






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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

def 	__lowerCamelCase      (		snake_case__	,snake_case__=False		)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  []
						for i in range(config.num_hidden_layers		):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F'blocks.{i}.norm1.weight', F'deit.encoder.layer.{i}.layernorm_before.weight')		)
												rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias')		)
												rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight')		)
												rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias')		)
												rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight')		)
												rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias')		)
												rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight')		)
												rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias')		)
												rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight')		)
												rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias')		)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ("""cls_token""", """deit.embeddings.cls_token"""),
						        ("""dist_token""", """deit.embeddings.distillation_token"""),
						        ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
						        ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
						        ("""pos_embed""", """deit.embeddings.position_embeddings"""),
						    ]		)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ("""norm.weight""", """layernorm.weight"""),
												        ("""norm.bias""", """layernorm.bias"""),
												        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
												        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
												    ]		)

												# if just the base model, we should remove "deit" from all keys that start with "deit"
												_SCREAMING_SNAKE_CASE     				=  [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
						else:
												# layernorm + classification heads
												rename_keys.extend(
												    [
												        ("""norm.weight""", """deit.layernorm.weight"""),
												        ("""norm.bias""", """deit.layernorm.bias"""),
												        ("""head.weight""", """cls_classifier.weight"""),
												        ("""head.bias""", """cls_classifier.bias"""),
												        ("""head_dist.weight""", """distillation_classifier.weight"""),
												        ("""head_dist.bias""", """distillation_classifier.bias"""),
												    ]		)

						return rename_keys

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__=False		)							->  Tuple:






						"""simple docstring"""





						for i in range(config.num_hidden_layers		):
												if base_model:
																		_SCREAMING_SNAKE_CASE     				=  """"""
												else:
																		_SCREAMING_SNAKE_CASE     				=  """deit."""
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'blocks.{i}.attn.qkv.weight'		)
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'blocks.{i}.attn.qkv.bias'		)
												# next, add query, keys and values (in that order) to the state dict
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[
												    : config.hidden_size, :
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[: config.hidden_size]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[
												    -config.hidden_size :, :
												]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[-config.hidden_size :]

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  dct.pop(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  val

def 	__lowerCamelCase      (		)							->  int:






						"""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 im

@torch.no_grad()
def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  DeiTConfig()
						# all deit models have fine-tuned heads
						_SCREAMING_SNAKE_CASE     				=  False
						# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
						_SCREAMING_SNAKE_CASE     				=  10_00
						_SCREAMING_SNAKE_CASE     				=  """huggingface/label-files"""
						_SCREAMING_SNAKE_CASE     				=  """imagenet-1k-id2label.json"""
						_SCREAMING_SNAKE_CASE     				=  json.load(open(hf_hub_download(snake_case__	,snake_case__	,repo_type="""dataset"""		)	,"""r"""		)		)
						_SCREAMING_SNAKE_CASE     				=  {int(snake_case__		): v for k, v in idalabel.items()}
						_SCREAMING_SNAKE_CASE     				=  idalabel
						_SCREAMING_SNAKE_CASE     				=  {v: k for k, v in idalabel.items()}
						_SCREAMING_SNAKE_CASE     				=  int(deit_name[-6:-4]		)
						_SCREAMING_SNAKE_CASE     				=  int(deit_name[-3:]		)
						# size of the architecture
						if deit_name[9:].startswith("""tiny"""		):
												_SCREAMING_SNAKE_CASE     				=  1_92
												_SCREAMING_SNAKE_CASE     				=  7_68
												_SCREAMING_SNAKE_CASE     				=  12
												_SCREAMING_SNAKE_CASE     				=  3
						elif deit_name[9:].startswith("""small"""		):
												_SCREAMING_SNAKE_CASE     				=  3_84
												_SCREAMING_SNAKE_CASE     				=  15_36
												_SCREAMING_SNAKE_CASE     				=  12
												_SCREAMING_SNAKE_CASE     				=  6
						if deit_name[9:].startswith("""base"""		):
												pass
						elif deit_name[4:].startswith("""large"""		):
												_SCREAMING_SNAKE_CASE     				=  10_24
												_SCREAMING_SNAKE_CASE     				=  40_96
												_SCREAMING_SNAKE_CASE     				=  24
												_SCREAMING_SNAKE_CASE     				=  16

						# load original model from timm
						_SCREAMING_SNAKE_CASE     				=  timm.create_model(snake_case__	,pretrained=snake_case__		)
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						_SCREAMING_SNAKE_CASE     				=  timm_model.state_dict()
						_SCREAMING_SNAKE_CASE     				=  create_rename_keys(snake_case__	,snake_case__		)
						for src, dest in rename_keys:
												rename_key(snake_case__	,snake_case__	,snake_case__		)
						read_in_q_k_v(snake_case__	,snake_case__	,snake_case__		)

						# load HuggingFace model
						_SCREAMING_SNAKE_CASE     				=  DeiTForImageClassificationWithTeacher(snake_case__		).eval()
						model.load_state_dict(snake_case__		)

						# Check outputs on an image, prepared by DeiTImageProcessor
						_SCREAMING_SNAKE_CASE     				=  int(
						    (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
						_SCREAMING_SNAKE_CASE     				=  DeiTImageProcessor(size=snake_case__	,crop_size=config.image_size		)
						_SCREAMING_SNAKE_CASE     				=  image_processor(images=prepare_img()	,return_tensors="""pt"""		)
						_SCREAMING_SNAKE_CASE     				=  encoding["""pixel_values"""]
						_SCREAMING_SNAKE_CASE     				=  model(snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  timm_model(snake_case__		)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(snake_case__	,outputs.logits	,atol=1e-3		)

						Path(snake_case__		).mkdir(exist_ok=snake_case__		)
						print(F'Saving model {deit_name} to {pytorch_dump_folder_path}'		)
						model.save_pretrained(snake_case__		)
						print(F'Saving image processor to {pytorch_dump_folder_path}'		)
						image_processor.save_pretrained(snake_case__		)


if __name__ == "__main__":
		UpperCamelCase			    =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--deit_name''',
		    default='''vit_deit_base_distilled_patch16_224''',
		    type=str,
		    help='''Name of the DeiT timm 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.'''
		)

		UpperCamelCase			    =     parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









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)

