






"""simple docstring"""




import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase			    =     logging.get_logger(__name__)

def 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  OrderedDict()
						for key, value in state_dict.items():
												if key.startswith("""module.encoder"""		):
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""module.encoder"""	,"""glpn.encoder"""		)
												if key.startswith("""module.decoder"""		):
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""module.decoder"""	,"""decoder.stages"""		)
												if "patch_embed" in key:
																		# replace for example patch_embed1 by patch_embeddings.0
																		_SCREAMING_SNAKE_CASE     				=  key[key.find("""patch_embed"""		) + len("""patch_embed"""		)]
																		_SCREAMING_SNAKE_CASE     				=  key.replace(F'patch_embed{idx}'	,F'patch_embeddings.{int(snake_case__		)-1}'		)
												if "norm" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""norm"""	,"""layer_norm"""		)
												if "glpn.encoder.layer_norm" in key:
																		# replace for example layer_norm1 by layer_norm.0
																		_SCREAMING_SNAKE_CASE     				=  key[key.find("""glpn.encoder.layer_norm"""		) + len("""glpn.encoder.layer_norm"""		)]
																		_SCREAMING_SNAKE_CASE     				=  key.replace(F'layer_norm{idx}'	,F'layer_norm.{int(snake_case__		)-1}'		)
												if "layer_norm1" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""layer_norm1"""	,"""layer_norm_1"""		)
												if "layer_norm2" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""layer_norm2"""	,"""layer_norm_2"""		)
												if "block" in key:
																		# replace for example block1 by block.0
																		_SCREAMING_SNAKE_CASE     				=  key[key.find("""block"""		) + len("""block"""		)]
																		_SCREAMING_SNAKE_CASE     				=  key.replace(F'block{idx}'	,F'block.{int(snake_case__		)-1}'		)
												if "attn.q" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""attn.q"""	,"""attention.self.query"""		)
												if "attn.proj" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""attn.proj"""	,"""attention.output.dense"""		)
												if "attn" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""attn"""	,"""attention.self"""		)
												if "fc1" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""fc1"""	,"""dense1"""		)
												if "fc2" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""fc2"""	,"""dense2"""		)
												if "linear_pred" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""linear_pred"""	,"""classifier"""		)
												if "linear_fuse" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""linear_fuse.conv"""	,"""linear_fuse"""		)
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""linear_fuse.bn"""	,"""batch_norm"""		)
												if "linear_c" in key:
																		# replace for example linear_c4 by linear_c.3
																		_SCREAMING_SNAKE_CASE     				=  key[key.find("""linear_c"""		) + len("""linear_c"""		)]
																		_SCREAMING_SNAKE_CASE     				=  key.replace(F'linear_c{idx}'	,F'linear_c.{int(snake_case__		)-1}'		)
												if "bot_conv" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""bot_conv"""	,"""0.convolution"""		)
												if "skip_conv1" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""skip_conv1"""	,"""1.convolution"""		)
												if "skip_conv2" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""skip_conv2"""	,"""2.convolution"""		)
												if "fusion1" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""fusion1"""	,"""1.fusion"""		)
												if "fusion2" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""fusion2"""	,"""2.fusion"""		)
												if "fusion3" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""fusion3"""	,"""3.fusion"""		)
												if "fusion" in key and "conv" in key:
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""conv"""	,"""convolutional_layer"""		)
												if key.startswith("""module.last_layer_depth"""		):
																		_SCREAMING_SNAKE_CASE     				=  key.replace("""module.last_layer_depth"""	,"""head.head"""		)
												_SCREAMING_SNAKE_CASE     				=  value

						return new_state_dict

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Optional[int]:






						"""simple docstring"""





						for i in range(config.num_encoder_blocks		):
												for j in range(config.depths[i]		):
																		# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																		_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'		)
																		_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'		)
																		# next, add keys and values (in that order) to the state dict
																		_SCREAMING_SNAKE_CASE     				=  kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		_SCREAMING_SNAKE_CASE     				=  kv_bias[: config.hidden_sizes[i]]
																		_SCREAMING_SNAKE_CASE     				=  kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		_SCREAMING_SNAKE_CASE     				=  kv_bias[config.hidden_sizes[i] :]

def 	__lowerCamelCase      (		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						_SCREAMING_SNAKE_CASE     				=  Image.open(requests.get(snake_case__	,stream=snake_case__		).raw		)

						return image

@torch.no_grad()
def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__=False	,snake_case__=None		)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]	,decoder_hidden_size=64	,depths=[3, 8, 27, 3]		)

						# load image processor (only resize + rescale)
						_SCREAMING_SNAKE_CASE     				=  GLPNImageProcessor()

						# prepare image
						_SCREAMING_SNAKE_CASE     				=  prepare_img()
						_SCREAMING_SNAKE_CASE     				=  image_processor(images=snake_case__	,return_tensors="""pt"""		).pixel_values

						logger.info("""Converting model..."""		)

						# load original state dict
						_SCREAMING_SNAKE_CASE     				=  torch.load(snake_case__	,map_location=torch.device("""cpu"""		)		)

						# rename keys
						_SCREAMING_SNAKE_CASE     				=  rename_keys(snake_case__		)

						# key and value matrices need special treatment
						read_in_k_v(snake_case__	,snake_case__		)

						# create HuggingFace model and load state dict
						_SCREAMING_SNAKE_CASE     				=  GLPNForDepthEstimation(snake_case__		)
						model.load_state_dict(snake_case__		)
						model.eval()

						# forward pass
						_SCREAMING_SNAKE_CASE     				=  model(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		_SCREAMING_SNAKE_CASE     				=  torch.tensor(
																		    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]		)
												elif "kitti" in model_name:
																		_SCREAMING_SNAKE_CASE     				=  torch.tensor(
																		    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]		)
												else:
																		raise ValueError(F'Unknown model name: {model_name}'		)

												_SCREAMING_SNAKE_CASE     				=  torch.Size([1, 4_80, 6_40]		)

												assert predicted_depth.shape == expected_shape
												assert torch.allclose(predicted_depth[0, :3, :3]	,snake_case__	,atol=1e-4		)
												print("""Looks ok!"""		)

						# finally, push to hub if required
						if push_to_hub:
												logger.info("""Pushing model and image processor to the hub..."""		)
												model.push_to_hub(
												    repo_path_or_name=Path(snake_case__	,snake_case__		)	,organization="""nielsr"""	,commit_message="""Add model"""	,use_temp_dir=snake_case__	,)
												image_processor.push_to_hub(
												    repo_path_or_name=Path(snake_case__	,snake_case__		)	,organization="""nielsr"""	,commit_message="""Add image processor"""	,use_temp_dir=snake_case__	,)


if __name__ == "__main__":
		UpperCamelCase			    =     argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''',
		    default=None,
		    type=str,
		    help='''Path to the original PyTorch checkpoint (.pth file).''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
		)
		parser.add_argument(
		    '''--model_name''',
		    default='''glpn-kitti''',
		    type=str,
		    help='''Name of the model in case you\'re pushing to the hub.''',
		)
		UpperCamelCase			    =     parser.parse_args()
		convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase			    =     {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

UpperCamelCase			    =     {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

UpperCamelCase			    =     {
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

UpperCamelCase			    =     {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

UpperCamelCase			    =     {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

UpperCamelCase			    =     {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





						if isinstance(snake_case__	,snake_case__		):
												return v
						if v.lower() in ("yes", "true", "t", "y", "1"):
												return True
						elif v.lower() in ("no", "false", "f", "n", "0"):
												return False
						else:
												raise argparse.ArgumentTypeError("""boolean value expected"""		)

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=False		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.0.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.0.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.2.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.in_layers.2.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.emb_layers.1.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.emb_layers.1.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.0.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.0.bias']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.3.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.out_layers.3.bias']

						if has_skip:
												_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.skip_connection.weight']
												_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.skip_connection.bias']

						return new_checkpoint

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=None		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.qkv.weight'].chunk(3	,dim=0		)
						_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.qkv.bias'].chunk(3	,dim=0		)

						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.norm.weight']
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.norm.bias']

						_SCREAMING_SNAKE_CASE     				=  weight_q.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  bias_q.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  weight_k.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  bias_k.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  weight_v.squeeze(-1		).squeeze(-1		)
						_SCREAMING_SNAKE_CASE     				=  bias_v.squeeze(-1		).squeeze(-1		)

						_SCREAMING_SNAKE_CASE     				=  (
						    checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1		).squeeze(-1		)
						)
						_SCREAMING_SNAKE_CASE     				=  checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1		).squeeze(-1		)

						return new_checkpoint

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  torch.load(snake_case__	,map_location="""cpu"""		)
						_SCREAMING_SNAKE_CASE     				=  {}

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.0.bias"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.2.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""time_embed.2.bias"""]

						if unet_config["num_class_embeds"] is not None:
												_SCREAMING_SNAKE_CASE     				=  checkpoint["""label_emb.weight"""]

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""input_blocks.0.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""input_blocks.0.0.bias"""]

						_SCREAMING_SNAKE_CASE     				=  unet_config["""down_block_types"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""layers_per_block"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""attention_head_dim"""]
						_SCREAMING_SNAKE_CASE     				=  unet_config["""block_out_channels"""]
						_SCREAMING_SNAKE_CASE     				=  1
						_SCREAMING_SNAKE_CASE     				=  channels_list[0]

						for i, layer_type in enumerate(snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  channels_list[i]
												_SCREAMING_SNAKE_CASE     				=  current_channels != prev_channels
												if layer_type == "ResnetDownsampleBlock2D":
																		for j in range(snake_case__		):
																								_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  True if j == 0 and downsample_block_has_skip else False
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								current_layer += 1

												elif layer_type == "AttnDownBlock2D":
																		for j in range(snake_case__		):
																								_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  True if j == 0 and downsample_block_has_skip else False
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.attentions.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_attention(
																								    snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																								current_layer += 1

												if i != len(snake_case__		) - 1:
																		_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.downsamplers.0'
																		_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.0'
																		_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																		current_layer += 1

												_SCREAMING_SNAKE_CASE     				=  current_channels

						# hardcoded the mid-block for now
						_SCREAMING_SNAKE_CASE     				=  """mid_block.resnets.0"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.0"""
						_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.attentions.0"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.1"""
						_SCREAMING_SNAKE_CASE     				=  convert_attention(snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.resnets.1"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.2"""
						_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  0
						_SCREAMING_SNAKE_CASE     				=  unet_config["""up_block_types"""]

						for i, layer_type in enumerate(snake_case__		):
												if layer_type == "ResnetUpsampleBlock2D":
																		for j in range(layers_per_block + 1		):
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								current_layer += 1

																		if i != len(snake_case__		) - 1:
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.upsamplers.0'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer-1}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
												elif layer_type == "AttnUpBlock2D":
																		for j in range(layers_per_block + 1		):
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.resnets.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer}.0'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.attentions.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_attention(
																								    snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																								current_layer += 1

																		if i != len(snake_case__		) - 1:
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.upsamplers.0'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer-1}.2'
																								_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.0.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.0.bias"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.2.weight"""]
						_SCREAMING_SNAKE_CASE     				=  checkpoint["""out.2.bias"""]

						return new_checkpoint


if __name__ == "__main__":
		UpperCamelCase			    =     argparse.ArgumentParser()

		parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
		)
		parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

		UpperCamelCase			    =     parser.parse_args()
		UpperCamelCase			    =     strabool(args.class_cond)

		UpperCamelCase			    =     os.path.basename(args.unet_path)
		print(f"Checkpoint: {ckpt_name}")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				UpperCamelCase			    =     IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				UpperCamelCase			    =     LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				UpperCamelCase			    =     TEST_UNET_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		if not args.class_cond:
				UpperCamelCase			    =     None

		UpperCamelCase			    =     con_pt_to_diffuser(args.unet_path, unet_config)

		UpperCamelCase			    =     UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				UpperCamelCase			    =     CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				UpperCamelCase			    =     CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				UpperCamelCase			    =     CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		UpperCamelCase			    =     CMStochasticIterativeScheduler(**scheduler_config)

		UpperCamelCase			    =     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)

