code: 
  """simple docstring"""
  import argparse
  
  import huggingface_hub
  import k_diffusion as K
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  _A						=     """pcuenq/k-upscaler"""
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  *, lowerCAmelCase ,  lowerCAmelCase						)  ->   int:
        UpperCAmelCase__ :       Any						=						{
            # 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__	(	lowerCAmelCase ,  *, lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
        UpperCAmelCase__ :       str						=						checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3 ,  dim=0						)
        UpperCAmelCase__ :       List[Any]						=						checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3 ,  dim=0						)
        UpperCAmelCase__ :       List[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 				a__	(	lowerCAmelCase ,  *, lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   int:
        UpperCAmelCase__ :       Tuple						=						checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2 ,  dim=0						)
        UpperCAmelCase__ :       List[Any]						=						checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2 ,  dim=0						)
  
        UpperCAmelCase__ :       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 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   List[str]:
        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__ :       int						=						{}
  
        if not hasattr(lowerCAmelCase ,  """attentions"""						):
              UpperCAmelCase__ :       Dict						=						1  # resnet only
        elif not block.attentions[0].add_self_attention:
              UpperCAmelCase__ :       Optional[int]						=						2  # resnet -> cross-attention
        else:
              UpperCAmelCase__ :       Union[str, 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}"
              UpperCAmelCase__ :       Any						=						F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
              UpperCAmelCase__ :       Tuple						=						n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
              UpperCAmelCase__ :       Tuple						=						F"""{block_prefix}.{idx}""" if block_type == """up""" else F"""{block_prefix}.{idx}"""
  
              diffusers_checkpoint.update(
                  resnet_to_diffusers_checkpoint(
                      lowerCAmelCase ,  lowerCAmelCase ,  diffusers_resnet_prefix=lowerCAmelCase ,  resnet_prefix=lowerCAmelCase						)						)
  
        if hasattr(lowerCAmelCase ,  """attentions"""						):
              for attention_idx, attention in enumerate(block.attentions						):
                    UpperCAmelCase__ :       Dict						=						F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
                    UpperCAmelCase__ :       int						=						n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
                    UpperCAmelCase__ :       Any						=						F"""{block_prefix}.{idx}"""
                    UpperCAmelCase__ :       Optional[Any]						=						F"""{block_prefix}.{idx }"""
                    UpperCAmelCase__ :       Union[str, Any]						=						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__ :       str						=						F"""{block_prefix}.{idx }"""
  
                    diffusers_checkpoint.update(
                        cross_attn_to_diffusers_checkpoint(
                            lowerCAmelCase ,  diffusers_attention_prefix=lowerCAmelCase ,  diffusers_attention_index=2 ,  attention_prefix=lowerCAmelCase ,  )						)
  
                    if attention.add_self_attention is True:
                          diffusers_checkpoint.update(
                              self_attn_to_diffusers_checkpoint(
                                  lowerCAmelCase ,  diffusers_attention_prefix=lowerCAmelCase ,  attention_prefix=lowerCAmelCase ,  )						)
  
        return diffusers_checkpoint
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   Any:
        UpperCAmelCase__ :       List[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(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  """down"""						)						)
  
        # up_blocks
        for up_block_idx, up_block in enumerate(model.up_blocks						):
              diffusers_checkpoint.update(block_to_diffusers_checkpoint(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  """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__	(	lowerCAmelCase						)  ->   Union[str, Any]:
        UpperCAmelCase__ :       List[str]						=						original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
        UpperCAmelCase__ :       str						=						original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)
  
        UpperCAmelCase__ :       List[Any]						=						original_config["""channels"""]
  
        assert (
            len(set(original_config["""depths"""]						)						) == 1
        ), "UNet2DConditionModel currently do not support blocks with different number of layers"
        UpperCAmelCase__ :       Optional[int]						=						original_config["""depths"""][0]
  
        UpperCAmelCase__ :       str						=						original_config["""mapping_cond_dim"""]
        UpperCAmelCase__ :       Optional[int]						=						original_config["""cross_cond_dim"""]
  
        UpperCAmelCase__ :       List[str]						=						[]
        UpperCAmelCase__ :       Any						=						[]
        for s, c in zip(original_config["""self_attn_depths"""] ,  original_config["""cross_attn_depths"""]						):
              if s:
                    UpperCAmelCase__ :       Tuple						=						"""self"""
                    UpperCAmelCase__ :       int						=						"""cross""" if c else None
              elif c:
                    UpperCAmelCase__ :       Tuple						=						"""cross"""
                    UpperCAmelCase__ :       Optional[Any]						=						None
              else:
                    UpperCAmelCase__ :       int						=						None
                    UpperCAmelCase__ :       Union[str, Any]						=						None
              attna_types.append(lowerCAmelCase						)
              attna_types.append(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Any						=						UNetaDConditionModel(
            in_channels=lowerCAmelCase ,  out_channels=lowerCAmelCase ,  down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""") ,  mid_block_type=lowerCAmelCase ,  up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""") ,  block_out_channels=lowerCAmelCase ,  layers_per_block=lowerCAmelCase ,  act_fn="""gelu""" ,  norm_num_groups=lowerCAmelCase ,  cross_attention_dim=lowerCAmelCase ,  attention_head_dim=64 ,  time_cond_proj_dim=lowerCAmelCase ,  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__	(	lowerCAmelCase						)  ->   Union[str, Any]:
        UpperCAmelCase__ :       Dict						=						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)
  
        UpperCAmelCase__ :       Tuple						=						huggingface_hub.hf_hub_download(lowerCAmelCase ,  """config_laion_text_cond_latent_upscaler_2.json"""						)
        UpperCAmelCase__ :       List[str]						=						huggingface_hub.hf_hub_download(
            lowerCAmelCase ,  """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[str]						=						K.config.load_config(open(lowerCAmelCase						)						)["""model"""]
        UpperCAmelCase__ :       str						=						unet_model_from_original_config(lowerCAmelCase						)
  
        UpperCAmelCase__ :       List[Any]						=						torch.load(lowerCAmelCase ,  map_location=lowerCAmelCase						)["""model_ema"""]
        UpperCAmelCase__ :       str						=						unet_to_diffusers_checkpoint(lowerCAmelCase ,  lowerCAmelCase						)
  
        model.load_state_dict(lowerCAmelCase ,  strict=lowerCAmelCase						)
        model.save_pretrained(args.dump_path						)
        print(F"""saving converted unet model in {args.dump_path}"""						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
  
      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      _A						=     parser.parse_args()
  
      main(args)
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
        UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
        UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
        # Encoder
        for layer_index in range(config.num_layers						):
              UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       str						=						tax_attention_key
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
              UpperCAmelCase__ :       List[Any]						=						tax_attention_value
  
              UpperCAmelCase__ :       Any						=						tax_attention_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi
  
              UpperCAmelCase__ :       int						=						tax_mlp_wo
              UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       str						=						tax_encoder_rel_embedding
  
        # Assigning
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm
  
        # Decoder
        for layer_index in range(config.num_decoder_layers						):
              UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       List[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                  """scale"""
              ]
  
              # Encoder-Decoder-Attention
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                  """kernel"""
              ]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
              # MLP
              if split_mlp_wi:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Optional[int]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
              UpperCAmelCase__ :       str						=						tax_attention_out
              UpperCAmelCase__ :       Tuple						=						tax_attention_query
              UpperCAmelCase__ :       int						=						tax_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm
  
              UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
              UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
              UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
              UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       str						=						tax_mlp_wi
  
              UpperCAmelCase__ :       Tuple						=						tax_mlp_wo
  
              UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm
  
        # Decoder Normalization
        UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Union[str, Any]						=						txa_decoder_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       Optional[int]						=						tax_decoder_rel_embedding
  
        # Token Embeddings
        UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
        UpperCAmelCase__ :       int						=						txa_token_embeddings
  
        # LM Head (only in v1.1 checkpoints)
        if "logits_dense" in tax_model["target"]["decoder"]:
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
        flax_model.save_pretrained(lowerCAmelCase						)
        print("""T5X Model was sucessfully converted!"""						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
      )
      parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
      parser.add_argument(
          """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
      )
      _A						=     parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  