

'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase__            =     logging.get_logger(__name__)

lowercase__            =     {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 for attribute in key.split('.'      ):
  UpperCAmelCase			:      Any		   =					getattr(UpperCAmelCase_    ,			UpperCAmelCase_      )

 if weight_type is not None:
  UpperCAmelCase			:      int		   =					getattr(UpperCAmelCase_    ,			UpperCAmelCase_      ).shape
 else:
  UpperCAmelCase			:      Optional[Any]		   =					hf_pointer.shape

 assert hf_shape == value.shape, (
     F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
     F""" {value.shape} for {full_name}"""
 )

 if weight_type == "weight":
  UpperCAmelCase			:      List[Any]		   =					value
 elif weight_type == "weight_g":
  UpperCAmelCase			:      Dict		   =					value
 elif weight_type == "weight_v":
  UpperCAmelCase			:      int		   =					value
 elif weight_type == "bias":
  UpperCAmelCase			:      str		   =					value
 else:
  UpperCAmelCase			:      Tuple		   =					value

 logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      List[Any]		   =					[]
 UpperCAmelCase			:      Union[str, Any]		   =					fairseq_model.state_dict()

 UpperCAmelCase			:      Dict		   =					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase			:      Optional[int]		   =					False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			hf_model.config.feat_extract_norm == 'group'    ,			)
   UpperCAmelCase			:      str		   =					True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCAmelCase			:      str		   =					'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

    if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
     UpperCAmelCase			:      str		   =					True
     if "*" in mapped_key:
      UpperCAmelCase			:      Dict		   =					name.split(UpperCAmelCase_      )[0].split('.'      )[-2]
      if not layer_index.isnumeric():
       continue
      UpperCAmelCase			:      List[Any]		   =					mapped_key.replace('*'    ,			UpperCAmelCase_      )
     if "weight_g" in name:
      UpperCAmelCase			:      int		   =					'weight_g'
     elif "weight_v" in name:
      UpperCAmelCase			:      Optional[Any]		   =					'weight_v'
     elif "weight" in name:
      UpperCAmelCase			:      Any		   =					'weight'
     elif "bias" in name:
      UpperCAmelCase			:      List[Any]		   =					'bias'
     else:
      UpperCAmelCase			:      List[Any]		   =					None
     set_recursively(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      )
    continue
  if not is_used:
   unused_weights.append(UpperCAmelCase_      )

 logger.warning(F"""Unused weights: {unused_weights}"""      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      List[Any]		   =					full_name.split('conv_layers.'      )[-1]
 UpperCAmelCase			:      Union[str, Any]		   =					name.split('.'      )
 UpperCAmelCase			:      str		   =					int(items[0]      )
 UpperCAmelCase			:      Optional[int]		   =					int(items[1]      )

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       F"""{full_name} has size {value.shape}, but"""
       F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
   )
   UpperCAmelCase			:      Any		   =					value
   logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       F"""{full_name} has size {value.shape}, but"""
       F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
   )
   UpperCAmelCase			:      List[str]		   =					value
   logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
       " found."
   )
   UpperCAmelCase			:      Dict		   =					value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       F"""{full_name} has size {value.shape}, but"""
       F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
   )
   UpperCAmelCase			:      Optional[int]		   =					value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
 else:
  unused_weights.append(UpperCAmelCase_      )




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      List[str]		   =					SEWDConfig()
 if is_finetuned:
  UpperCAmelCase			:      Optional[int]		   =					model.wav_encoder.wav_model.cfg
 else:
  UpperCAmelCase			:      Optional[int]		   =					model.cfg

 UpperCAmelCase			:      List[str]		   =					fs_config.conv_bias
 UpperCAmelCase			:      int		   =					eval(fs_config.conv_feature_layers      )
 UpperCAmelCase			:      Dict		   =					[x[0] for x in conv_layers]
 UpperCAmelCase			:      Dict		   =					[x[1] for x in conv_layers]
 UpperCAmelCase			:      List[str]		   =					[x[2] for x in conv_layers]
 UpperCAmelCase			:      Optional[int]		   =					'gelu'
 UpperCAmelCase			:      Union[str, Any]		   =					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 UpperCAmelCase			:      Optional[int]		   =					0.0
 UpperCAmelCase			:      Optional[Any]		   =					fs_config.activation_fn.name
 UpperCAmelCase			:      Union[str, Any]		   =					fs_config.encoder_embed_dim
 UpperCAmelCase			:      Union[str, Any]		   =					0.02
 UpperCAmelCase			:      str		   =					fs_config.encoder_ffn_embed_dim
 UpperCAmelCase			:      Optional[int]		   =					1E-5
 UpperCAmelCase			:      str		   =					fs_config.encoder_layerdrop
 UpperCAmelCase			:      Union[str, Any]		   =					fs_config.encoder_attention_heads
 UpperCAmelCase			:      Any		   =					fs_config.conv_pos_groups
 UpperCAmelCase			:      int		   =					fs_config.conv_pos
 UpperCAmelCase			:      Dict		   =					len(UpperCAmelCase_      )
 UpperCAmelCase			:      Optional[Any]		   =					fs_config.encoder_layers
 UpperCAmelCase			:      Optional[Any]		   =					fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 UpperCAmelCase			:      Union[str, Any]		   =					fs_config.max_position_embeddings
 UpperCAmelCase			:      List[Any]		   =					fs_config.position_buckets
 UpperCAmelCase			:      List[Any]		   =					fs_config.share_att_key
 UpperCAmelCase			:      Optional[Any]		   =					fs_config.relative_attention
 UpperCAmelCase			:      Tuple		   =					fs_config.position_biased_input
 UpperCAmelCase			:      Tuple		   =					tuple(fs_config.pos_att_type.split('|'      )      )
 UpperCAmelCase			:      Tuple		   =					fs_config.norm_rel_ebd

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  UpperCAmelCase			:      int		   =					model.cfg
  UpperCAmelCase			:      List[str]		   =					fs_config.final_dropout
  UpperCAmelCase			:      Optional[int]		   =					fs_config.layerdrop
 UpperCAmelCase			:      List[Any]		   =					fs_config.activation_dropout
 UpperCAmelCase			:      Dict		   =					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 UpperCAmelCase			:      Optional[Any]		   =					fs_config.attention_dropout
 UpperCAmelCase			:      Optional[Any]		   =					fs_config.dropout_input
 UpperCAmelCase			:      str		   =					fs_config.dropout
 UpperCAmelCase			:      Any		   =					fs_config.mask_channel_length
 UpperCAmelCase			:      str		   =					fs_config.mask_channel_prob
 UpperCAmelCase			:      str		   =					fs_config.mask_length
 UpperCAmelCase			:      Optional[int]		   =					fs_config.mask_prob

 UpperCAmelCase			:      int		   =					'Wav2Vec2FeatureExtractor'
 UpperCAmelCase			:      Dict		   =					'Wav2Vec2CTCTokenizer'

 return config




@torch.no_grad()
def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_=None    ,			UpperCAmelCase_=None    ,			UpperCAmelCase_=True      ):

 if is_finetuned:
  UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Tuple		   =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]    ,			arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
 else:
  UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Tuple		   =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

 if config_path is not None:
  UpperCAmelCase			:      Tuple		   =					SEWDConfig.from_pretrained(UpperCAmelCase_      )
 else:
  UpperCAmelCase			:      str		   =					convert_config(model[0]    ,			UpperCAmelCase_      )
 UpperCAmelCase			:      str		   =					model[0].eval()

 UpperCAmelCase			:      List[str]		   =					True if config.feat_extract_norm == 'layer' else False
 UpperCAmelCase			:      Union[str, Any]		   =					WavaVecaFeatureExtractor(
     feature_size=1    ,			sampling_rate=1_60_00    ,			padding_value=0    ,			do_normalize=UpperCAmelCase_    ,			return_attention_mask=UpperCAmelCase_    ,			)

 if is_finetuned:
  if dict_path:
   UpperCAmelCase			:      Tuple		   =					Dictionary.load(UpperCAmelCase_      )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   UpperCAmelCase			:      List[str]		   =					target_dict.pad_index
   UpperCAmelCase			:      int		   =					target_dict.bos_index
   UpperCAmelCase			:      Tuple		   =					target_dict.pad_index
   UpperCAmelCase			:      Optional[Any]		   =					target_dict.bos_index
   UpperCAmelCase			:      Tuple		   =					target_dict.eos_index
   UpperCAmelCase			:      Tuple		   =					len(target_dict.symbols      )
   UpperCAmelCase			:      Union[str, Any]		   =					os.path.join(UpperCAmelCase_    ,			'vocab.json'      )
   if not os.path.isdir(UpperCAmelCase_      ):
    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase_      )      )
    return
   os.makedirs(UpperCAmelCase_    ,			exist_ok=UpperCAmelCase_      )
   with open(UpperCAmelCase_    ,			'w'    ,			encoding='utf-8'      ) as vocab_handle:
    json.dump(target_dict.indices    ,			UpperCAmelCase_      )
   UpperCAmelCase			:      List[str]		   =					WavaVecaCTCTokenizer(
       UpperCAmelCase_    ,			unk_token=target_dict.unk_word    ,			pad_token=target_dict.pad_word    ,			bos_token=target_dict.bos_word    ,			eos_token=target_dict.eos_word    ,			word_delimiter_token='|'    ,			do_lower_case=UpperCAmelCase_    ,			)
   UpperCAmelCase			:      Optional[Any]		   =					WavaVecaProcessor(feature_extractor=UpperCAmelCase_    ,			tokenizer=UpperCAmelCase_      )
   processor.save_pretrained(UpperCAmelCase_      )

  UpperCAmelCase			:      Dict		   =					SEWDForCTC(UpperCAmelCase_      )
 else:
  UpperCAmelCase			:      int		   =					SEWDModel(UpperCAmelCase_      )
  feature_extractor.save_pretrained(UpperCAmelCase_      )

 recursively_load_weights(UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      )

 hf_model.save_pretrained(UpperCAmelCase_      )


if __name__ == "__main__":
 lowercase__            =     argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 lowercase__            =     parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )







'''simple docstring'''

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowercase__            =     "pcuenq/k-upscaler"




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      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  UpperCamelCase(	UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      List[str]		   =					checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      List[Any]		   =					checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3    ,			dim=0      )
 UpperCAmelCase			:      Optional[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  UpperCamelCase(	UpperCAmelCase_    ,			*, UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2    ,			dim=0      )
 UpperCAmelCase   ,					UpperCAmelCase			:      Any		   =					checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2    ,			dim=0      )

 UpperCAmelCase			:      Optional[int]		   =					{
     # 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  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      str		   =					'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
 UpperCAmelCase			:      Tuple		   =					F"""{block_prefix}.{block_idx}"""

 UpperCAmelCase			:      Union[str, Any]		   =					{}

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

 # 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  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      List[Any]		   =					original_config['input_channels'] + original_config['unet_cond_dim']
 UpperCAmelCase			:      Any		   =					original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

 UpperCAmelCase			:      Tuple		   =					original_config['channels']

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

 UpperCAmelCase			:      Tuple		   =					original_config['mapping_cond_dim']
 UpperCAmelCase			:      Any		   =					original_config['cross_cond_dim']

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

 UpperCAmelCase			:      List[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  UpperCamelCase(	UpperCAmelCase_      ):
 UpperCAmelCase			:      Optional[Any]		   =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

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

 UpperCAmelCase			:      List[Any]		   =					torch.load(UpperCAmelCase_    ,			map_location=UpperCAmelCase_      )['model_ema']
 UpperCAmelCase			:      Optional[Any]		   =					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__":
 lowercase__            =     argparse.ArgumentParser()

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

 main(args)




