



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 )	->						Optional[int]:
				a__	:							Any      						= 3_84
				if "tiny" in model_name:
								a__	:							Dict      						= [3, 3, 9, 3]
								a__	:							Union[str, Any]      						= [96, 1_92, 3_84, 7_68]
				if "small" in model_name:
								a__	:							Dict      						= [3, 3, 27, 3]
								a__	:							str      						= [96, 1_92, 3_84, 7_68]
				if "base" in model_name:
								a__	:							Union[str, Any]      						= [3, 3, 27, 3]
								a__	:							Any      						= [1_28, 2_56, 5_12, 10_24]
								a__	:							str      						= 5_12
				if "large" in model_name:
								a__	:							List[Any]      						= [3, 3, 27, 3]
								a__	:							Dict      						= [1_92, 3_84, 7_68, 15_36]
								a__	:							Optional[Any]      						= 7_68
				if "xlarge" in model_name:
								a__	:							Optional[Any]      						= [3, 3, 27, 3]
								a__	:							List[Any]      						= [2_56, 5_12, 10_24, 20_48]
								a__	:							Union[str, Any]      						= 10_24

				# set label information
				a__	:							Dict      						= 1_50
				a__	:							Any      						= "huggingface/label-files"
				a__	:							str      						= "ade20k-id2label.json"
				a__	:							Union[str, Any]      						= json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type="dataset" )	,				"r" ) )
				a__	:							str      						= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				a__	:							Any      						= {v: k for k, v in idalabel.items()}

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

				return config






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Optional[int]:
				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     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						int:
				a__	:							List[Any]      						= dct.pop(__UpperCamelCase )
				a__	:							Dict      						= val






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[str]:
				a__	:							List[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__	:							int      						= model_name_to_url[model_name]
				a__	:							List[Any]      						= torch.hub.load_state_dict_from_url(__UpperCamelCase	,				map_location="cpu" )["state_dict"]

				a__	:							str      						= get_upernet_config(__UpperCamelCase )
				a__	:							Dict      						= UperNetForSemanticSegmentation(__UpperCamelCase )
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								a__	:							str      						= state_dict.pop(__UpperCamelCase )
								if "bn" in key:
												a__	:							str      						= key.replace("bn"	,				"batch_norm" )
								a__	:							Dict      						= val

				# rename keys
				a__	:							Tuple      						= create_rename_keys(__UpperCamelCase )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )

				model.load_state_dict(__UpperCamelCase )

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

				a__	:							List[str]      						= SegformerImageProcessor()
				a__	:							Any      						= processor(__UpperCamelCase	,				return_tensors="pt" ).pixel_values

				with torch.no_grad():
								a__	:							Dict      						= model(__UpperCamelCase )

				if model_name == "upernet-convnext-tiny":
								a__	:							Tuple      						= 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":
								a__	:							Optional[Any]      						= 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":
								a__	:							Tuple      						= 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":
								a__	:							Optional[Any]      						= 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":
								a__	:							Tuple      						= 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 unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							str      						= UNetaDModel(
											    block_out_channels=(32, 64) ,					layers_per_block=2 ,					sample_size=32 ,					in_channels=3 ,					out_channels=3 ,					down_block_types=("DownBlock2D", "AttnDownBlock2D") ,					up_block_types=("AttnUpBlock2D", "UpBlock2D") ,					)
											return model




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.dummy_uncond_unet
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							Union[str, Any]      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)

											a__	:							Tuple      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy"				).images

											a__	:							Optional[int]      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy" ,					return_dict=__UpperCAmelCase				)[0]

											a__	:							str      						= image[0, -3:, -3:, -1]
											a__	:							int      						= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							int      						= np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

											assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2





@slow
@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= "google/ddpm-cifar10-32"

											a__	:							List[Any]      						= UNetaDModel.from_pretrained(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							int      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= torch.manual_seed(0				)
											a__	:							Union[str, Any]      						= pndm(generator=__UpperCAmelCase ,					output_type="numpy"				).images

											a__	:							List[Any]      						= image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							Dict      						= np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

											assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


