






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


__snake_case     :     Union[str, Any]       =  logging.get_logger(__name__)


__snake_case     :     Any       =  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__snake_case     :     int       =  {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

__snake_case     :     Dict       =  {
    """RUCAIBox/mvp""": 10_24,
}







@lru_cache()
def 						_UpperCamelCase (     )	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    (
						    list(range(ord('!'		)     ,							ord('~'		) + 1		)		) + list(range(ord('¡'		)     ,							ord('¬'		) + 1		)		) + list(range(ord('®'		)     ,							ord('ÿ'		) + 1		)		)
						)
						lowerCAmelCase__         =    bs[:]
						lowerCAmelCase__         =    0
						for b in range(2**8		):
												if b not in bs:
																		bs.append(UpperCamelCase_		)
																		cs.append(2**8 + n		)
																		n += 1
						lowerCAmelCase__         =    [chr(UpperCamelCase_		) for n in cs]
						return dict(zip(UpperCamelCase_     ,							UpperCamelCase_		)		)







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple		)	-> Tuple:

						"""simple docstring"""



						lowerCAmelCase__         =    set()
						lowerCAmelCase__         =    word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												lowerCAmelCase__         =    char
						return pairs


class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Dict				=       VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE						:							int				=       PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE						:							Any				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE						:							Tuple				=       ['''input_ids''', '''attention_mask''']






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase="replace"  ,    _UpperCamelCase="<s>"  ,    _UpperCamelCase="</s>"  ,    _UpperCamelCase="</s>"  ,    _UpperCamelCase="<s>"  ,    _UpperCamelCase="<unk>"  ,    _UpperCamelCase="<pad>"  ,    _UpperCamelCase="<mask>"  ,    _UpperCamelCase=False  ,    **_UpperCamelCase  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    AddedToken(_UpperCamelCase  ,    lstrip=_UpperCamelCase  ,    rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase  ,    _UpperCamelCase ) else bos_token
												lowerCAmelCase__         =    AddedToken(_UpperCamelCase  ,    lstrip=_UpperCamelCase  ,    rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase  ,    _UpperCamelCase ) else eos_token
												lowerCAmelCase__         =    AddedToken(_UpperCamelCase  ,    lstrip=_UpperCamelCase  ,    rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase  ,    _UpperCamelCase ) else sep_token
												lowerCAmelCase__         =    AddedToken(_UpperCamelCase  ,    lstrip=_UpperCamelCase  ,    rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase  ,    _UpperCamelCase ) else cls_token
												lowerCAmelCase__         =    AddedToken(_UpperCamelCase  ,    lstrip=_UpperCamelCase  ,    rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase  ,    _UpperCamelCase ) else unk_token
												lowerCAmelCase__         =    AddedToken(_UpperCamelCase  ,    lstrip=_UpperCamelCase  ,    rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase  ,    _UpperCamelCase ) else pad_token

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

												super().__init__(
												    errors=_UpperCamelCase  ,    bos_token=_UpperCamelCase  ,    eos_token=_UpperCamelCase  ,    unk_token=_UpperCamelCase  ,    sep_token=_UpperCamelCase  ,    cls_token=_UpperCamelCase  ,    pad_token=_UpperCamelCase  ,    mask_token=_UpperCamelCase  ,    add_prefix_space=_UpperCamelCase  ,    **_UpperCamelCase  ,    )
												with open(_UpperCamelCase  ,    encoding='utf-8' ) as vocab_handle:
																		lowerCAmelCase__         =    json.load(_UpperCamelCase )
												lowerCAmelCase__         =    {v: k for k, v in self.encoder.items()}
												lowerCAmelCase__         =    errors  # how to handle errors in decoding
												lowerCAmelCase__         =    bytes_to_unicode()
												lowerCAmelCase__         =    {v: k for k, v in self.byte_encoder.items()}
												with open(_UpperCamelCase  ,    encoding='utf-8' ) as merges_handle:
																		lowerCAmelCase__         =    merges_handle.read().split('\n' )[1:-1]
												lowerCAmelCase__         =    [tuple(merge.split() ) for merge in bpe_merges]
												lowerCAmelCase__         =    dict(zip(_UpperCamelCase  ,    range(len(_UpperCamelCase ) ) ) )
												lowerCAmelCase__         =    {}
												lowerCAmelCase__         =    add_prefix_space

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												lowerCAmelCase__         =    re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return len(self.encoder )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return dict(self.encoder  ,    **self.added_tokens_encoder )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												if token in self.cache:
																		return self.cache[token]
												lowerCAmelCase__         =    tuple(_UpperCamelCase )
												lowerCAmelCase__         =    get_pairs(_UpperCamelCase )

												if not pairs:
																		return token

												while True:
																		lowerCAmelCase__         =    min(_UpperCamelCase  ,    key=lambda _UpperCamelCase : self.bpe_ranks.get(_UpperCamelCase  ,    float('inf' ) ) )
																		if bigram not in self.bpe_ranks:
																								break
																		lowerCAmelCase__    ,							lowerCAmelCase__         =    bigram
																		lowerCAmelCase__         =    []
																		lowerCAmelCase__         =    0
																		while i < len(_UpperCamelCase ):
																								try:
																														lowerCAmelCase__         =    word.index(_UpperCamelCase  ,    _UpperCamelCase )
																								except ValueError:
																														new_word.extend(word[i:] )
																														break
																								else:
																														new_word.extend(word[i:j] )
																														lowerCAmelCase__         =    j

																								if word[i] == first and i < len(_UpperCamelCase ) - 1 and word[i + 1] == second:
																														new_word.append(first + second )
																														i += 2
																								else:
																														new_word.append(word[i] )
																														i += 1
																		lowerCAmelCase__         =    tuple(_UpperCamelCase )
																		lowerCAmelCase__         =    new_word
																		if len(_UpperCamelCase ) == 1:
																								break
																		else:
																								lowerCAmelCase__         =    get_pairs(_UpperCamelCase )
												lowerCAmelCase__         =    ' '.join(_UpperCamelCase )
												lowerCAmelCase__         =    word
												return word






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    []
												for token in re.findall(self.pat  ,    _UpperCamelCase ):
																		lowerCAmelCase__         =    ''.join(
																		    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																		bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCamelCase ).split(' ' ) )
												return bpe_tokens






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												return self.encoder.get(_UpperCamelCase  ,    self.encoder.get(self.unk_token ) )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												return self.decoder.get(_UpperCamelCase )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    ''.join(_UpperCamelCase )
												lowerCAmelCase__         =    bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'  ,    errors=self.errors )
												return text






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = None ):






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

												with open(_UpperCamelCase  ,    'w'  ,    encoding='utf-8' ) as f:
																		f.write(json.dumps(self.encoder  ,    indent=2  ,    sort_keys=_UpperCamelCase  ,    ensure_ascii=_UpperCamelCase ) + '\n' )

												lowerCAmelCase__         =    0
												with open(_UpperCamelCase  ,    'w'  ,    encoding='utf-8' ) as writer:
																		writer.write('#version: 0.2\n' )
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,    key=lambda _UpperCamelCase : kv[1] ):
																								if index != token_index:
																														logger.warning(
																														    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																														    ' Please check that the tokenizer is not corrupted!' )
																														lowerCAmelCase__         =    token_index
																								writer.write(' '.join(_UpperCamelCase ) + '\n' )
																								index += 1

												return vocab_file, merge_file






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = None ):






												"""simple docstring"""
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCAmelCase__         =    [self.cls_token_id]
												lowerCAmelCase__         =    [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = None  ,    _UpperCamelCase = 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 )) + [1]
												return [1] + ([0] * len(_UpperCamelCase )) + [1, 1] + ([0] * len(_UpperCamelCase )) + [1]






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase = None ):






												"""simple docstring"""
												lowerCAmelCase__         =    [self.sep_token_id]
												lowerCAmelCase__         =    [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]







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase=False  ,    **_UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    kwargs.pop('add_prefix_space'  ,    self.add_prefix_space )
												if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase ) > 0 and not text[0].isspace()):
																		lowerCAmelCase__         =    ' ' + text
												return (text, kwargs)













import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       Optional[Any]=1		)	-> Optional[int]:

						"""simple docstring"""



						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
						else:
												return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int=0		)	-> Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item
												lowerCAmelCase__         =    new_item.replace('block.'     ,							'resnets.'		)
												lowerCAmelCase__         =    new_item.replace('conv_shorcut'     ,							'conv1'		)
												lowerCAmelCase__         =    new_item.replace('in_shortcut'     ,							'conv_shortcut'		)
												lowerCAmelCase__         =    new_item.replace('temb_proj'     ,							'time_emb_proj'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)

												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Union[str, Any]=0     ,							UpperCamelCase_							:       Dict=False		)	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []
						for old_item in old_list:
												lowerCAmelCase__         =    old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		lowerCAmelCase__         =    new_item.replace('attn'     ,							'attentions'		)
												lowerCAmelCase__         =    new_item.replace('.k.'     ,							'.key.'		)
												lowerCAmelCase__         =    new_item.replace('.v.'     ,							'.value.'		)
												lowerCAmelCase__         =    new_item.replace('.q.'     ,							'.query.'		)

												lowerCAmelCase__         =    new_item.replace('proj_out'     ,							'proj_attn'		)
												lowerCAmelCase__         =    new_item.replace('norm'     ,							'group_norm'		)

												lowerCAmelCase__         =    shave_segments(UpperCamelCase_     ,							n_shave_prefix_segments=UpperCamelCase_		)
												mapping.append({'old': old_item, 'new': new_item}		)

						return mapping







def 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       str=None     ,							UpperCamelCase_							:       str=None		)	-> Dict:

						"""simple docstring"""



						assert isinstance(UpperCamelCase_     ,							UpperCamelCase_		), "Paths should be a list of dicts containing 'old' and 'new' keys."

						if attention_paths_to_split is not None:
												if config is None:
																		raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'		)

												for path, path_map in attention_paths_to_split.items():
																		lowerCAmelCase__         =    old_checkpoint[path]
																		lowerCAmelCase__         =    old_tensor.shape[0] // 3

																		lowerCAmelCase__         =    (-1, channels) if len(old_tensor.shape		) == 3 else (-1)

																		lowerCAmelCase__         =    old_tensor.shape[0] // config.get('num_head_channels'     ,							1		) // 3

																		lowerCAmelCase__         =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
																		lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    old_tensor.split(channels // num_heads     ,							dim=1		)

																		lowerCAmelCase__         =    query.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    key.reshape(UpperCamelCase_		).squeeze()
																		lowerCAmelCase__         =    value.reshape(UpperCamelCase_		).squeeze()

						for path in paths:
												lowerCAmelCase__         =    path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase__         =    new_path.replace('down.'     ,							'down_blocks.'		)
												lowerCAmelCase__         =    new_path.replace('up.'     ,							'up_blocks.'		)

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								lowerCAmelCase__         =    new_path.replace(replacement['old']     ,							replacement['new']		)

												if "attentions" in new_path:
																		lowerCAmelCase__         =    old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase__         =    old_checkpoint[path['old']]







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Dict		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['temb.dense.0.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.0.bias']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.weight']
						lowerCAmelCase__         =    checkpoint['temb.dense.1.bias']

						lowerCAmelCase__         =    checkpoint['norm_out.weight']
						lowerCAmelCase__         =    checkpoint['norm_out.bias']

						lowerCAmelCase__         =    checkpoint['conv_in.weight']
						lowerCAmelCase__         =    checkpoint['conv_in.bias']
						lowerCAmelCase__         =    checkpoint['conv_out.weight']
						lowerCAmelCase__         =    checkpoint['conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"down.{i}.downsample.op.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[F"down.{i}.downsample.op.bias"]
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

												if any('block' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,							)

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"up.{i}.upsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[F"up.{i}.upsample.conv.bias"]

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

						lowerCAmelCase__         =    {k.replace('mid_new_2'     ,							'mid_block'		): v for k, v in new_checkpoint.items()}
						return new_checkpoint







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       int		)	-> Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__         =    {}

						lowerCAmelCase__         =    checkpoint['encoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['encoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['encoder.conv_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.norm_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.norm_out.bias']

						lowerCAmelCase__         =    checkpoint['decoder.conv_in.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_in.bias']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.weight']
						lowerCAmelCase__         =    checkpoint['decoder.conv_out.bias']

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'down' in layer}		)
						lowerCAmelCase__         =    {
						    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
						}

						lowerCAmelCase__         =    len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
						lowerCAmelCase__         =    {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)}

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"encoder.down.{i}.downsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[
																		    F"encoder.down.{i}.downsample.conv.bias"
																		]

												if any('block' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)

												if any('attn' in layer for layer in down_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																		    for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']		):
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							config=UpperCamelCase_		)

						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase__         =    [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,							)

						lowerCAmelCase__         =    renew_resnet_paths(UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,							)

						lowerCAmelCase__         =    renew_attention_paths(UpperCamelCase_     ,							in_mid=UpperCamelCase_		)
						assign_to_checkpoint(
						    UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,							)

						for i in range(UpperCamelCase_		):
												lowerCAmelCase__         =    num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    checkpoint[
																		    F"decoder.up.{i}.upsample.conv.weight"
																		]
																		lowerCAmelCase__         =    checkpoint[
																		    F"decoder.up.{i}.upsample.conv.bias"
																		]

												if any('block' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_resnet_paths(blocks[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

												if any('attn' in layer for layer in up_blocks[i]		):
																		lowerCAmelCase__         =    len(
																		    {'.'.join(shave_segments(UpperCamelCase_     ,							3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'attn' in layer}		)
																		lowerCAmelCase__         =    {
																		    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase_		)
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1		):
																														lowerCAmelCase__         =    {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
																														lowerCAmelCase__         =    renew_attention_paths(attns[j]		)
																														assign_to_checkpoint(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							additional_replacements=[replace_indices]		)

						lowerCAmelCase__         =    {k.replace('mid_new_2'     ,							'mid_block'		): v for k, v in new_checkpoint.items()}
						lowerCAmelCase__         =    checkpoint['quant_conv.weight']
						lowerCAmelCase__         =    checkpoint['quant_conv.bias']
						if "quantize.embedding.weight" in checkpoint:
												lowerCAmelCase__         =    checkpoint['quantize.embedding.weight']
						lowerCAmelCase__         =    checkpoint['post_quant_conv.weight']
						lowerCAmelCase__         =    checkpoint['post_quant_conv.bias']

						return new_checkpoint


if __name__ == "__main__":
					__snake_case     :     Tuple       =  argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)

					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the architecture.""",
					)

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					__snake_case     :     str       =  parser.parse_args()
					__snake_case     :     Any       =  torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__snake_case     :     Optional[int]       =  json.loads(f.read())

					# unet case
					__snake_case     :     Any       =  {key.split(""".""")[0] for key in checkpoint.keys()}
					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
										__snake_case     :     Optional[int]       =  convert_vq_autoenc_checkpoint(checkpoint, config)
					else:
										__snake_case     :     List[str]       =  convert_ddpm_checkpoint(checkpoint, config)

					if "ddpm" in config:
										del config["ddpm"]

					if config["_class_name"] == "VQModel":
										__snake_case     :     List[str]       =  VQModel(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					elif config["_class_name"] == "AutoencoderKL":
										__snake_case     :     List[Any]       =  AutoencoderKL(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					else:
										__snake_case     :     Any       =  UNetaDModel(**config)
										model.load_state_dict(converted_checkpoint)

										__snake_case     :     List[str]       =  DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

										__snake_case     :     Tuple       =  DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)





