

import os
import unicodedata
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 SPIECE_UNDERLINE, logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{"""vocab_file""": """spiece.model"""}

lowerCAmelCase__								=		{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

lowerCAmelCase__								=		{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
lowerCAmelCase__								=		0
lowerCAmelCase__								=		1
lowerCAmelCase__								=		2
lowerCAmelCase__								=		3
lowerCAmelCase__								=		4







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  VOCAB_FILES_NAMES
		a__					       =  PRETRAINED_VOCAB_FILES_MAP
		a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__					       =  "left"


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

			_UpperCamelCase   :		Optional[Any]			=						{} if sp_model_kwargs is None else sp_model_kwargs

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

			_UpperCamelCase   :		Any			=						3

			_UpperCamelCase   :		List[Any]			=						do_lower_case
			_UpperCamelCase   :		int			=						remove_space
			_UpperCamelCase   :		Union[str, Any]			=						keep_accents
			_UpperCamelCase   :		List[str]			=						vocab_file

			_UpperCamelCase   :		Tuple			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(__snake_case)


		@property
		def 		A__					(				self):
			return len(self.sp_model)


		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 __getstate__(				self):
			_UpperCamelCase   :		Dict			=						self.__dict__.copy()
			_UpperCamelCase   :		str			=						None
			return state


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

			# for backward compatibility
			if not hasattr(self	,       'sp_model_kwargs'):
				_UpperCamelCase   :		Optional[Any]			=						{}

			_UpperCamelCase   :		Tuple			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(self.vocab_file)


		def 		A__					(				self	,       __snake_case):
			if self.remove_space:
				_UpperCamelCase   :		Any			=						' '.join(inputs.strip().split())
			else:
				_UpperCamelCase   :		Optional[Any]			=						inputs
			_UpperCamelCase   :		str			=						outputs.replace('``'	,       '"').replace('\'\''	,       '"')

			if not self.keep_accents:
				_UpperCamelCase   :		List[str]			=						unicodedata.normalize('NFKD'	,       __snake_case)
				_UpperCamelCase   :		Optional[int]			=						''.join([c for c in outputs if not unicodedata.combining(__snake_case)])
			if self.do_lower_case:
				_UpperCamelCase   :		Dict			=						outputs.lower()

			return outputs


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Any			=						self.preprocess_text(__snake_case)
			_UpperCamelCase   :		Dict			=						self.sp_model.encode(__snake_case	,       out_type=__snake_case)
			_UpperCamelCase   :		List[Any]			=						[]
			for piece in pieces:
				if len(__snake_case) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
					_UpperCamelCase   :		List[Any]			=						self.sp_model.EncodeAsPieces(piece[:-1].replace(__snake_case	,       ''))
					if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
						if len(cur_pieces[0]) == 1:
							_UpperCamelCase   :		int			=						cur_pieces[1:]
						else:
							_UpperCamelCase   :		Optional[Any]			=						cur_pieces[0][1:]
					cur_pieces.append(piece[-1])
					new_pieces.extend(__snake_case)
				else:
					new_pieces.append(__snake_case)

			return new_pieces


		def 		A__					(				self	,       __snake_case):
			return self.sp_model.PieceToId(__snake_case)


		def 		A__					(				self	,       __snake_case):
			return self.sp_model.IdToPiece(__snake_case)


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


		def 		A__					(				self	,       __snake_case	,       __snake_case = False	,       __snake_case = None	,       __snake_case = True	,       **__snake_case	,       ):
			_UpperCamelCase   :		List[Any]			=						kwargs.pop('use_source_tokenizer'	,       __snake_case)

			_UpperCamelCase   :		Union[str, Any]			=						self.convert_ids_to_tokens(__snake_case	,       skip_special_tokens=__snake_case)

			# To avoid mixing byte-level and unicode for byte-level BPT
			# we need to build string separately for added tokens and byte-level tokens
			# cf. https://github.com/huggingface/transformers/issues/1133
			_UpperCamelCase   :		Optional[int]			=						[]
			_UpperCamelCase   :		Dict			=						[]
			for token in filtered_tokens:
				if skip_special_tokens and token in self.all_special_ids:
					continue
				if token in self.added_tokens_encoder:
					if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(__snake_case))
						_UpperCamelCase   :		List[Any]			=						[]
					sub_texts.append(__snake_case)
				else:
					current_sub_text.append(__snake_case)
			if current_sub_text:
				sub_texts.append(self.convert_tokens_to_string(__snake_case))

			# Mimic the behavior of the Rust tokenizer:
			# By default, there are no spaces between special tokens
			_UpperCamelCase   :		Optional[Any]			=						''.join(__snake_case)

			_UpperCamelCase   :		Any			=						(
			    clean_up_tokenization_spaces
			    if clean_up_tokenization_spaces is not None
			    else self.clean_up_tokenization_spaces
			)
			if clean_up_tokenization_spaces:
				_UpperCamelCase   :		int			=						self.clean_up_tokenization(__snake_case)
				return clean_text
			else:
				return text


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Optional[Any]			=						[self.sep_token_id]
			_UpperCamelCase   :		Optional[int]			=						[self.cls_token_id]
			if token_ids_a is None:
				return token_ids_a + sep + cls
			return token_ids_a + sep + token_ids_a + sep + cls


		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 not None:
				return ([0] * len(__snake_case)) + [1] + ([0] * len(__snake_case)) + [1, 1]
			return ([0] * len(__snake_case)) + [1, 1]


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Optional[Any]			=						[self.sep_token_id]
			_UpperCamelCase   :		Any			=						[2]

			if token_ids_a is None:
				return len(token_ids_a + sep) * [0] + cls_segment_id
			return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id






		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   :		Tuple			=						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   :		List[str]			=						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)




