

import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
       from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_SCREAMING_SNAKE_CASE					:  int             =      logging.get_logger(__name__)  # pylint: disable=invalid-name

_SCREAMING_SNAKE_CASE					:  Tuple             =      256






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= ["melgan"]






  def __init__(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		SpectrogramNotesEncoder     ,       __lowerCamelCase						:		SpectrogramContEncoder     ,       __lowerCamelCase						:		TaFilmDecoder     ,       __lowerCamelCase						:		DDPMScheduler     ,       __lowerCamelCase						:		OnnxRuntimeModel if is_onnx_available() else Any     ,       )		->							None:
       super().__init__()

       # From MELGAN
       SCREAMING_SNAKE_CASE__   =   math.log(1e-5 )  # Matches MelGAN training.
       SCREAMING_SNAKE_CASE__   =   4.0  # Largest value for most examples
       SCREAMING_SNAKE_CASE__   =   128

       self.register_modules(
           notes_encoder=__lowerCamelCase     ,       continuous_encoder=__lowerCamelCase     ,       decoder=__lowerCamelCase     ,       scheduler=__lowerCamelCase     ,       melgan=__lowerCamelCase     ,       )






  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Union[str, Any]=(-1.0, 1.0)     ,       __lowerCamelCase						:		Tuple=False )		->							List[str]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   output_range
       if clip:
            SCREAMING_SNAKE_CASE__   =   torch.clip(__lowerCamelCase     ,       self.min_value     ,       self.max_value )
       # Scale to [0, 1].
       SCREAMING_SNAKE_CASE__   =   (features - self.min_value) / (self.max_value - self.min_value)
       # Scale to [min_out, max_out].
       return zero_one * (max_out - min_out) + min_out






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		Any=(-1.0, 1.0)     ,       __lowerCamelCase						:		Tuple=False )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   input_range
       SCREAMING_SNAKE_CASE__   =   torch.clip(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase ) if clip else outputs
       # Scale to [0, 1].
       SCREAMING_SNAKE_CASE__   =   (outputs - min_out) / (max_out - min_out)
       # Scale to [self.min_value, self.max_value].
       return zero_one * (self.max_value - self.min_value) + self.min_value






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		List[str] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   input_tokens > 0
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.notes_encoder(
           encoder_input_tokens=__lowerCamelCase     ,       encoder_inputs_mask=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   self.continuous_encoder(
           encoder_inputs=__lowerCamelCase     ,       encoder_inputs_mask=__lowerCamelCase )

       return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]






  def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		List[str] )		->							str:
       SCREAMING_SNAKE_CASE__   =   noise_time
       if not torch.is_tensor(__lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   torch.tensor([timesteps]     ,       dtype=torch.long     ,       device=input_tokens.device )
       elif torch.is_tensor(__lowerCamelCase ) and len(timesteps.shape ) == 0:
            SCREAMING_SNAKE_CASE__   =   timesteps[None].to(input_tokens.device )

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       SCREAMING_SNAKE_CASE__   =   timesteps * torch.ones(input_tokens.shape[0]     ,       dtype=timesteps.dtype     ,       device=timesteps.device )

       SCREAMING_SNAKE_CASE__   =   self.decoder(
           encodings_and_masks=__lowerCamelCase     ,       decoder_input_tokens=__lowerCamelCase     ,       decoder_noise_time=__lowerCamelCase )
       return logits







  @torch.no_grad()
  def __call__(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		List[List[int]]     ,       __lowerCamelCase						:		Optional[torch.Generator] = None     ,       __lowerCamelCase						:		int = 100     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		str = "numpy"     ,       __lowerCamelCase						:		Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,       __lowerCamelCase						:		int = 1     ,       )		->							Union[AudioPipelineOutput, Tuple]:
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__lowerCamelCase     ,       __lowerCamelCase ) or callback_steps <= 0)
       ):
            raise ValueError(
                f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                f''' {type(__lowerCamelCase )}.''' )

       SCREAMING_SNAKE_CASE__   =   np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]     ,       dtype=np.floataa )
       SCREAMING_SNAKE_CASE__   =   np.zeros([1, 0, self.n_dims]     ,       np.floataa )
       SCREAMING_SNAKE_CASE__   =   torch.ones((1, TARGET_FEATURE_LENGTH)     ,       dtype=__lowerCamelCase     ,       device=self.device )

       for i, encoder_input_tokens in enumerate(__lowerCamelCase ):
            if i == 0:
                 SCREAMING_SNAKE_CASE__   =   torch.from_numpy(pred_mel[:1].copy() ).to(
                     device=self.device     ,       dtype=self.decoder.dtype )
                 # The first chunk has no previous context.
                 SCREAMING_SNAKE_CASE__   =   torch.zeros((1, TARGET_FEATURE_LENGTH)     ,       dtype=__lowerCamelCase     ,       device=self.device )
            else:
                 # The full song pipeline does not feed in a context feature, so the mask
                 # will be all 0s after the feature converter. Because we know we're
                 # feeding in a full context chunk from the previous prediction, set it
                 # to all 1s.
                 SCREAMING_SNAKE_CASE__   =   ones

            SCREAMING_SNAKE_CASE__   =   self.scale_features(
                __lowerCamelCase     ,       output_range=[-1.0, 1.0]     ,       clip=__lowerCamelCase )

            SCREAMING_SNAKE_CASE__   =   self.encode(
                input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )     ,       continuous_inputs=__lowerCamelCase     ,       continuous_mask=__lowerCamelCase     ,       )

            # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
            SCREAMING_SNAKE_CASE__   =   randn_tensor(
                shape=encoder_continuous_inputs.shape     ,       generator=__lowerCamelCase     ,       device=self.device     ,       dtype=self.decoder.dtype     ,       )

            # set step values
            self.scheduler.set_timesteps(__lowerCamelCase )

            # Denoising diffusion loop
            for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                 SCREAMING_SNAKE_CASE__   =   self.decode(
                     encodings_and_masks=__lowerCamelCase     ,       input_tokens=__lowerCamelCase     ,       noise_time=t / self.scheduler.config.num_train_timesteps     ,       )

                 # Compute previous output: x_t -> x_t-1
                 SCREAMING_SNAKE_CASE__   =   self.scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       generator=__lowerCamelCase ).prev_sample

            SCREAMING_SNAKE_CASE__   =   self.scale_to_features(__lowerCamelCase     ,       input_range=[-1.0, 1.0] )
            SCREAMING_SNAKE_CASE__   =   mel[:1]
            SCREAMING_SNAKE_CASE__   =   mel.cpu().float().numpy()

            SCREAMING_SNAKE_CASE__   =   np.concatenate([full_pred_mel, pred_mel[:1]]     ,       axis=1 )

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                 callback(__lowerCamelCase     ,       __lowerCamelCase )

            logger.info('''Generated segment'''     ,       __lowerCamelCase )

       if output_type == "numpy" and not is_onnx_available():
            raise ValueError(
                '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
       elif output_type == "numpy" and self.melgan is None:
            raise ValueError(
                '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )

       if output_type == "numpy":
            SCREAMING_SNAKE_CASE__   =   self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
       else:
            SCREAMING_SNAKE_CASE__   =   full_pred_mel

       if not return_dict:
            return (output,)

       return AudioPipelineOutput(audios=__lowerCamelCase )





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_SCREAMING_SNAKE_CASE					:  str             =      {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1000,
    '''block_out_channels''': [32, 64],
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  Dict             =      {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''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''',
}

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

_SCREAMING_SNAKE_CASE					:  str             =      {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}
def     UpperCAmelCase_       (						_A    ):





     '''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     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=False    ):





     '''simple docstring'''




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

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

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A=None    ):





     '''simple docstring'''




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

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

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

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

     return new_checkpoint
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   torch.load(_A				,		map_location='''cpu'''    )
     SCREAMING_SNAKE_CASE__   =   {}

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

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

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

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

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

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

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

          SCREAMING_SNAKE_CASE__   =   current_channels

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

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   unet_config['''up_block_types''']

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

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

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

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

     return new_checkpoint


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Optional[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.''')

       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()
       _SCREAMING_SNAKE_CASE					:  List[str]             =      strabool(args.class_cond)

       _SCREAMING_SNAKE_CASE					:  int             =      os.path.basename(args.unet_path)
       print(F"Checkpoint: {ckpt_name}")

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

       if not args.class_cond:
              _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      None

       _SCREAMING_SNAKE_CASE					:  int             =      con_pt_to_diffuser(args.unet_path, unet_config)

       _SCREAMING_SNAKE_CASE					:  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:
              _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              _SCREAMING_SNAKE_CASE					:  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)):
              _SCREAMING_SNAKE_CASE					:  int             =      CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")

       _SCREAMING_SNAKE_CASE					:  int             =      CMStochasticIterativeScheduler(**scheduler_config)

       _SCREAMING_SNAKE_CASE					:  str             =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)


