






"""simple docstring"""



from sklearn.metrics import accuracy_score

import datasets


_snake_case       :      Dict       =					"\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


_snake_case       :      int       =					"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


_snake_case       :      Tuple       =					"\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,							_KWARGS_DESCRIPTION  )
class        __lowercase(       datasets.Metric  ):






						def 		lowerCAmelCase   (					self)			->  Optional[Any]:




											"""simple docstring"""





											return datasets.MetricInfo(
											    description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
											        {
											            'predictions': datasets.Sequence(datasets.Value('int32')),
											            'references': datasets.Sequence(datasets.Value('int32')),
											        }
											        if self.config_name == 'multilabel'
											        else {
											            'predictions': datasets.Value('int32'),
											            'references': datasets.Value('int32'),
											        })    ,						reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']    ,						)






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=True    ,						_SCREAMING_SNAKE_CASE=None)			->  Dict:




											"""simple docstring"""





											return {
											    "accuracy": float(
											        accuracy_score(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						normalize=_SCREAMING_SNAKE_CASE    ,						sample_weight=_SCREAMING_SNAKE_CASE))
											}







"""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)