



'''simple docstring'''

import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Tuple   				=					MobileBertTokenizer
							lowerCAmelCase       :    Any   				=					MobileBertTokenizerFast
							lowerCAmelCase       :    Tuple   				=					True
							lowerCAmelCase       :    Tuple   				=					True
							lowerCAmelCase       :    List[str]   				=					filter_non_english
							lowerCAmelCase       :    Tuple   				=					'google/mobilebert-uncased'







							def 							__lowercase			(							self					:       List[str]		):
									super().setUp()

									_a     :      Any        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      int        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)

									_a     :      Optional[int]        =							[
									    (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
									    for tokenizer_def in self.tokenizers_list
									]







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       List[Any]		):
									_a     :      Union[str, Any]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Any		):
									_a     :      List[str]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      List[str]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[9, 6, 7, 12, 10, 11]		)







							def 							__lowercase			(							self					:       Dict		):
									if not self.test_rust_tokenizer:
											return

									_a     :      Dict        =							self.get_tokenizer()
									_a     :      Optional[Any]        =							self.get_rust_tokenizer()

									_a     :      List[Any]        =							'UNwant\u00E9d,running'

									_a     :      Union[str, Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      str        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Any        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      List[Any]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      List[str]        =							self.get_rust_tokenizer()
									_a     :      Tuple        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      List[Any]        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									# With lower casing
									_a     :      List[str]        =							self.get_tokenizer(do_lower_case=_UpperCAmelCase		)
									_a     :      Tuple        =							self.get_rust_tokenizer(do_lower_case=_UpperCAmelCase		)

									_a     :      int        =							'UNwant\u00E9d,running'

									_a     :      Optional[Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      int        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      List[str]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      str        =							self.get_rust_tokenizer()
									_a     :      Tuple        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      Tuple        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Any        =							BasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'		)	,['ah', '\u535A', '\u63A8', 'zz']		)







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      str        =							BasicTokenizer(do_lower_case=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '		)	,['hello', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['hello']		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Optional[int]        =							BasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['hällo', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['h\u00E9llo']		)







							def 							__lowercase			(							self					:       Any		):
									_a     :      str        =							BasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['hallo', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['hello']		)







							def 							__lowercase			(							self					:       str		):
									_a     :      List[Any]        =							BasicTokenizer(do_lower_case=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['hallo', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['hello']		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      List[str]        =							BasicTokenizer(do_lower_case=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '		)	,['HeLLo', '!', 'how', 'Are', 'yoU', '?']		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      List[str]        =							BasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['HäLLo', '!', 'how', 'Are', 'yoU', '?']		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Any        =							BasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['HaLLo', '!', 'how', 'Are', 'yoU', '?']		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      List[str]        =							BasicTokenizer(do_lower_case=_UpperCAmelCase	,never_split=['[UNK]']		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'		)	,['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      List[Any]        =							['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

									_a     :      Dict        =							{}
									for i, token in enumerate(_UpperCAmelCase		):
											_a     :      Tuple        =							i
									_a     :      Union[str, Any]        =							WordpieceTokenizer(vocab=_UpperCAmelCase	,unk_token='[UNK]'		)

									self.assertListEqual(tokenizer.tokenize(''		)	,[]		)

									self.assertListEqual(tokenizer.tokenize('unwanted running'		)	,['un', '##want', '##ed', 'runn', '##ing']		)

									self.assertListEqual(tokenizer.tokenize('unwantedX running'		)	,['[UNK]', 'runn', '##ing']		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									self.assertTrue(_is_whitespace(' '		)		)
									self.assertTrue(_is_whitespace('\t'		)		)
									self.assertTrue(_is_whitespace('\r'		)		)
									self.assertTrue(_is_whitespace('\n'		)		)
									self.assertTrue(_is_whitespace('\u00A0'		)		)

									self.assertFalse(_is_whitespace('A'		)		)
									self.assertFalse(_is_whitespace('-'		)		)







							def 							__lowercase			(							self					:       int		):
									self.assertTrue(_is_control('\u0005'		)		)

									self.assertFalse(_is_control('A'		)		)
									self.assertFalse(_is_control(' '		)		)
									self.assertFalse(_is_control('\t'		)		)
									self.assertFalse(_is_control('\r'		)		)







							def 							__lowercase			(							self					:       str		):
									self.assertTrue(_is_punctuation('-'		)		)
									self.assertTrue(_is_punctuation('$'		)		)
									self.assertTrue(_is_punctuation('`'		)		)
									self.assertTrue(_is_punctuation('.'		)		)

									self.assertFalse(_is_punctuation('A'		)		)
									self.assertFalse(_is_punctuation(' '		)		)







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							self.get_tokenizer()
									_a     :      int        =							self.get_rust_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(_UpperCAmelCase		) for t in ['Test', '\xad', 'test']]	,[['[UNK]'], [], ['[UNK]']]		)

									self.assertListEqual(
									    [rust_tokenizer.tokenize(_UpperCAmelCase		) for t in ['Test', '\xad', 'test']]	,[['[UNK]'], [], ['[UNK]']]		)







							@slow
							def 							__lowercase			(							self					:       Dict		):
									_a     :      List[Any]        =							self.tokenizer_class.from_pretrained('google/mobilebert-uncased'		)

									_a     :      Any        =							tokenizer.encode('sequence builders'	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Tuple        =							tokenizer.encode('multi-sequence build'	,add_special_tokens=_UpperCAmelCase		)

									_a     :      Optional[Any]        =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
									_a     :      Tuple        =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	,_UpperCAmelCase		)

									assert encoded_sentence == [101] + text + [102]
									assert encoded_pair == [101] + text + [102] + text_a + [102]







							def 							__lowercase			(							self					:       Optional[Any]		):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      int        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)

													_a     :      Union[str, Any]        =							F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
													_a     :      List[str]        =							tokenizer_r.encode_plus(
													    _UpperCAmelCase	,return_attention_mask=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase	,return_offsets_mapping=_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,)

													_a     :      Optional[int]        =							tokenizer_r.do_lower_case if hasattr(_UpperCAmelCase	,'do_lower_case'		) else False
													_a     :      Dict        =							(
													    [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), 'A'),
													        ((1, 2), ','),
													        ((3, 5), 'na'),
													        ((5, 6), '##ï'),
													        ((6, 8), '##ve'),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), 'Allen'),
													        ((21, 23), '##NL'),
													        ((23, 24), '##P'),
													        ((25, 33), 'sentence'),
													        ((33, 34), '.'),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													    if not do_lower_case
													    else [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), 'a'),
													        ((1, 2), ','),
													        ((3, 8), 'naive'),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), 'allen'),
													        ((21, 23), '##nl'),
													        ((23, 24), '##p'),
													        ((25, 33), 'sentence'),
													        ((33, 34), '.'),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													)

													self.assertEqual(
													    [e[1] for e in expected_results]	,tokenizer_r.convert_ids_to_tokens(tokens['input_ids']		)		)
													self.assertEqual([e[0] for e in expected_results]	,tokens['offset_mapping']		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      List[str]        =							['的', '人', '有']
									_a     :      str        =							''.join(_UpperCAmelCase		)
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      Optional[int]        =							True
													_a     :      Dict        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      Optional[int]        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)

													_a     :      Tuple        =							tokenizer_p.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
													_a     :      int        =							tokenizer_r.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)

													_a     :      int        =							tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase		)
													_a     :      Dict        =							tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase		)

													# it is expected that each Chinese character is not preceded by "##"
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

													_a     :      Any        =							False
													_a     :      Tuple        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      List[Any]        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)

													_a     :      Union[str, Any]        =							tokenizer_r.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
													_a     :      Union[str, Any]        =							tokenizer_p.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)

													_a     :      str        =							tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase		)
													_a     :      List[str]        =							tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase		)

													# it is expected that only the first Chinese character is not preceded by "##".
													_a     :      int        =							[
													    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_UpperCAmelCase		)
													]
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)









'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=1  )			->      Dict:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('.'  )[n_shave_prefix_segments:]  )
		else:
				return ".".join(path.split('.'  )[:n_shave_prefix_segments]  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Tuple:
		_a     :      Any        =							[]
		for old_item in old_list:
				_a     :      Union[str, Any]        =							old_item.replace('in_layers.0'						,			'norm1'  )
				_a     :      Optional[int]        =							new_item.replace('in_layers.2'						,			'conv1'  )

				_a     :      str        =							new_item.replace('out_layers.0'						,			'norm2'  )
				_a     :      List[str]        =							new_item.replace('out_layers.3'						,			'conv2'  )

				_a     :      str        =							new_item.replace('emb_layers.1'						,			'time_emb_proj'  )
				_a     :      Tuple        =							new_item.replace('skip_connection'						,			'conv_shortcut'  )

				_a     :      Any        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Any:
		_a     :      List[str]        =							[]
		for old_item in old_list:
				_a     :      List[Any]        =							old_item

				_a     :      Optional[int]        =							new_item.replace('norm.weight'						,			'group_norm.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('norm.bias'						,			'group_norm.bias'  )

				_a     :      Any        =							new_item.replace('proj_out.weight'						,			'proj_attn.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('proj_out.bias'						,			'proj_attn.bias'  )

				_a     :      Optional[int]        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Any:
		assert isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

		# Splits the attention layers into three variables.
		if attention_paths_to_split is not None:
				for path, path_map in attention_paths_to_split.items():
						_a     :      Optional[Any]        =							old_checkpoint[path]
						_a     :      Optional[Any]        =							old_tensor.shape[0] // 3

						_a     :      Any        =							(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

						_a     :      int        =							old_tensor.shape[0] // config['num_head_channels'] // 3

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

						_a     :      Dict        =							query.reshape(lowerCAmelCase_  )
						_a     :      str        =							key.reshape(lowerCAmelCase_  )
						_a     :      Optional[int]        =							value.reshape(lowerCAmelCase_  )

		for path in paths:
				_a     :      Dict        =							path['new']

				# These have already been assigned
				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				# Global renaming happens here
				_a     :      Any        =							new_path.replace('middle_block.0'						,			'mid_block.resnets.0'  )
				_a     :      str        =							new_path.replace('middle_block.1'						,			'mid_block.attentions.0'  )
				_a     :      Union[str, Any]        =							new_path.replace('middle_block.2'						,			'mid_block.resnets.1'  )

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

        # proj_attn.weight has to be converted from conv 1D to linear
				if "proj_attn.weight" in new_path:
						_a     :      List[str]        =							old_checkpoint[path['old']][:, :, 0]
				else:
						_a     :      Dict        =							old_checkpoint[path['old']]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Optional[int]        =							{}

		_a     :      Dict        =							checkpoint['time_embed.0.weight']
		_a     :      Tuple        =							checkpoint['time_embed.0.bias']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.2.weight']
		_a     :      List[str]        =							checkpoint['time_embed.2.bias']

		_a     :      List[str]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['input_blocks.0.0.bias']

		_a     :      Optional[int]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      List[str]        =							checkpoint['out.2.weight']
		_a     :      Optional[int]        =							checkpoint['out.2.bias']

		# Retrieves the keys for the input blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'input_blocks' in layer}  )
		_a     :      Dict        =							{
		    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the middle blocks only
		_a     :      List[Any]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'middle_block' in layer}  )
		_a     :      Union[str, Any]        =							{
		    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the output blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'output_blocks' in layer}  )
		_a     :      str        =							{
		    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		for i in range(1						,			lowerCAmelCase_  ):
				_a     :      List[Any]        =							(i - 1) // (config['num_res_blocks'] + 1)
				_a     :      Optional[int]        =							(i - 1) % (config['num_res_blocks'] + 1)

				_a     :      Optional[int]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
				_a     :      Optional[Any]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

				if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
						_a     :      List[Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.weight"""
						]
						_a     :      Union[str, Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.bias"""
						]
						continue

				_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
				_a     :      List[str]        =							{'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				_a     :      Optional[Any]        =							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
				assign_to_checkpoint(
				    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path, resnet_op]						,			config=lowerCAmelCase_  )

				if len(lowerCAmelCase_  ):
						_a     :      List[str]        =							renew_attention_paths(lowerCAmelCase_  )
						_a     :      List[Any]        =							{
						    'old': f"""input_blocks.{i}.1""",
						    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
						}
						_a     :      Optional[Any]        =							{
						    f"""input_blocks.{i}.1.qkv.bias""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
						    },
						    f"""input_blocks.{i}.1.qkv.weight""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
						    },
						}
						assign_to_checkpoint(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_						,			)

		_a     :      str        =							middle_blocks[0]
		_a     :      Tuple        =							middle_blocks[1]
		_a     :      Any        =							middle_blocks[2]

		_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      int        =							renew_attention_paths(lowerCAmelCase_  )
		_a     :      int        =							{
		    'middle_block.1.qkv.bias': {
		        'key': 'mid_block.attentions.0.key.bias',
		        'query': 'mid_block.attentions.0.query.bias',
		        'value': 'mid_block.attentions.0.value.bias',
		    },
		    'middle_block.1.qkv.weight': {
		        'key': 'mid_block.attentions.0.key.weight',
		        'query': 'mid_block.attentions.0.query.weight',
		        'value': 'mid_block.attentions.0.value.weight',
		    },
		}
		assign_to_checkpoint(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_  )

		for i in range(lowerCAmelCase_  ):
				_a     :      List[str]        =							i // (config['num_res_blocks'] + 1)
				_a     :      Any        =							i % (config['num_res_blocks'] + 1)
				_a     :      Union[str, Any]        =							[shave_segments(lowerCAmelCase_						,			2  ) for name in output_blocks[i]]
				_a     :      Optional[Any]        =							{}

				for layer in output_block_layers:
						_a       ,					_a     :      str        =							layer.split('.'  )[0], shave_segments(lowerCAmelCase_						,			1  )
						if layer_id in output_block_list:
								output_block_list[layer_id].append(lowerCAmelCase_  )
						else:
								_a     :      str        =							[layer_name]

				if len(lowerCAmelCase_  ) > 1:
						_a     :      str        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
						_a     :      Optional[Any]        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

						_a     :      Dict        =							renew_resnet_paths(lowerCAmelCase_  )
						_a     :      str        =							renew_resnet_paths(lowerCAmelCase_  )

						_a     :      Optional[int]        =							{'old': f"""output_blocks.{i}.0""", 'new': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			config=lowerCAmelCase_  )

						if ["conv.weight", "conv.bias"] in output_block_list.values():
								_a     :      List[Any]        =							list(output_block_list.values()  ).index(['conv.weight', 'conv.bias']  )
								_a     :      Tuple        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.weight"""
								]
								_a     :      List[str]        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.bias"""
								]

								# Clear attentions as they have been attributed above.
								if len(lowerCAmelCase_  ) == 2:
										_a     :      Union[str, Any]        =							[]

						if len(lowerCAmelCase_  ):
								_a     :      Tuple        =							renew_attention_paths(lowerCAmelCase_  )
								_a     :      str        =							{
								    'old': f"""output_blocks.{i}.1""",
								    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
								}
								_a     :      List[Any]        =							{
								    f"""output_blocks.{i}.1.qkv.bias""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
								    },
								    f"""output_blocks.{i}.1.qkv.weight""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
								    },
								}
								assign_to_checkpoint(
								    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=to_split if any('qkv' in key for key in attentions  ) else None						,			config=lowerCAmelCase_						,			)
				else:
						_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_						,			n_shave_prefix_segments=1  )
						for path in resnet_0_paths:
								_a     :      int        =							'.'.join(['output_blocks', str(lowerCAmelCase_  ), path['old']]  )
								_a     :      Union[str, Any]        =							'.'.join(['up_blocks', str(lowerCAmelCase_  ), 'resnets', str(lowerCAmelCase_  ), path['new']]  )

								_a     :      Union[str, Any]        =							checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
							__lowerCAmelCase									=   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.''')

							__lowerCAmelCase									=   parser.parse_args()

							__lowerCAmelCase									=   torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														__lowerCAmelCase									=   json.loads(f.read())

							__lowerCAmelCase									=   convert_ldm_checkpoint(checkpoint, config)

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

							__lowerCAmelCase									=   UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__lowerCAmelCase									=   DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
														__lowerCAmelCase									=   VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														__lowerCAmelCase									=   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)




