





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCAmelCase						:       Union[str, Any]        =					'''pcuenq/k-upscaler'''







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						*, a ,						a  )				->				Dict:
					__A     :						Tuple          =    {
					    # 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 			_SCREAMING_SNAKE_CASE    (       a ,						*, a ,						a  )				->				Optional[int]:
					__A   ,    __A   ,    __A     :						Tuple          =    checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3 ,						dim=0  )
					__A   ,    __A   ,    __A     :						Dict          =    checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3 ,						dim=0  )
					__A     :						Any          =    {
					    # 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 			_SCREAMING_SNAKE_CASE    (       a ,						*, a ,						a ,						a  )				->				List[Any]:
					__A   ,    __A     :						Dict          =    checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2 ,						dim=0  )
					__A   ,    __A     :						str          =    checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2 ,						dim=0  )

					__A     :						Optional[Any]          =    {
					    # 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 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				Dict:
					__A     :						List[str]          =    'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
					__A     :						int          =    F"""{block_prefix}.{block_idx}"""

					__A     :						Optional[int]          =    {}

					if not hasattr(a ,						'attentions'  ):
										__A     :						Tuple          =    1  # resnet only
					elif not block.attentions[0].add_self_attention:
										__A     :						Any          =    2  # resnet -> cross-attention
					else:
										__A     :						Optional[Any]          =    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     :						List[str]          =    F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
										__A     :						Optional[Any]          =    n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
										__A     :						Any          =    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  ):
															__A     :						str          =    F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
															__A     :						Tuple          =    n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
															__A     :						Dict          =    F"""{block_prefix}.{idx}"""
															__A     :						Tuple          =    F"""{block_prefix}.{idx }"""
															__A     :						Union[str, Any]          =    1 if not attention.add_self_attention else 2
															__A     :						str          =    (
															    n * attention_idx + cross_attention_index
															    if block_type == 'up'
															    else n * attention_idx + cross_attention_index + 1
															)
															__A     :						Tuple          =    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 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				List[str]:
					__A     :						Optional[Any]          =    {}

					# 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 			_SCREAMING_SNAKE_CASE    (       a  )				->				Any:
					__A     :						str          =    original_config['input_channels'] + original_config['unet_cond_dim']
					__A     :						Tuple          =    original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

					__A     :						str          =    original_config['channels']

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

					__A     :						str          =    original_config['mapping_cond_dim']
					__A     :						Union[str, Any]          =    original_config['cross_cond_dim']

					__A     :						Optional[int]          =    []
					__A     :						Union[str, Any]          =    []
					for s, c in zip(original_config['self_attn_depths'] ,						original_config['cross_attn_depths']  ):
										if s:
															__A     :						Dict          =    'self'
															__A     :						Optional[Any]          =    'cross' if c else None
										elif c:
															__A     :						Tuple          =    'cross'
															__A     :						List[str]          =    None
										else:
															__A     :						Any          =    None
															__A     :						Union[str, Any]          =    None
										attna_types.append(a  )
										attna_types.append(a  )

					__A     :						str          =    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 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[str]:
					__A     :						Dict          =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

					__A     :						List[Any]          =    huggingface_hub.hf_hub_download(a ,						'config_laion_text_cond_latent_upscaler_2.json'  )
					__A     :						Union[str, Any]          =    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'  )
					__A     :						Optional[Any]          =    K.config.load_config(open(a  )  )['model']
					__A     :						Tuple          =    unet_model_from_original_config(a  )

					__A     :						Dict          =    torch.load(a ,						map_location=a  )['model_ema']
					__A     :						int          =    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__":
				UpperCAmelCase						:       Optional[int]        =					argparse.ArgumentParser()

				parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				UpperCAmelCase						:       int        =					parser.parse_args()

				main(args)











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

UpperCAmelCase						:       List[Any]        =					['''UtteranceLevel''']







@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[str]:
					__A     :						Any          =    torch.load(a ,						map_location='cpu'  )
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""  )

					__A     :						Dict          =    checkpoint['Downstream']

					__A     :						Optional[Any]          =    HubertConfig.from_pretrained(a  )
					__A     :						Tuple          =    HubertForSequenceClassification.from_pretrained(a ,						config=a  )
					__A     :						str          =    WavaVecaFeatureExtractor.from_pretrained(
					    a ,						return_attention_mask=a ,						do_normalize=a  )

					if hf_congfig.use_weighted_layer_sum:
										__A     :						Optional[Any]          =    checkpoint['Featurizer']['weights']

					__A     :						Dict          =    downstream_dict['projector.weight']
					__A     :						int          =    downstream_dict['projector.bias']
					__A     :						List[Any]          =    downstream_dict['model.post_net.linear.weight']
					__A     :						int          =    downstream_dict['model.post_net.linear.bias']

					hf_feature_extractor.save_pretrained(a  )
					hf_model.save_pretrained(a  )


if __name__ == "__main__":
				UpperCAmelCase						:       Dict        =					argparse.ArgumentParser()
				parser.add_argument(
				    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
				)
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
				parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
				parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
				UpperCAmelCase						:       Dict        =					parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




