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

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

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

			return config




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					str              =						[]

			# 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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Union[str, Any]              =						dct.pop(_lowerCamelCase    )
			A							:					List[str]              =						val




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					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",
			}
			A							:					Optional[Any]              =						model_name_to_url[model_name]
			A							:					Dict              =						torch.hub.load_state_dict_from_url(_lowerCamelCase ,    map_location="cpu"    )["state_dict"]

			A							:					Optional[Any]              =						get_upernet_config(_lowerCamelCase    )
			A							:					Dict              =						UperNetForSemanticSegmentation(_lowerCamelCase    )
			model.eval()

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

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

			model.load_state_dict(_lowerCamelCase    )

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

			A							:					List[Any]              =						SegformerImageProcessor()
			A							:					Optional[int]              =						processor(_lowerCamelCase ,    return_tensors="pt"    ).pixel_values

			with torch.no_grad():
						A							:					int              =						model(_lowerCamelCase    )

			if model_name == "upernet-convnext-tiny":
						A							:					Union[str, Any]              =						torch.tensor(
						    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]    )
			elif model_name == "upernet-convnext-small":
						A							:					Optional[int]              =						torch.tensor(
						    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]    )
			elif model_name == "upernet-convnext-base":
						A							:					str              =						torch.tensor(
						    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]    )
			elif model_name == "upernet-convnext-large":
						A							:					int              =						torch.tensor(
						    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]    )
			elif model_name == "upernet-convnext-xlarge":
						A							:					Any              =						torch.tensor(
						    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]    )
			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__":
							__SCREAMING_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."""
							)

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




import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE           =  """."""


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
							__SCREAMING_SNAKE_CASE           =  []
							__SCREAMING_SNAKE_CASE           =  []
							with open(doctest_file_path) as fp:
														for line in fp:
																					__SCREAMING_SNAKE_CASE           =  line.strip()
																					__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														__SCREAMING_SNAKE_CASE           =  """\n""".join(non_existent_paths)
														raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
							if all_paths != sorted(all_paths):
														raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")