



'''simple docstring'''
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__lowerCAmelCase							="pcuenq/k-upscaler"

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  *, _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







					a_									=   {
					    # 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 							a	(   _UpperCAmelCase					,  *, _UpperCAmelCase					,  _UpperCAmelCase )				->				str:






					"""simple docstring"""







					a_							,					a_							,					a_									=   checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3					,  dim=0 )
					a_							,					a_							,					a_									=   checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3					,  dim=0 )
					a_									=   {
					    # 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 							a	(   _UpperCAmelCase					,  *, _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Any:






					"""simple docstring"""







					a_							,					a_									=   checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2					,  dim=0 )
					a_							,					a_									=   checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2					,  dim=0 )

					a_									=   {
					    # 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 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







					a_									=   'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
					a_									=   F'''{block_prefix}.{block_idx}'''

					a_									=   {}

					if not hasattr(_UpperCAmelCase					,  'attentions' ):
										a_									=   1  # resnet only
					elif not block.attentions[0].add_self_attention:
										a_									=   2  # resnet -> cross-attention
					else:
										a_									=   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}"
										a_									=   F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
										a_									=   n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
										a_									=   F'''{block_prefix}.{idx}''' if block_type == 'up' else F'''{block_prefix}.{idx}'''

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

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

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

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

					return diffusers_checkpoint

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Tuple:






					"""simple docstring"""







					a_									=   {}

					# 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(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  'down' ) )

					# up_blocks
					for up_block_idx, up_block in enumerate(model.up_blocks ):
										diffusers_checkpoint.update(block_to_diffusers_checkpoint(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  '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 							a	(   _UpperCAmelCase )				->				Union[str, Any]:






					"""simple docstring"""







					a_									=   original_config['input_channels'] + original_config['unet_cond_dim']
					a_									=   original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

					a_									=   original_config['channels']

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

					a_									=   original_config['mapping_cond_dim']
					a_									=   original_config['cross_cond_dim']

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

					a_									=   UNetaDConditionModel(
					    in_channels=_UpperCAmelCase					,  out_channels=_UpperCAmelCase					,  down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')					,  mid_block_type=_UpperCAmelCase					,  up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,  block_out_channels=_UpperCAmelCase					,  layers_per_block=_UpperCAmelCase					,  act_fn='gelu'					,  norm_num_groups=_UpperCAmelCase					,  cross_attention_dim=_UpperCAmelCase					,  attention_head_dim=6_4					,  time_cond_proj_dim=_UpperCAmelCase					,  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 							a	(   _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







					a_									=   torch.device('cuda' if torch.cuda.is_available() else 'cpu' )

					a_									=   huggingface_hub.hf_hub_download(_UpperCAmelCase					,  'config_laion_text_cond_latent_upscaler_2.json' )
					a_									=   huggingface_hub.hf_hub_download(
					    _UpperCAmelCase					,  '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' )
					a_									=   K.config.load_config(open(_UpperCAmelCase ) )['model']
					a_									=   unet_model_from_original_config(_UpperCAmelCase )

					a_									=   torch.load(_UpperCAmelCase					,  map_location=_UpperCAmelCase )['model_ema']
					a_									=   unet_to_diffusers_checkpoint(_UpperCAmelCase					,  _UpperCAmelCase )

					model.load_state_dict(_UpperCAmelCase					,  strict=_UpperCAmelCase )
					model.save_pretrained(args.dump_path )
					print(F'''saving converted unet model in {args.dump_path}''' )


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

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
						__lowerCAmelCase							=parser.parse_args()

						main(args)









'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__=7   , UpperCAmelCase__=3   , UpperCAmelCase__=18   , UpperCAmelCase__=30   , UpperCAmelCase__=400   , UpperCAmelCase__=True   , UpperCAmelCase__=None   , UpperCAmelCase__=0.8_7_5   , UpperCAmelCase__=True   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , UpperCAmelCase__=[0.5, 0.5, 0.5]   , )      ->	Optional[int]:
										a_									=   size if size is not None else {'shortest_edge': 20}
										a_									=   parent
										a_									=   batch_size
										a_									=   num_channels
										a_									=   image_size
										a_									=   min_resolution
										a_									=   max_resolution
										a_									=   do_resize
										a_									=   size
										a_									=   crop_pct
										a_									=   do_normalize
										a_									=   image_mean
										a_									=   image_std




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}



@require_torch
@require_vision
class    _snake_case   (				snake_case     ,					unittest.TestCase					):

					"""simple docstring"""





					_UpperCamelCase		 =      ConvNextImageProcessor if is_vision_available() else None




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   ConvNextImageProcessingTester(self     )




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Any:
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_resize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'size'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'crop_pct'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'do_normalize'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_mean'     )     )
										self.assertTrue(hasattr(UpperCAmelCase__   , 'image_std'     )     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										a_									=   self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 20}     )

										a_									=   self.image_processing_class.from_dict(self.image_processor_dict   , size=42     )
										self.assertEqual(image_processor.size   , {'shortest_edge': 42}     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , Image.Image     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , numpify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , np.ndarray     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										# Initialize image_processing
										a_									=   self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										a_									=   prepare_image_inputs(self.image_processor_tester   , equal_resolution=UpperCAmelCase__   , torchify=UpperCAmelCase__     )
										for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase__   , torch.Tensor     )

										# Test not batched input
										a_									=   image_processing(image_inputs[0]   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

										# Test batched
										a_									=   image_processing(UpperCAmelCase__   , return_tensors='pt'     ).pixel_values
										self.assertEqual(
										    encoded_images.shape   , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )   , )

