





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

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

		SCREAMING_SNAKE_CASE__								=   ConvNextConfig(
		    depths=UpperCamelCase__       , hidden_sizes=UpperCamelCase__       , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     )
		SCREAMING_SNAKE_CASE__								=   UperNetConfig(
		    backbone_config=UpperCamelCase__       , auxiliary_in_channels=UpperCamelCase__       , num_labels=UpperCamelCase__       , idalabel=UpperCamelCase__       , labelaid=UpperCamelCase__       , )

		return config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   []

		# 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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__								=   dct.pop(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   val






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__								=   {
		    """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__								=   model_name_to_url[model_name]
		SCREAMING_SNAKE_CASE__								=   torch.hub.load_state_dict_from_url(UpperCamelCase__       , map_location="""cpu"""     )["""state_dict"""]

		SCREAMING_SNAKE_CASE__								=   get_upernet_config(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   UperNetForSemanticSegmentation(UpperCamelCase__     )
		model.eval()

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

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

		model.load_state_dict(UpperCamelCase__     )

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

		SCREAMING_SNAKE_CASE__								=   SegformerImageProcessor()
		SCREAMING_SNAKE_CASE__								=   processor(UpperCamelCase__       , return_tensors="""pt"""     ).pixel_values

		with torch.no_grad():
				SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__     )

		if model_name == "upernet-convnext-tiny":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]     )
		elif model_name == "upernet-convnext-small":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]     )
		elif model_name == "upernet-convnext-base":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]     )
		elif model_name == "upernet-convnext-large":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]     )
		elif model_name == "upernet-convnext-xlarge":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]     )
		print("""Logits:"""       , outputs.logits[0, 0, :3, :3]     )
		assert torch.allclose(outputs.logits[0, 0, :3, :3]       , UpperCamelCase__       , 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(UpperCamelCase__     )
				print(f'''Saving processor to {pytorch_dump_folder_path}'''     )
				processor.save_pretrained(UpperCamelCase__     )

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

import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a         =   namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 							a		(				snake_case__:   Dict		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				return (abs(source - target   ) / target) < 0.0_1




@pytest.mark.integration
def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   _TestCommandArgs(dataset=snake_case__		,  all_configs=snake_case__		,  save_infos=snake_case__   )
				lowercase_	    =   TestCommand(*snake_case__   )
				test_command.run()
				lowercase_	    =   os.path.join(snake_case__		,  '''README.md'''   )
				assert os.path.exists(snake_case__   )
				lowercase_	    =   DatasetInfosDict.from_directory(snake_case__   )
				lowercase_	    =   DatasetInfosDict(
				    {
				        '''default''': DatasetInfo(
				            features=Features(
				                {
				                    '''tokens''': Sequence(Value('''string'''   )   ),
				                    '''ner_tags''': Sequence(
				                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']   )   ),
				                    '''langs''': Sequence(Value('''string'''   )   ),
				                    '''spans''': Sequence(Value('''string'''   )   ),
				                }   )		,  splits=[
				                {
				                    '''name''': '''train''',
				                    '''num_bytes''': 2_351_563,
				                    '''num_examples''': 10_000,
				                },
				                {
				                    '''name''': '''validation''',
				                    '''num_bytes''': 238_418,
				                    '''num_examples''': 1_000,
				                },
				            ]		,  download_size=3_940_680		,  dataset_size=2_589_981		,  )
				    }   )
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								lowercase_    ,      lowercase_	    =   getattr(dataset_infos['''default''']		,  snake_case__   ), getattr(expected_dataset_infos['''default''']		,  snake_case__   )
								if key == "num_bytes":
												assert is_apercent_close(snake_case__		,  snake_case__   )
								elif key == "splits":
												assert list(snake_case__   ) == list(snake_case__   )
												for split in result:
																assert result[split].name == expected[split].name
																assert result[split].num_examples == expected[split].num_examples
																assert is_apercent_close(result[split].num_bytes		,  expected[split].num_bytes   )
								else:
												result == expected




