



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						(      _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	List[str]							  =	3_8_4
							if "tiny" in model_name:
														UpperCAmelCase  :	Any							  =	[3, 3, 9, 3]
														UpperCAmelCase  :	Union[str, Any]							  =	[9_6, 1_9_2, 3_8_4, 7_6_8]
							if "small" in model_name:
														UpperCAmelCase  :	Union[str, Any]							  =	[3, 3, 2_7, 3]
														UpperCAmelCase  :	str							  =	[9_6, 1_9_2, 3_8_4, 7_6_8]
							if "base" in model_name:
														UpperCAmelCase  :	Union[str, Any]							  =	[3, 3, 2_7, 3]
														UpperCAmelCase  :	List[str]							  =	[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
														UpperCAmelCase  :	List[str]							  =	5_1_2
							if "large" in model_name:
														UpperCAmelCase  :	Tuple							  =	[3, 3, 2_7, 3]
														UpperCAmelCase  :	Optional[Any]							  =	[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
														UpperCAmelCase  :	Union[str, Any]							  =	7_6_8
							if "xlarge" in model_name:
														UpperCAmelCase  :	Dict							  =	[3, 3, 2_7, 3]
														UpperCAmelCase  :	Optional[int]							  =	[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
														UpperCAmelCase  :	List[Any]							  =	1_0_2_4

							# set label information
							UpperCAmelCase  :	Tuple							  =	1_5_0
							UpperCAmelCase  :	Optional[Any]							  =	"""huggingface/label-files"""
							UpperCAmelCase  :	List[Any]							  =	"""ade20k-id2label.json"""
							UpperCAmelCase  :	Union[str, Any]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
							UpperCAmelCase  :	Optional[Any]							  =	{int(_lowercase	): v for k, v in idalabel.items()}
							UpperCAmelCase  :	int							  =	{v: k for k, v in idalabel.items()}

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

							return config



def    __lowerCamelCase						(      _lowercase	)   ->    Dict:
							UpperCAmelCase  :	Dict							  =	[]

							# 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						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Dict:
							UpperCAmelCase  :	Optional[Any]							  =	dct.pop(_lowercase	)
							UpperCAmelCase  :	List[Any]							  =	val



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	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""",
							}
							UpperCAmelCase  :	Optional[Any]							  =	model_name_to_url[model_name]
							UpperCAmelCase  :	int							  =	torch.hub.load_state_dict_from_url(_lowercase			,       map_location="""cpu"""	)["""state_dict"""]

							UpperCAmelCase  :	int							  =	get_upernet_config(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	UperNetForSemanticSegmentation(_lowercase	)
							model.eval()

							# replace "bn" => "batch_norm"
							for key in state_dict.copy().keys():
														UpperCAmelCase  :	Union[str, Any]							  =	state_dict.pop(_lowercase	)
														if "bn" in key:
																					UpperCAmelCase  :	List[Any]							  =	key.replace("""bn"""			,       """batch_norm"""	)
														UpperCAmelCase  :	str							  =	val

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

							model.load_state_dict(_lowercase	)

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

							UpperCAmelCase  :	int							  =	SegformerImageProcessor()
							UpperCAmelCase  :	Any							  =	processor(_lowercase			,       return_tensors="""pt"""	).pixel_values

							with torch.no_grad():
														UpperCAmelCase  :	List[Any]							  =	model(_lowercase	)

							if model_name == "upernet-convnext-tiny":
														UpperCAmelCase  :	Tuple							  =	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":
														UpperCAmelCase  :	List[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":
														UpperCAmelCase  :	Union[str, Any]							  =	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":
														UpperCAmelCase  :	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":
														UpperCAmelCase  :	List[Any]							  =	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]			,       _lowercase			,       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(_lowercase	)
														print(F'''Saving processor to {pytorch_dump_folder_path}'''	)
														processor.save_pretrained(_lowercase	)

							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__":
							a     :    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."""
							)

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






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

