






"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     '''▁'''

UpperCamelCase			    =     {'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCamelCase			    =     {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

UpperCamelCase			    =     {
    '''facebook/xglm-564M''': 2_048,
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							VOCAB_FILES_NAMES
			__snake_case    :				List[str]									=							PRETRAINED_VOCAB_FILES_MAP
			__snake_case    :				List[str]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case    :				Any									=							["input_ids", "attention_mask"]







			def __init__(     self:  Optional[int]				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  Any="<s>"				,     UpperCAmelCase_:  Optional[Any]="</s>"				,     UpperCAmelCase_:  List[str]="</s>"				,     UpperCAmelCase_:  List[str]="<s>"				,     UpperCAmelCase_:  List[Any]="<unk>"				,     UpperCAmelCase_:  List[Any]="<pad>"				,     UpperCAmelCase_:  Optional[Dict[str, Any]] = None				,     **UpperCAmelCase_:  Optional[int]				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {} if sp_model_kwargs is None else sp_model_kwargs

									# Compatibility with the original tokenizer
									_SCREAMING_SNAKE_CASE     				=  7
									_SCREAMING_SNAKE_CASE     				=  [F'<madeupword{i}>' for i in range(self.num_madeup_words					)]

									_SCREAMING_SNAKE_CASE     				=  kwargs.get("""additional_special_tokens"""				,     []					)
									kwargs["additional_special_tokens"] += [
									    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    bos_token=UpperCAmelCase_				,     eos_token=UpperCAmelCase_				,     unk_token=UpperCAmelCase_				,     sep_token=UpperCAmelCase_				,     cls_token=UpperCAmelCase_				,     pad_token=UpperCAmelCase_				,     sp_model_kwargs=self.sp_model_kwargs				,     **UpperCAmelCase_				,     )

									_SCREAMING_SNAKE_CASE     				=  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(str(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									_SCREAMING_SNAKE_CASE     				=  1

									# Mimic fairseq token-to-id alignment for the first 4 token
									_SCREAMING_SNAKE_CASE     				=  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

									_SCREAMING_SNAKE_CASE     				=  len(self.sp_model					)
									_SCREAMING_SNAKE_CASE     				=  {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words					)}
									self.fairseq_tokens_to_ids.update(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def __getstate__(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.__dict__.copy()
									_SCREAMING_SNAKE_CASE     				=  None
									_SCREAMING_SNAKE_CASE     				=  self.sp_model.serialized_model_proto()
									return state







			def __setstate__(     self:  str				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  d

									# for backward compatibility
									if not hasattr(self				,     """sp_model_kwargs"""					):
															_SCREAMING_SNAKE_CASE     				=  {}

									_SCREAMING_SNAKE_CASE     				=  spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									if token_ids_a is None:
															return [self.sep_token_id] + token_ids_a
									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]
									return sep + token_ids_a + sep + sep + token_ids_a







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None				,     UpperCAmelCase_:  bool = False					):







									'''simple docstring'''




									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=UpperCAmelCase_				,     token_ids_a=UpperCAmelCase_				,     already_has_special_tokens=UpperCAmelCase_					)

									if token_ids_a is None:
															return [1] + ([0] * len(UpperCAmelCase_					))
									return [1] + ([0] * len(UpperCAmelCase_					)) + [1, 1] + ([0] * len(UpperCAmelCase_					))







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]

									if token_ids_a is None:
															return len(sep + token_ids_a					) * [0]
									return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]







			@property
			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									return len(self.sp_model					) + self.fairseq_offset + self.num_madeup_words







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {self.convert_ids_to_tokens(UpperCAmelCase_					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									return self.sp_model.encode(UpperCAmelCase_				,     out_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									_SCREAMING_SNAKE_CASE     				=  self.sp_model.PieceToId(UpperCAmelCase_					)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset					)







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """""".join(UpperCAmelCase_					).replace(UpperCAmelCase_				,     """ """					).strip()
									return out_string







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  Optional[str] = None					):







									'''simple docstring'''




									if not os.path.isdir(UpperCAmelCase_					):
															logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
															return
									_SCREAMING_SNAKE_CASE     				=  os.path.join(
									    UpperCAmelCase_				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCAmelCase_					) and os.path.isfile(self.vocab_file					):
															copyfile(self.vocab_file				,     UpperCAmelCase_					)
									elif not os.path.isfile(self.vocab_file					):
															with open(UpperCAmelCase_				,     """wb"""					) as fi:
																					_SCREAMING_SNAKE_CASE     				=  self.sp_model.serialized_model_proto()
																					fi.write(UpperCAmelCase_					)

									return (out_vocab_file,)









import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase			    =     {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''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''',
}

UpperCamelCase			    =     {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''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''',
}

UpperCamelCase			    =     {
    '''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''',
}

UpperCamelCase			    =     {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

UpperCamelCase			    =     {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

UpperCamelCase			    =     {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





						if isinstance(snake_case__	,snake_case__		):
												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      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=False		)							->  Optional[int]:






						"""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 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__=None		)							->  Optional[int]:






						"""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 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  torch.load(snake_case__	,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(snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  channels_list[i]
												_SCREAMING_SNAKE_CASE     				=  current_channels != prev_channels
												if layer_type == "ResnetDownsampleBlock2D":
																		for j in range(snake_case__		):
																								_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(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								current_layer += 1

												elif layer_type == "AttnDownBlock2D":
																		for j in range(snake_case__		):
																								_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(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.attentions.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_attention(
																								    snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																								current_layer += 1

												if i != len(snake_case__		) - 1:
																		_SCREAMING_SNAKE_CASE     				=  F'down_blocks.{i}.downsamplers.0'
																		_SCREAMING_SNAKE_CASE     				=  F'input_blocks.{current_layer}.0'
																		_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																		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(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.attentions.0"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.1"""
						_SCREAMING_SNAKE_CASE     				=  convert_attention(snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  """mid_block.resnets.1"""
						_SCREAMING_SNAKE_CASE     				=  """middle_block.2"""
						_SCREAMING_SNAKE_CASE     				=  convert_resnet(snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  0
						_SCREAMING_SNAKE_CASE     				=  unet_config["""up_block_types"""]

						for i, layer_type in enumerate(snake_case__		):
												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(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								current_layer += 1

																		if i != len(snake_case__		) - 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(snake_case__	,snake_case__	,snake_case__	,snake_case__		)
												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(snake_case__	,snake_case__	,snake_case__	,snake_case__	,has_skip=snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  F'up_blocks.{i}.attentions.{j}'
																								_SCREAMING_SNAKE_CASE     				=  F'output_blocks.{current_layer}.1'
																								_SCREAMING_SNAKE_CASE     				=  convert_attention(
																								    snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		)
																								current_layer += 1

																		if i != len(snake_case__		) - 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(snake_case__	,snake_case__	,snake_case__	,snake_case__		)

						_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__":
		UpperCamelCase			    =     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.''')

		UpperCamelCase			    =     parser.parse_args()
		UpperCamelCase			    =     strabool(args.class_cond)

		UpperCamelCase			    =     os.path.basename(args.unet_path)
		print(f"Checkpoint: {ckpt_name}")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				UpperCamelCase			    =     IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				UpperCamelCase			    =     LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				UpperCamelCase			    =     TEST_UNET_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		if not args.class_cond:
				UpperCamelCase			    =     None

		UpperCamelCase			    =     con_pt_to_diffuser(args.unet_path, unet_config)

		UpperCamelCase			    =     UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				UpperCamelCase			    =     CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				UpperCamelCase			    =     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)):
				UpperCamelCase			    =     CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		UpperCamelCase			    =     CMStochasticIterativeScheduler(**scheduler_config)

		UpperCamelCase			    =     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)

