


'''simple docstring'''


import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


__magic_name__								=   '''base_with_context'''




def   lowerCamelCase       (   lowerCamelCase	:   int				,    lowerCamelCase	:   Union[str, Any]):
      A_    :	List[str]							 =					nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]))
      A_    :	str							 =					nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""])				,    requires_grad=a__)
      for lyr_num, lyr in enumerate(model.encoders):
            A_    :	Union[str, Any]							 =					weights[F'layers_{lyr_num}']
            A_    :	Dict							 =					nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]))

            A_    :	Tuple							 =					ly_weight["""attention"""]
            A_    :	List[str]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T))
            A_    :	List[Any]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T))
            A_    :	Optional[int]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T))
            A_    :	int							 =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T))

            A_    :	str							 =					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]))

            A_    :	Optional[int]							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T))
            A_    :	Dict							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T))
            A_    :	str							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T))

      A_    :	Dict							 =					nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]))
      return model




def   lowerCamelCase       (   lowerCamelCase	:   Optional[Any]				,    lowerCamelCase	:   List[str]):
      A_    :	Optional[Any]							 =					nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T))

      A_    :	Union[str, Any]							 =					nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""])				,    requires_grad=a__)

      for lyr_num, lyr in enumerate(model.encoders):
            A_    :	List[str]							 =					weights[F'layers_{lyr_num}']
            A_    :	Optional[int]							 =					ly_weight["""attention"""]

            A_    :	Dict							 =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T))
            A_    :	int							 =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T))
            A_    :	Optional[int]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T))
            A_    :	Any							 =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T))
            A_    :	Union[str, Any]							 =					nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]))

            A_    :	Tuple							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T))
            A_    :	Any							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T))
            A_    :	Union[str, Any]							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T))
            A_    :	Any							 =					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]))

      A_    :	str							 =					nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]))

      return model




def   lowerCamelCase       (   lowerCamelCase	:   Optional[int]				,    lowerCamelCase	:   Tuple):
      A_    :	Optional[Any]							 =					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T))
      A_    :	Optional[Any]							 =					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T))

      A_    :	List[Any]							 =					nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""])				,    requires_grad=a__)

      A_    :	int							 =					nn.Parameter(
          torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T))

      for lyr_num, lyr in enumerate(model.decoders):
            A_    :	List[Any]							 =					weights[F'layers_{lyr_num}']
            A_    :	Tuple							 =					nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]))

            A_    :	str							 =					nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T))

            A_    :	Tuple							 =					ly_weight["""self_attention"""]
            A_    :	List[Any]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T))
            A_    :	int							 =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T))
            A_    :	List[Any]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T))
            A_    :	Optional[Any]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T))

            A_    :	Optional[Any]							 =					ly_weight["""MultiHeadDotProductAttention_0"""]
            A_    :	Optional[Any]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T))
            A_    :	List[str]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T))
            A_    :	Optional[int]							 =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T))
            A_    :	str							 =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T))
            A_    :	Optional[Any]							 =					nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]))

            A_    :	Union[str, Any]							 =					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]))
            A_    :	Dict							 =					nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T))
            A_    :	Tuple							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T))
            A_    :	List[str]							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T))
            A_    :	Dict							 =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T))

      A_    :	Tuple							 =					nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]))

      A_    :	Dict							 =					nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T))

      return model




def   lowerCamelCase       (   lowerCamelCase	:   Optional[int]):
      A_    :	Optional[Any]							 =					checkpoints.load_tax_checkpoint(args.checkpoint_path)
      A_    :	List[Any]							 =					jnp.tree_util.tree_map(onp.array				,    a__)

      A_    :	Dict							 =					[
          """from __gin__ import dynamic_registration""",
          """from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
          """diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
          """diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
      ]

      A_    :	Any							 =					os.path.join(args.checkpoint_path				,    """.."""				,    """config.gin""")
      A_    :	Union[str, Any]							 =					inference.parse_training_gin_file(a__				,    a__)
      A_    :	Any							 =					inference.InferenceModel(args.checkpoint_path				,    a__)

      A_    :	List[Any]							 =					DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""				,    variance_type="""fixed_large""")

      A_    :	Any							 =					SpectrogramNotesEncoder(
          max_length=synth_model.sequence_length["""inputs"""]				,    vocab_size=synth_model.model.module.config.vocab_size				,    d_model=synth_model.model.module.config.emb_dim				,    dropout_rate=synth_model.model.module.config.dropout_rate				,    num_layers=synth_model.model.module.config.num_encoder_layers				,    num_heads=synth_model.model.module.config.num_heads				,    d_kv=synth_model.model.module.config.head_dim				,    d_ff=synth_model.model.module.config.mlp_dim				,    feed_forward_proj="""gated-gelu"""				,    )

      A_    :	str							 =					SpectrogramContEncoder(
          input_dims=synth_model.audio_codec.n_dims				,    targets_context_length=synth_model.sequence_length["""targets_context"""]				,    d_model=synth_model.model.module.config.emb_dim				,    dropout_rate=synth_model.model.module.config.dropout_rate				,    num_layers=synth_model.model.module.config.num_encoder_layers				,    num_heads=synth_model.model.module.config.num_heads				,    d_kv=synth_model.model.module.config.head_dim				,    d_ff=synth_model.model.module.config.mlp_dim				,    feed_forward_proj="""gated-gelu"""				,    )

      A_    :	Optional[Any]							 =					TaFilmDecoder(
          input_dims=synth_model.audio_codec.n_dims				,    targets_length=synth_model.sequence_length["""targets_context"""]				,    max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time				,    d_model=synth_model.model.module.config.emb_dim				,    num_layers=synth_model.model.module.config.num_decoder_layers				,    num_heads=synth_model.model.module.config.num_heads				,    d_kv=synth_model.model.module.config.head_dim				,    d_ff=synth_model.model.module.config.mlp_dim				,    dropout_rate=synth_model.model.module.config.dropout_rate				,    )

      A_    :	Union[str, Any]							 =					load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]				,    a__)
      A_    :	str							 =					load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]				,    a__)
      A_    :	List[str]							 =					load_decoder(ta_checkpoint["""target"""]["""decoder"""]				,    a__)

      A_    :	Union[str, Any]							 =					OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""")

      A_    :	Optional[int]							 =					SpectrogramDiffusionPipeline(
          notes_encoder=a__				,    continuous_encoder=a__				,    decoder=a__				,    scheduler=a__				,    melgan=a__				,    )
      if args.save:
            pipe.save_pretrained(args.output_path)


if __name__ == "__main__":
     __magic_name__								=   argparse.ArgumentParser()

     parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
     parser.add_argument(
         '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
     )
     parser.add_argument(
         '--checkpoint_path',
         default=f"""{MODEL}/checkpoint_500000""",
         type=str,
         required=False,
         help='Path to the original jax model checkpoint.',
     )
     __magic_name__								=   parser.parse_args()

     main(args)




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__    :List[Any]       =				{
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :List[str]       =				{
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :Any       =				{
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

lowerCAmelCase__    :Optional[int]       =				{
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Optional[int]       =				{
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Any       =				{
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						int:


							'''simple docstring'''





							if isinstance(a__     ,    a__			):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError('boolean value expected'			)

def 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						int     ,    a__:						Union[str, Any]     ,    a__:						Dict     ,    a__:						Optional[int]=False			)    ->						Dict:


							'''simple docstring'''





							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.0.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.0.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.2.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.in_layers.2.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.0.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.0.bias''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.3.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.skip_connection.weight''']
														_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						List[str]     ,    a__:						List[Any]     ,    a__:						List[str]=None			)    ->						Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,    dim=0			)
							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,    dim=0			)

							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.norm.weight''']
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.norm.bias''']

							_UpperCAmelCase											=     weight_q.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_q.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     weight_k.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_k.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     weight_v.squeeze(-1			).squeeze(-1			)
							_UpperCAmelCase											=     bias_v.squeeze(-1			).squeeze(-1			)

							_UpperCAmelCase											=     (
							    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1			).squeeze(-1			)
							)
							_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1			).squeeze(-1			)

							return new_checkpoint

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						Any			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.load(a__     ,    map_location='cpu'			)
							_UpperCAmelCase											=     {}

							_UpperCAmelCase											=     checkpoint['time_embed.0.weight']
							_UpperCAmelCase											=     checkpoint['time_embed.0.bias']
							_UpperCAmelCase											=     checkpoint['time_embed.2.weight']
							_UpperCAmelCase											=     checkpoint['time_embed.2.bias']

							if unet_config["num_class_embeds"] is not None:
														_UpperCAmelCase											=     checkpoint['label_emb.weight']

							_UpperCAmelCase											=     checkpoint['input_blocks.0.0.weight']
							_UpperCAmelCase											=     checkpoint['input_blocks.0.0.bias']

							_UpperCAmelCase											=     unet_config['down_block_types']
							_UpperCAmelCase											=     unet_config['layers_per_block']
							_UpperCAmelCase											=     unet_config['attention_head_dim']
							_UpperCAmelCase											=     unet_config['block_out_channels']
							_UpperCAmelCase											=     1
							_UpperCAmelCase											=     channels_list[0]

							for i, layer_type in enumerate(a__			):
														_UpperCAmelCase											=     channels_list[i]
														_UpperCAmelCase											=     current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(a__			):
																												_UpperCAmelCase											=     F'''down_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     True if j == 0 and downsample_block_has_skip else False
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(a__			):
																												_UpperCAmelCase											=     F'''down_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     True if j == 0 and downsample_block_has_skip else False
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''down_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

														if i != len(a__			) - 1:
																					_UpperCAmelCase											=     F'''down_blocks.{i}.downsamplers.0'''
																					_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																					_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
																					current_layer += 1

														_UpperCAmelCase											=     current_channels

							# hardcoded the mid-block for now
							_UpperCAmelCase											=     'mid_block.resnets.0'
							_UpperCAmelCase											=     'middle_block.0'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.attentions.0'
							_UpperCAmelCase											=     'middle_block.1'
							_UpperCAmelCase											=     convert_attention(a__     ,    a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.resnets.1'
							_UpperCAmelCase											=     'middle_block.2'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     unet_config['up_block_types']

							for i, layer_type in enumerate(a__			):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1			):
																												_UpperCAmelCase											=     F'''up_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.1'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1			):
																												_UpperCAmelCase											=     F'''up_blocks.{i}.resnets.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.0'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''up_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.2'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_UpperCAmelCase											=     checkpoint['out.0.weight']
							_UpperCAmelCase											=     checkpoint['out.0.bias']
							_UpperCAmelCase											=     checkpoint['out.2.weight']
							_UpperCAmelCase											=     checkpoint['out.2.bias']

							return new_checkpoint


if __name__ == "__main__":
		lowerCAmelCase__    :Union[str, Any]       =				argparse.ArgumentParser()

		parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
		)
		parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

		lowerCAmelCase__    :Dict       =				parser.parse_args()
		lowerCAmelCase__    :List[str]       =				strabool(args.class_cond)

		lowerCAmelCase__    :Any       =				os.path.basename(args.unet_path)
		print(f'''Checkpoint: {ckpt_name}''')

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, Any]       =				IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowerCAmelCase__    :Tuple       =				LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				lowerCAmelCase__    :Any       =				TEST_UNET_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		if not args.class_cond:
				lowerCAmelCase__    :str       =				None

		lowerCAmelCase__    :Tuple       =				con_pt_to_diffuser(args.unet_path, unet_config)

		lowerCAmelCase__    :Optional[int]       =				UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				lowerCAmelCase__    :int       =				CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, Any]       =				CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowerCAmelCase__    :Tuple       =				CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		lowerCAmelCase__    :Any       =				CMStochasticIterativeScheduler(**scheduler_config)

		lowerCAmelCase__    :Union[str, Any]       =				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)
