






"""simple docstring"""



import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case       :      Tuple       =					logging.get_logger(__name__)




def 						lowerCamelCase_				(  __A  :		Tuple			):
					lowerCamelCase__				=ConvNextConfig()

					if "tiny" in checkpoint_url:
										lowerCamelCase__				=[3, 3, 9, 3]
										lowerCamelCase__				=[96, 192, 384, 768]
					if "small" in checkpoint_url:
										lowerCamelCase__				=[3, 3, 27, 3]
										lowerCamelCase__				=[96, 192, 384, 768]
					if "base" in checkpoint_url:
										lowerCamelCase__				=[3, 3, 27, 3]
										lowerCamelCase__				=[128, 256, 512, 1024]
					if "large" in checkpoint_url:
										lowerCamelCase__				=[3, 3, 27, 3]
										lowerCamelCase__				=[192, 384, 768, 1536]
					if "xlarge" in checkpoint_url:
										lowerCamelCase__				=[3, 3, 27, 3]
										lowerCamelCase__				=[256, 512, 1024, 2048]

					if "1k" in checkpoint_url:
										lowerCamelCase__				=1000
										lowerCamelCase__				='imagenet-1k-id2label.json'
										lowerCamelCase__				=(1, 1000)
					else:
										lowerCamelCase__				=2_1841
										lowerCamelCase__				='imagenet-22k-id2label.json'
										lowerCamelCase__				=(1, 2_1841)

					lowerCamelCase__				='huggingface/label-files'
					lowerCamelCase__				=num_labels
					lowerCamelCase__				=json.load(open(hf_hub_download(__A			,	__A			,	repo_type='dataset'			)			,	'r'			)			)
					lowerCamelCase__				={int(__A			): v for k, v in idalabel.items()}
					if "1k" not in checkpoint_url:
										# this dataset contains 21843 labels but the model only has 21841
										# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
										del idalabel[9205]
										del idalabel[1_5027]
					lowerCamelCase__				=idalabel
					lowerCamelCase__				={v: k for k, v in idalabel.items()}
					lowerCamelCase__				=hidden_sizes
					lowerCamelCase__				=depths

					return config, expected_shape




def 						lowerCamelCase_				(  __A  :		List[Any]			):
					if "downsample_layers.0.0" in name:
										lowerCamelCase__				=name.replace('downsample_layers.0.0'			,	'embeddings.patch_embeddings'			)
					if "downsample_layers.0.1" in name:
										lowerCamelCase__				=name.replace('downsample_layers.0.1'			,	'embeddings.norm'			)  # we rename to layernorm later on
					if "downsample_layers.1.0" in name:
										lowerCamelCase__				=name.replace('downsample_layers.1.0'			,	'stages.1.downsampling_layer.0'			)
					if "downsample_layers.1.1" in name:
										lowerCamelCase__				=name.replace('downsample_layers.1.1'			,	'stages.1.downsampling_layer.1'			)
					if "downsample_layers.2.0" in name:
										lowerCamelCase__				=name.replace('downsample_layers.2.0'			,	'stages.2.downsampling_layer.0'			)
					if "downsample_layers.2.1" in name:
										lowerCamelCase__				=name.replace('downsample_layers.2.1'			,	'stages.2.downsampling_layer.1'			)
					if "downsample_layers.3.0" in name:
										lowerCamelCase__				=name.replace('downsample_layers.3.0'			,	'stages.3.downsampling_layer.0'			)
					if "downsample_layers.3.1" in name:
										lowerCamelCase__				=name.replace('downsample_layers.3.1'			,	'stages.3.downsampling_layer.1'			)
					if "stages" in name and "downsampling_layer" not in name:
										# stages.0.0. for instance should be renamed to stages.0.layers.0.
										lowerCamelCase__				=name[: len('stages.0'			)] + '.layers' + name[len('stages.0'			) :]
					if "stages" in name:
										lowerCamelCase__				=name.replace('stages'			,	'encoder.stages'			)
					if "norm" in name:
										lowerCamelCase__				=name.replace('norm'			,	'layernorm'			)
					if "gamma" in name:
										lowerCamelCase__				=name.replace('gamma'			,	'layer_scale_parameter'			)
					if "head" in name:
										lowerCamelCase__				=name.replace('head'			,	'classifier'			)

					return name




def 						lowerCamelCase_				(  ):
					lowerCamelCase__				='http://images.cocodataset.org/val2017/000000039769.jpg'
					lowerCamelCase__				=Image.open(requests.get(__A			,	stream=__A			).raw			)
					return im




@torch.no_grad()
def 						lowerCamelCase_				(  __A  :		Any			,	__A  :		List[str]			):
					lowerCamelCase__				,     lowerCamelCase__				=get_convnext_config(__A			)
					# load original state_dict from URL
					lowerCamelCase__				=torch.hub.load_state_dict_from_url(__A			)['model']
					# rename keys
					for key in state_dict.copy().keys():
										lowerCamelCase__				=state_dict.pop(__A			)
										lowerCamelCase__				=val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										lowerCamelCase__				=state_dict.pop(__A			)
										if not key.startswith('classifier'			):
															lowerCamelCase__				='convnext.' + key
										lowerCamelCase__				=val

					# load HuggingFace model
					lowerCamelCase__				=ConvNextForImageClassification(__A			)
					model.load_state_dict(__A			)
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					lowerCamelCase__				=224 if '224' in checkpoint_url else 384
					lowerCamelCase__				=ConvNextImageProcessor(size=__A			)
					lowerCamelCase__				=image_processor(images=prepare_img()			,	return_tensors='pt'			).pixel_values

					lowerCamelCase__				=model(__A			).logits

					# note: the logits below were obtained without center cropping
					if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
										lowerCamelCase__				=torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
										lowerCamelCase__				=torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
										lowerCamelCase__				=torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
										lowerCamelCase__				=torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
										lowerCamelCase__				=torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
										lowerCamelCase__				=torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
										lowerCamelCase__				=torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
										lowerCamelCase__				=torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
										lowerCamelCase__				=torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
										lowerCamelCase__				=torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
										lowerCamelCase__				=torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
										lowerCamelCase__				=torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
										lowerCamelCase__				=torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
										lowerCamelCase__				=torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]			)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
										lowerCamelCase__				=torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]			)
					else:
										raise ValueError(f"Unknown URL: {checkpoint_url}"			)

					assert torch.allclose(logits[0, :3]			,	__A			,	atol=1E-3			)
					assert logits.shape == expected_shape

					Path(__A			).mkdir(exist_ok=__A			)
					print(f"Saving model to {pytorch_dump_folder_path}"			)
					model.save_pretrained(__A			)
					print(f"Saving image processor to {pytorch_dump_folder_path}"			)
					image_processor.save_pretrained(__A			)

					print('Pushing model to the hub...'			)
					lowerCamelCase__				='convnext'
					if "tiny" in checkpoint_url:
										model_name += "-tiny"
					elif "small" in checkpoint_url:
										model_name += "-small"
					elif "base" in checkpoint_url:
										model_name += "-base"
					elif "xlarge" in checkpoint_url:
										model_name += "-xlarge"
					elif "large" in checkpoint_url:
										model_name += "-large"
					if "224" in checkpoint_url:
										model_name += "-224"
					elif "384" in checkpoint_url:
										model_name += "-384"
					if "22k" in checkpoint_url and "1k" not in checkpoint_url:
										model_name += "-22k"
					if "22k" in checkpoint_url and "1k" in checkpoint_url:
										model_name += "-22k-1k"

					model.push_to_hub(
					    repo_path_or_name=Path(__A			,	__A			)			,	organization='nielsr'			,	commit_message='Add model'			,	)


if __name__ == "__main__":
						_snake_case       :      List[Any]       =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
						    type=str,
						    help="URL of the original ConvNeXT checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the output PyTorch model directory.",
						)

						_snake_case       :      int       =					parser.parse_args()
						convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""



import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_snake_case       :      Optional[Any]       =					"pcuenq/k-upscaler"




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		Optional[int]			,	*, __A  :		int			,	__A  :		Union[str, Any]			):
					lowerCamelCase__				={
					    # norm1
					    f"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[f"{resnet_prefix}.main.0.mapper.weight"],
					    f"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[f"{resnet_prefix}.main.0.mapper.bias"],
					    # conv1
					    f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.main.2.weight"],
					    f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.main.2.bias"],
					    # norm2
					    f"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[f"{resnet_prefix}.main.4.mapper.weight"],
					    f"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[f"{resnet_prefix}.main.4.mapper.bias"],
					    # conv2
					    f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.main.6.weight"],
					    f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.main.6.bias"],
					}

					if resnet.conv_shortcut is not None:
										rv.update(
										    {
										        f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.skip.weight"],
										    }			)

					return rv




def 						lowerCamelCase_				(  __A  :		Dict			,	*, __A  :		Union[str, Any]			,	__A  :		Union[str, Any]			):
					lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3			,	dim=0			)
					lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3			,	dim=0			)
					lowerCamelCase__				={
					    # norm
					    f"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[f"{attention_prefix}.norm_in.mapper.weight"],
					    f"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[f"{attention_prefix}.norm_in.mapper.bias"],
					    # to_q
					    f"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q,
					    # to_k
					    f"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k,
					    # to_v
					    f"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v,
					    # to_out
					    f"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[f"{attention_prefix}.out_proj.weight"]
					    .squeeze(-1			)
					    .squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[f"{attention_prefix}.out_proj.bias"],
					}

					return rv




def 						lowerCamelCase_				(  __A  :		List[str]			,	*, __A  :		Optional[Any]			,	__A  :		List[str]			,	__A  :		Optional[int]			):
					lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2			,	dim=0			)
					lowerCamelCase__				,     lowerCamelCase__				=checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2			,	dim=0			)

					lowerCamelCase__				={
					    # norm2 (ada groupnorm)
					    f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[
					        f"{attention_prefix}.norm_dec.mapper.weight"
					    ],
					    f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[
					        f"{attention_prefix}.norm_dec.mapper.bias"
					    ],
					    # layernorm on encoder_hidden_state
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[
					        f"{attention_prefix}.norm_enc.weight"
					    ],
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[
					        f"{attention_prefix}.norm_enc.bias"
					    ],
					    # to_q
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[
					        f"{attention_prefix}.q_proj.weight"
					    ]
					    .squeeze(-1			)
					    .squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[
					        f"{attention_prefix}.q_proj.bias"
					    ],
					    # to_k
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k,
					    # to_v
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1			).squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v,
					    # to_out
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[
					        f"{attention_prefix}.out_proj.weight"
					    ]
					    .squeeze(-1			)
					    .squeeze(-1			),
					    f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[
					        f"{attention_prefix}.out_proj.bias"
					    ],
					}

					return rv




def 						lowerCamelCase_				(  __A  :		Tuple			,	__A  :		Union[str, Any]			,	__A  :		List[str]			,	__A  :		List[str]			):
					lowerCamelCase__				='inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
					lowerCamelCase__				=f"{block_prefix}.{block_idx}"

					lowerCamelCase__				={}

					if not hasattr(__A			,	'attentions'			):
										lowerCamelCase__				=1  # resnet only
					elif not block.attentions[0].add_self_attention:
										lowerCamelCase__				=2  # resnet -> cross-attention
					else:
										lowerCamelCase__				=3  # resnet -> self-attention -> cross-attention)

					for resnet_idx, resnet in enumerate(block.resnets			):
										# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
										lowerCamelCase__				=f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
										lowerCamelCase__				=n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
										lowerCamelCase__				=f"{block_prefix}.{idx}" if block_type == 'up' else f"{block_prefix}.{idx}"

										diffusers_checkpoint.update(
										    resnet_to_diffusers_checkpoint(
										        __A			,	__A			,	diffusers_resnet_prefix=__A			,	resnet_prefix=__A			)			)

					if hasattr(__A			,	'attentions'			):
										for attention_idx, attention in enumerate(block.attentions			):
															lowerCamelCase__				=f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
															lowerCamelCase__				=n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
															lowerCamelCase__				=f"{block_prefix}.{idx}"
															lowerCamelCase__				=f"{block_prefix}.{idx }"
															lowerCamelCase__				=1 if not attention.add_self_attention else 2
															lowerCamelCase__				=(
															    n * attention_idx + cross_attention_index
															    if block_type == 'up'
															    else n * attention_idx + cross_attention_index + 1
															)
															lowerCamelCase__				=f"{block_prefix}.{idx }"

															diffusers_checkpoint.update(
															    cross_attn_to_diffusers_checkpoint(
															        __A			,	diffusers_attention_prefix=__A			,	diffusers_attention_index=2			,	attention_prefix=__A			,	)			)

															if attention.add_self_attention is True:
																				diffusers_checkpoint.update(
																				    self_attn_to_diffusers_checkpoint(
																				        __A			,	diffusers_attention_prefix=__A			,	attention_prefix=__A			,	)			)

					return diffusers_checkpoint




def 						lowerCamelCase_				(  __A  :		str			,	__A  :		Dict			):
					lowerCamelCase__				={}

					# pre-processing
					diffusers_checkpoint.update(
					    {
					        'conv_in.weight': checkpoint['inner_model.proj_in.weight'],
					        'conv_in.bias': checkpoint['inner_model.proj_in.bias'],
					    }			)

					# timestep and class embedding
					diffusers_checkpoint.update(
					    {
					        'time_proj.weight': checkpoint['inner_model.timestep_embed.weight'].squeeze(-1			),
					        'time_embedding.linear_1.weight': checkpoint['inner_model.mapping.0.weight'],
					        'time_embedding.linear_1.bias': checkpoint['inner_model.mapping.0.bias'],
					        'time_embedding.linear_2.weight': checkpoint['inner_model.mapping.2.weight'],
					        'time_embedding.linear_2.bias': checkpoint['inner_model.mapping.2.bias'],
					        'time_embedding.cond_proj.weight': checkpoint['inner_model.mapping_cond.weight'],
					    }			)

					# down_blocks
					for down_block_idx, down_block in enumerate(model.down_blocks			):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(__A			,	__A			,	__A			,	'down'			)			)

					# up_blocks
					for up_block_idx, up_block in enumerate(model.up_blocks			):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(__A			,	__A			,	__A			,	'up'			)			)

					# post-processing
					diffusers_checkpoint.update(
					    {
					        'conv_out.weight': checkpoint['inner_model.proj_out.weight'],
					        'conv_out.bias': checkpoint['inner_model.proj_out.bias'],
					    }			)

					return diffusers_checkpoint




def 						lowerCamelCase_				(  __A  :		Optional[int]			):
					lowerCamelCase__				=original_config['input_channels'] + original_config['unet_cond_dim']
					lowerCamelCase__				=original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

					lowerCamelCase__				=original_config['channels']

					assert (
					    len(set(original_config['depths']			)			) == 1
					), "UNet2DConditionModel currently do not support blocks with different number of layers"
					lowerCamelCase__				=original_config['depths'][0]

					lowerCamelCase__				=original_config['mapping_cond_dim']
					lowerCamelCase__				=original_config['cross_cond_dim']

					lowerCamelCase__				=[]
					lowerCamelCase__				=[]
					for s, c in zip(original_config['self_attn_depths']			,	original_config['cross_attn_depths']			):
										if s:
															lowerCamelCase__				='self'
															lowerCamelCase__				='cross' if c else None
										elif c:
															lowerCamelCase__				='cross'
															lowerCamelCase__				=None
										else:
															lowerCamelCase__				=None
															lowerCamelCase__				=None
										attna_types.append(__A			)
										attna_types.append(__A			)

					lowerCamelCase__				=UNetaDConditionModel(
					    in_channels=__A			,	out_channels=__A			,	down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')			,	mid_block_type=__A			,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')			,	block_out_channels=__A			,	layers_per_block=__A			,	act_fn='gelu'			,	norm_num_groups=__A			,	cross_attention_dim=__A			,	attention_head_dim=64			,	time_cond_proj_dim=__A			,	resnet_time_scale_shift='scale_shift'			,	time_embedding_type='fourier'			,	timestep_post_act='gelu'			,	conv_in_kernel=1			,	conv_out_kernel=1			,	)

					return unet




def 						lowerCamelCase_				(  __A  :		int			):
					lowerCamelCase__				=torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

					lowerCamelCase__				=huggingface_hub.hf_hub_download(__A			,	'config_laion_text_cond_latent_upscaler_2.json'			)
					lowerCamelCase__				=huggingface_hub.hf_hub_download(
					    __A			,	'laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'			)
					print(f"loading original model configuration from {orig_config_path}"			)
					print(f"loading original model checkpoint from {orig_weights_path}"			)

					print('converting to diffusers unet'			)
					lowerCamelCase__				=K.config.load_config(open(__A			)			)['model']
					lowerCamelCase__				=unet_model_from_original_config(__A			)

					lowerCamelCase__				=torch.load(__A			,	map_location=__A			)['model_ema']
					lowerCamelCase__				=unet_to_diffusers_checkpoint(__A			,	__A			)

					model.load_state_dict(__A			,	strict=__A			)
					model.save_pretrained(args.dump_path			)
					print(f"saving converted unet model in {args.dump_path}"			)


if __name__ == "__main__":
						_snake_case       :      Optional[int]       =					argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
						_snake_case       :      List[Any]       =					parser.parse_args()

						main(args)