

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		"""▁"""

lowerCAmelCase__								=		{"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCAmelCase__								=		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCAmelCase__								=		{
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  VOCAB_FILES_NAMES
		a__					       =  PRETRAINED_VOCAB_FILES_MAP
		a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__					       =  ["input_ids", "attention_mask"]


		def __init__(				self	,       __snake_case	,       __snake_case="<s>"	,       __snake_case="</s>"	,       __snake_case="</s>"	,       __snake_case="<s>"	,       __snake_case="<unk>"	,       __snake_case="<pad>"	,       __snake_case="<mask>"	,       __snake_case = None	,       **__snake_case	,       ):
			# Mask token behave like a normal word, i.e. include the space before it
			_UpperCamelCase   :		Dict			=						AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case) if isinstance(__snake_case	,       __snake_case) else mask_token

			_UpperCamelCase   :		str			=						{} if sp_model_kwargs is None else sp_model_kwargs

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

			_UpperCamelCase   :		Union[str, Any]			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(str(__snake_case))
			_UpperCamelCase   :		Dict			=						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'

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

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			_UpperCamelCase   :		List[Any]			=						1

			_UpperCamelCase   :		Any			=						len(self.sp_model) + self.fairseq_offset
			_UpperCamelCase   :		List[str]			=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


		def __getstate__(				self):
			_UpperCamelCase   :		List[Any]			=						self.__dict__.copy()
			_UpperCamelCase   :		Optional[Any]			=						None
			_UpperCamelCase   :		Any			=						self.sp_model.serialized_model_proto()
			return state


		def __setstate__(				self	,       __snake_case):
			_UpperCamelCase   :		int			=						d

			# for backward compatibility
			if not hasattr(self	,       'sp_model_kwargs'):
				_UpperCamelCase   :		Tuple			=						{}

			_UpperCamelCase   :		Optional[int]			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto)


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):

			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			_UpperCamelCase   :		Tuple			=						[self.cls_token_id]
			_UpperCamelCase   :		int			=						[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep


		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = False):

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

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


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Optional[Any]			=						[self.sep_token_id]
			_UpperCamelCase   :		Optional[Any]			=						[self.cls_token_id]

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


		@property
		def 		A__					(				self):
			return len(self.sp_model) + self.fairseq_offset + 1  # Add the <mask> token


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						{self.convert_ids_to_tokens(__snake_case): i for i in range(self.vocab_size)}
			vocab.update(self.added_tokens_encoder)
			return vocab


		def 		A__					(				self	,       __snake_case):
			return self.sp_model.encode(__snake_case	,       out_type=__snake_case)


		def 		A__					(				self	,       __snake_case):
			if token in self.fairseq_tokens_to_ids:
				return self.fairseq_tokens_to_ids[token]
			_UpperCamelCase   :		str			=						self.sp_model.PieceToId(__snake_case)

			# 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 		A__					(				self	,       __snake_case):
			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 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Optional[int]			=						''.join(__snake_case).replace(__snake_case	,       ' ').strip()
			return out_string






		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			if not os.path.isdir(__snake_case):
				logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
				return
			_UpperCamelCase   :		str			=						os.path.join(
			    __snake_case	,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

			if os.path.abspath(self.vocab_file) != os.path.abspath(__snake_case) and os.path.isfile(self.vocab_file):
				copyfile(self.vocab_file	,       __snake_case)
			elif not os.path.isfile(self.vocab_file):
				with open(__snake_case	,       'wb') as fi:
					_UpperCamelCase   :		Any			=						self.sp_model.serialized_model_proto()
					fi.write(__snake_case)

			return (out_vocab_file,)







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__								=		{
    """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__								=		{
    """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__								=		{
    """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__								=		{
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

lowerCAmelCase__								=		{
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

lowerCAmelCase__								=		{
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				List[str]:




	'''simple docstring'''




	if isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
		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_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Union[str, Any]=False	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.in_layers.0.weight''']
	_UpperCamelCase   :		str			=						checkpoint[F'''{old_prefix}.in_layers.0.bias''']
	_UpperCamelCase   :		str			=						checkpoint[F'''{old_prefix}.in_layers.2.weight''']
	_UpperCamelCase   :		Union[str, Any]			=						checkpoint[F'''{old_prefix}.in_layers.2.bias''']
	_UpperCamelCase   :		Dict			=						checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
	_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
	_UpperCamelCase   :		Tuple			=						checkpoint[F'''{old_prefix}.out_layers.0.weight''']
	_UpperCamelCase   :		List[Any]			=						checkpoint[F'''{old_prefix}.out_layers.0.bias''']
	_UpperCamelCase   :		Optional[Any]			=						checkpoint[F'''{old_prefix}.out_layers.3.weight''']
	_UpperCamelCase   :		Union[str, Any]			=						checkpoint[F'''{old_prefix}.out_layers.3.bias''']

	if has_skip:
		_UpperCamelCase   :		Tuple			=						checkpoint[F'''{old_prefix}.skip_connection.weight''']
		_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.skip_connection.bias''']

	return new_checkpoint






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Any=None	)						->				int:




	'''simple docstring'''




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

	_UpperCamelCase   :		Dict			=						checkpoint[F'''{old_prefix}.norm.weight''']
	_UpperCamelCase   :		Optional[int]			=						checkpoint[F'''{old_prefix}.norm.bias''']

	_UpperCamelCase   :		List[str]			=						weight_q.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Dict			=						bias_q.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Any			=						weight_k.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		List[Any]			=						bias_k.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Dict			=						weight_v.squeeze(-1	).squeeze(-1	)
	_UpperCamelCase   :		Tuple			=						bias_v.squeeze(-1	).squeeze(-1	)

	_UpperCamelCase   :		Optional[Any]			=						(
	    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1	).squeeze(-1	)
	)
	_UpperCamelCase   :		Dict			=						checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1	).squeeze(-1	)

	return new_checkpoint






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Optional[int]	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		Union[str, Any]			=						{}

	_UpperCamelCase   :		Optional[int]			=						checkpoint['time_embed.0.weight']
	_UpperCamelCase   :		List[Any]			=						checkpoint['time_embed.0.bias']
	_UpperCamelCase   :		Dict			=						checkpoint['time_embed.2.weight']
	_UpperCamelCase   :		Optional[Any]			=						checkpoint['time_embed.2.bias']

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

	_UpperCamelCase   :		Optional[int]			=						checkpoint['input_blocks.0.0.weight']
	_UpperCamelCase   :		Union[str, Any]			=						checkpoint['input_blocks.0.0.bias']

	_UpperCamelCase   :		Optional[int]			=						unet_config['down_block_types']
	_UpperCamelCase   :		Optional[Any]			=						unet_config['layers_per_block']
	_UpperCamelCase   :		Dict			=						unet_config['attention_head_dim']
	_UpperCamelCase   :		List[str]			=						unet_config['block_out_channels']
	_UpperCamelCase   :		str			=						1
	_UpperCamelCase   :		Optional[int]			=						channels_list[0]

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

		elif layer_type == "AttnDownBlock2D":
			for j in range(UpperCAmelCase_	):
				_UpperCamelCase   :		List[str]			=						F'''down_blocks.{i}.resnets.{j}'''
				_UpperCamelCase   :		str			=						F'''input_blocks.{current_layer}.0'''
				_UpperCamelCase   :		int			=						True if j == 0 and downsample_block_has_skip else False
				_UpperCamelCase   :		Any			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       has_skip=UpperCAmelCase_	)
				_UpperCamelCase   :		Dict			=						F'''down_blocks.{i}.attentions.{j}'''
				_UpperCamelCase   :		Optional[int]			=						F'''input_blocks.{current_layer}.1'''
				_UpperCamelCase   :		Dict			=						convert_attention(
				    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
				current_layer += 1

		if i != len(UpperCAmelCase_	) - 1:
			_UpperCamelCase   :		int			=						F'''down_blocks.{i}.downsamplers.0'''
			_UpperCamelCase   :		Optional[int]			=						F'''input_blocks.{current_layer}.0'''
			_UpperCamelCase   :		List[Any]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
			current_layer += 1

		_UpperCamelCase   :		Tuple			=						current_channels

	# hardcoded the mid-block for now
	_UpperCamelCase   :		Any			=						'mid_block.resnets.0'
	_UpperCamelCase   :		Optional[Any]			=						'middle_block.0'
	_UpperCamelCase   :		Tuple			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[Any]			=						'mid_block.attentions.0'
	_UpperCamelCase   :		Tuple			=						'middle_block.1'
	_UpperCamelCase   :		Union[str, Any]			=						convert_attention(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	_UpperCamelCase   :		Tuple			=						'mid_block.resnets.1'
	_UpperCamelCase   :		str			=						'middle_block.2'
	_UpperCamelCase   :		List[str]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	_UpperCamelCase   :		List[Any]			=						0
	_UpperCamelCase   :		Optional[int]			=						unet_config['up_block_types']

	for i, layer_type in enumerate(UpperCAmelCase_	):
		if layer_type == "ResnetUpsampleBlock2D":
			for j in range(layers_per_block + 1	):
				_UpperCamelCase   :		Optional[Any]			=						F'''up_blocks.{i}.resnets.{j}'''
				_UpperCamelCase   :		Optional[int]			=						F'''output_blocks.{current_layer}.0'''
				_UpperCamelCase   :		str			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       has_skip=UpperCAmelCase_	)
				current_layer += 1

			if i != len(UpperCAmelCase_	) - 1:
				_UpperCamelCase   :		List[Any]			=						F'''up_blocks.{i}.upsamplers.0'''
				_UpperCamelCase   :		Dict			=						F'''output_blocks.{current_layer-1}.1'''
				_UpperCamelCase   :		Optional[int]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
		elif layer_type == "AttnUpBlock2D":
			for j in range(layers_per_block + 1	):
				_UpperCamelCase   :		str			=						F'''up_blocks.{i}.resnets.{j}'''
				_UpperCamelCase   :		Union[str, Any]			=						F'''output_blocks.{current_layer}.0'''
				_UpperCamelCase   :		Optional[int]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       has_skip=UpperCAmelCase_	)
				_UpperCamelCase   :		int			=						F'''up_blocks.{i}.attentions.{j}'''
				_UpperCamelCase   :		List[Any]			=						F'''output_blocks.{current_layer}.1'''
				_UpperCamelCase   :		Optional[int]			=						convert_attention(
				    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
				current_layer += 1

			if i != len(UpperCAmelCase_	) - 1:
				_UpperCamelCase   :		List[Any]			=						F'''up_blocks.{i}.upsamplers.0'''
				_UpperCamelCase   :		Union[str, Any]			=						F'''output_blocks.{current_layer-1}.2'''
				_UpperCamelCase   :		List[str]			=						convert_resnet(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	_UpperCamelCase   :		List[Any]			=						checkpoint['out.0.weight']
	_UpperCamelCase   :		str			=						checkpoint['out.0.bias']
	_UpperCamelCase   :		int			=						checkpoint['out.2.weight']
	_UpperCamelCase   :		List[Any]			=						checkpoint['out.2.bias']

	return new_checkpoint


if __name__ == "__main__":
							lowerCAmelCase__								=		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__								=		parser.parse_args()
							lowerCAmelCase__								=		strabool(args.class_cond)

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

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

							if not args.class_cond:
														lowerCAmelCase__								=		None

							lowerCAmelCase__								=		con_pt_to_diffuser(args.unet_path, unet_config)

							lowerCAmelCase__								=		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__								=		CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														lowerCAmelCase__								=		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__								=		CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

							lowerCAmelCase__								=		CMStochasticIterativeScheduler(**scheduler_config)

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




