

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_lowerCamelCase       :     Any           =							"pcuenq/k-upscaler"

def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    Tuple       ,				*, UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    int    )						->		Any:
  UpperCAmelCase :							str								=				{
      # 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							:    List[str]       ,				*, UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    Tuple    )						->		Optional[Any]:
  UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							str								=				checkpoint[f'''{attention_prefix}.qkv_proj.weight'''].chunk(3       ,				dim=0    )
  UpperCAmelCase				,		UpperCAmelCase				,		UpperCAmelCase :							Dict								=				checkpoint[f'''{attention_prefix}.qkv_proj.bias'''].chunk(3       ,				dim=0    )
  UpperCAmelCase :							Dict								=				{
      # 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							:    int       ,				*, UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Dict       ,				UpperCAmelCase							:    Optional[int]    )						->		Tuple:
  UpperCAmelCase				,		UpperCAmelCase :							List[str]								=				checkpoint[f'''{attention_prefix}.kv_proj.weight'''].chunk(2       ,				dim=0    )
  UpperCAmelCase				,		UpperCAmelCase :							Optional[int]								=				checkpoint[f'''{attention_prefix}.kv_proj.bias'''].chunk(2       ,				dim=0    )

  UpperCAmelCase :							List[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     a__  (	UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Dict    )						->		Dict:
  UpperCAmelCase :							Tuple								=				'''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
  UpperCAmelCase :							Union[str, Any]								=				f'''{block_prefix}.{block_idx}'''

  UpperCAmelCase :							Optional[int]								=				{}

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

  UpperCAmelCase :							Optional[int]								=				original_config['''channels''']

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

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

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

  UpperCAmelCase :							Any								=				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=64       ,				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							:    Any    )						->		List[str]:
  UpperCAmelCase :							Any								=				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''    )

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

  UpperCAmelCase :							str								=				torch.load(UpperCAmelCase       ,				map_location=UpperCAmelCase    )['''model_ema''']
  UpperCAmelCase :							Tuple								=				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       :     Union[str, Any]           =							argparse.ArgumentParser()

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

     main(args)








from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase       :     str           =							logging.get_logger(__name__)





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  ["""input_values""", """attention_mask"""]



   def __init__(							self							:  Union[str, Any],	__A							:  Dict=1,	__A							:  Any=1_6_0_0_0,	__A							:  List[str]=1_2_8,	__A							:  str=1_0_2_4,	__A							:  int=0.0,	__A							:  str=True,	__A							:  Any=-4.2_6_7_7_3_9_3,	__A							:  Optional[int]=4.5_6_8_9_9_7_4,	__A							:  Optional[int]=False,	**__A							:  str,	):
     super().__init__(feature_size=__A,	sampling_rate=__A,	padding_value=__A,	**__A		)
     UpperCAmelCase :							int								=				num_mel_bins
     UpperCAmelCase :							Tuple								=				max_length
     UpperCAmelCase :							str								=				do_normalize
     UpperCAmelCase :							List[Any]								=				mean
     UpperCAmelCase :							Union[str, Any]								=				std
     UpperCAmelCase :							Any								=				return_attention_mask



   def  __magic_name__					(							self							:  Dict,	__A							:  np.ndarray,	__A							:  int,	):
     UpperCAmelCase :							Union[str, Any]								=				torch.from_numpy(__A		).unsqueeze(0		)
     UpperCAmelCase :							List[str]								=				ta_kaldi.fbank(
         __A,	htk_compat=__A,	sample_frequency=self.sampling_rate,	use_energy=__A,	window_type='''hanning''',	num_mel_bins=self.num_mel_bins,	dither=0.0,	frame_shift=1_0,	)

     UpperCAmelCase :							int								=				fbank.shape[0]
     UpperCAmelCase :							int								=				max_length - n_frames

     # pad or truncate, depending on difference
     if difference > 0:
       UpperCAmelCase :							List[Any]								=				torch.nn.ZeroPadad((0, 0, 0, difference)		)
       UpperCAmelCase :							int								=				pad_module(__A		)
     elif difference < 0:
       UpperCAmelCase :							Dict								=				fbank[0:max_length, :]

     UpperCAmelCase :							int								=				fbank.numpy()

     return fbank



   def  __magic_name__					(							self							:  Optional[Any],	__A							:  np.ndarray		):
     return (input_values - (self.mean)) / (self.std * 2)





   def __call__(							self							:  Union[str, Any],	__A							:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],	__A							:  Optional[int] = None,	__A							:  Optional[Union[str, TensorType]] = None,	**__A							:  Optional[Any],	):

     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
             F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
             F''' {self.sampling_rate} and not {sampling_rate}.'''		)
     else:
       logger.warning(
           '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
           '''Failing to do so can result in silent errors that might be hard to debug.'''		)

     UpperCAmelCase :							Any								=				isinstance(__A,	np.ndarray		) and len(raw_speech.shape		) > 1
     if is_batched_numpy and len(raw_speech.shape		) > 2:
       raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''		)
     UpperCAmelCase :							Dict								=				is_batched_numpy or (
         isinstance(__A,	(list, tuple)		) and (isinstance(raw_speech[0],	(np.ndarray, tuple, list)		))
     )

     if is_batched:
       UpperCAmelCase :							List[Any]								=				[np.asarray(__A,	dtype=np.floataa		) for speech in raw_speech]
     elif not is_batched and not isinstance(__A,	np.ndarray		):
       UpperCAmelCase :							Union[str, Any]								=				np.asarray(__A,	dtype=np.floataa		)
     elif isinstance(__A,	np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
       UpperCAmelCase :							int								=				raw_speech.astype(np.floataa		)

     # always return batch
     if not is_batched:
       UpperCAmelCase :							str								=				[raw_speech]

     # extract fbank features and pad/truncate to max_length
     UpperCAmelCase :							Union[str, Any]								=				[self._extract_fbank_features(__A,	max_length=self.max_length		) for waveform in raw_speech]

     # convert into BatchFeature
     UpperCAmelCase :							Optional[Any]								=				BatchFeature({'''input_values''': features}		)

     # make sure list is in array format
     UpperCAmelCase :							Optional[int]								=				padded_inputs.get('''input_values'''		)
     if isinstance(input_values[0],	__A		):
       UpperCAmelCase :							Dict								=				[np.asarray(__A,	dtype=np.floataa		) for feature in input_values]

     # normalization
     if self.do_normalize:
       UpperCAmelCase :							str								=				[self.normalize(__A		) for feature in input_values]

     if return_tensors is not None:
       UpperCAmelCase :							Tuple								=				padded_inputs.convert_to_tensors(__A		)

     return padded_inputs





