


import os
import re
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__    :Tuple       =				logging.get_logger(__name__)

lowerCAmelCase__    :List[Any]       =				{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__    :Optional[int]       =				{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase__    :Any       =				{
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}

class 						__a (							UpperCAmelCase     ):
		_a      :    str           =      VOCAB_FILES_NAMES
		_a      :    int           =      PRETRAINED_VOCAB_FILES_MAP
		_a      :    Union[str, Any]           =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a      :    int           =      ['input_ids', 'attention_mask']
		_a      :    List[int]           =      []
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE="<unk>"     ,       _SCREAMING_SNAKE_CASE="<s>"     ,       _SCREAMING_SNAKE_CASE="</s>"     ,       _SCREAMING_SNAKE_CASE="<pad>"     ,       _SCREAMING_SNAKE_CASE="[SEP]"     ,       _SCREAMING_SNAKE_CASE="[MASK]"     ,       _SCREAMING_SNAKE_CASE="[CLS]"     ,       _SCREAMING_SNAKE_CASE = None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			None:




									"""simple docstring"""





									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else bos_token
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else eos_token
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else unk_token
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else pad_token
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else cls_token
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else sep_token

									# Mask token behave like a normal word, i.e. include the space before it
									_UpperCAmelCase											=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) else mask_token

									_UpperCAmelCase											=     {} if sp_model_kwargs is None else sp_model_kwargs

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

									_UpperCAmelCase											=     vocab_file

									_UpperCAmelCase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(_SCREAMING_SNAKE_CASE		)
		@property
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									return self.sp_model.get_piece_size()
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab
		def __getstate__(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.__dict__.copy()
									_UpperCAmelCase											=     None
									return state
		def __setstate__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     d

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

									_UpperCAmelCase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									return self.sp_model.encode(_SCREAMING_SNAKE_CASE     ,       out_type=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE		)
									return token
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     []
									_UpperCAmelCase											=     ''
									_UpperCAmelCase											=     False
									for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																							if not prev_is_special:
																														out_string += " "
																							out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		) + token
																							_UpperCAmelCase											=     True
																							_UpperCAmelCase											=     []
																else:
																							current_sub_tokens.append(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase											=     False
									out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		)
									return out_string.strip()
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = True     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     kwargs.pop('use_source_tokenizer'     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE     ,       skip_special_tokens=_SCREAMING_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											=     []
									_UpperCAmelCase											=     []
									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(_SCREAMING_SNAKE_CASE		)		)
																														_UpperCAmelCase											=     []
																							sub_texts.append(_SCREAMING_SNAKE_CASE		)
																else:
																							current_sub_text.append(_SCREAMING_SNAKE_CASE		)
									if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
																_UpperCAmelCase											=     re.sub(R' (\[(MASK|SEP)\])'     ,       R'\1'     ,       ' '.join(_SCREAMING_SNAKE_CASE		)		)
									else:
																_UpperCAmelCase											=     ''.join(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     (
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
																_UpperCAmelCase											=     self.clean_up_tokenization(_SCREAMING_SNAKE_CASE		)
																return clean_text
									else:
																return text
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			Tuple[str]:




									"""simple docstring"""





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

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

									return (out_vocab_file,)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[int]:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase											=     [self.cls_token_id]
									_UpperCAmelCase											=     [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False		)					->			List[int]:




									"""simple docstring"""





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

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






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     [self.sep_token_id]
									_UpperCAmelCase											=     [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		) * [0] + len(token_ids_a + sep		) * [1]




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


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

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

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

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

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

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

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


							'''simple docstring'''





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

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


							'''simple docstring'''





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

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

							return new_checkpoint

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


							'''simple docstring'''





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

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

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

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

							return new_checkpoint

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


							'''simple docstring'''





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

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

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

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

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

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

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

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

														_UpperCAmelCase											=     current_channels

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

							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     unet_config['up_block_types']

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

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

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

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

							return new_checkpoint


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

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

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

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

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

		if not args.class_cond:
				lowerCAmelCase__    :str       =				None

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

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

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

		lowerCAmelCase__    :Any       =				CMStochasticIterativeScheduler(**scheduler_config)

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