

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

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

	__A :			int		    =			ConvNextConfig(
	    depths=a__			,hidden_sizes=a__			,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
	__A :			Union[str, Any]		    =			UperNetConfig(
	    backbone_config=a__			,auxiliary_in_channels=a__			,num_labels=a__			,idalabel=a__			,labelaid=a__			,)

	return config
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				Dict:
	__A :			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 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Dict			,a__    :   List[Any]       )					->				int:
	__A :			Any		    =			dct.pop(a__       )
	__A :			Dict		    =			val
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   List[str]			,a__    :   Optional[int]       )					->				int:
	__A :			Optional[int]		    =			{
	    """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""",
	}
	__A :			Dict		    =			model_name_to_url[model_name]
	__A :			Optional[int]		    =			torch.hub.load_state_dict_from_url(a__			,map_location="""cpu"""       )["""state_dict"""]

	__A :			Dict		    =			get_upernet_config(a__       )
	__A :			Tuple		    =			UperNetForSemanticSegmentation(a__       )
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		__A :			Optional[Any]		    =			state_dict.pop(a__       )
		if "bn" in key:
			__A :			Dict		    =			key.replace("""bn"""			,"""batch_norm"""       )
		__A :			List[Any]		    =			val

	# rename keys
	__A :			Optional[int]		    =			create_rename_keys(a__       )
	for src, dest in rename_keys:
		rename_key(a__			,a__			,a__       )

	model.load_state_dict(a__       )

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

	__A :			Optional[int]		    =			SegformerImageProcessor()
	__A :			Union[str, Any]		    =			processor(a__			,return_tensors="""pt"""       ).pixel_values

	with torch.no_grad():
		__A :			List[str]		    =			model(a__       )

	if model_name == "upernet-convnext-tiny":
		__A :			int		    =			torch.tensor(
		    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]       )
	elif model_name == "upernet-convnext-small":
		__A :			Optional[int]		    =			torch.tensor(
		    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]       )
	elif model_name == "upernet-convnext-base":
		__A :			List[Any]		    =			torch.tensor(
		    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]       )
	elif model_name == "upernet-convnext-large":
		__A :			str		    =			torch.tensor(
		    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]       )
	elif model_name == "upernet-convnext-xlarge":
		__A :			Optional[Any]		    =			torch.tensor(
		    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]       )
	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_							:					List[str]		 =   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_							:					Tuple		 =   parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import fire

from utils import calculate_rouge, save_json
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Tuple			,a__    :   Any=None			,**a__    :   Dict       )					->				Optional[Any]:
	__A :			int		    =			[x.strip() for x in open(a__       ).readlines()]
	__A :			List[str]		    =			[x.strip() for x in open(a__       ).readlines()][: len(a__       )]
	__A :			List[Any]		    =			calculate_rouge(a__			,a__			,**a__       )
	if save_path is not None:
		save_json(a__			,a__			,indent=a__       )
	return metrics  # these print nicely


if __name__ == "__main__":
			fire.Fire(calculate_rouge_path)





