
																																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     getattr(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																																SCREAMING_SNAKE_CASE__		: Optional[int]     =     doctest.DocTestSuite(SCREAMING_SNAKE_CASE__					)
																																SCREAMING_SNAKE_CASE__		: int     =     unittest.TextTestRunner().run(SCREAMING_SNAKE_CASE__					)
																																self.assertIs(len(result.failures					)     , 0					)
																									except AttributeError:
																																logger.info(F'''{module_identifier} is not a module.'''					)
																		else:
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     doctest.testfile(str("""..""" / directory / file					)     , optionflags=doctest.ELLIPSIS					)
																									self.assertIs(result.failed     , 0					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     """modeling"""
											SCREAMING_SNAKE_CASE__		: int     =     [
											    """modeling_ctrl.py""",
											    """modeling_tf_ctrl.py""",
											]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__     , ignore_files=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     """tokenization"""
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     """configuration"""
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: Dict     =     ["""configuration""", """modeling""", """tokenization"""]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , n_identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     Path("""docs/source"""					)
											SCREAMING_SNAKE_CASE__		: Any     =     ["""favicon.ico"""]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , ignore_files=SCREAMING_SNAKE_CASE__     , only_modules=SCREAMING_SNAKE_CASE__					)




