





"""simple docstring"""




import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class 				_a		(   _lowerCAmelCase			,       unittest.TestCase       ):
				UpperCamelCase			=					RealmTokenizer
				UpperCamelCase			=					RealmTokenizerFast
				UpperCamelCase			=					True
				UpperCamelCase			=					True
				UpperCamelCase			=					filter_non_english






				def        snake_case			(				self	:					int							)			->   Optional[int]:




										'''simple docstring'''



										super().setUp()

										_UpperCamelCase    :      int						     =					[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_UpperCamelCase    :      Any						     =					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]							)							)






				def        snake_case			(				self	:					Optional[Any],				lowerCAmelCase__	:					List[str]							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					'''UNwant\u00E9d,running'''
										_UpperCamelCase    :      Optional[Any]						     =					'''unwanted, running'''
										return input_text, output_text






				def        snake_case			(				self	:					Optional[Any]							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					self.tokenizer_class(self.vocab_file							)

										_UpperCamelCase    :      Optional[Any]						     =					tokenizer.tokenize('''UNwant\u00E9d,running'''							)
										self.assertListEqual(lowerCAmelCase__,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']							)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__							),				[9, 6, 7, 1_2, 1_0, 1_1]							)






				def        snake_case			(				self	:					Any							)			->   Any:




										'''simple docstring'''



										if not self.test_rust_tokenizer:
																return

										_UpperCamelCase    :      Tuple						     =					self.get_tokenizer()
										_UpperCamelCase    :      Dict						     =					self.get_rust_tokenizer()

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

										_UpperCamelCase    :      List[str]						     =					tokenizer.tokenize(lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[int]						     =					rust_tokenizer.tokenize(lowerCAmelCase__							)
										self.assertListEqual(lowerCAmelCase__,				lowerCAmelCase__							)

										_UpperCamelCase    :      Optional[int]						     =					tokenizer.encode(lowerCAmelCase__,				add_special_tokens=lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[int]						     =					rust_tokenizer.encode(lowerCAmelCase__,				add_special_tokens=lowerCAmelCase__							)
										self.assertListEqual(lowerCAmelCase__,				lowerCAmelCase__							)

										_UpperCamelCase    :      Optional[int]						     =					self.get_rust_tokenizer()
										_UpperCamelCase    :      str						     =					tokenizer.encode(lowerCAmelCase__							)
										_UpperCamelCase    :      str						     =					rust_tokenizer.encode(lowerCAmelCase__							)
										self.assertListEqual(lowerCAmelCase__,				lowerCAmelCase__							)

										# With lower casing
										_UpperCamelCase    :      Optional[Any]						     =					self.get_tokenizer(do_lower_case=lowerCAmelCase__							)
										_UpperCamelCase    :      List[str]						     =					self.get_rust_tokenizer(do_lower_case=lowerCAmelCase__							)

										_UpperCamelCase    :      Union[str, Any]						     =					'''UNwant\u00E9d,running'''

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

										_UpperCamelCase    :      Union[str, Any]						     =					tokenizer.encode(lowerCAmelCase__,				add_special_tokens=lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					rust_tokenizer.encode(lowerCAmelCase__,				add_special_tokens=lowerCAmelCase__							)
										self.assertListEqual(lowerCAmelCase__,				lowerCAmelCase__							)

										_UpperCamelCase    :      List[str]						     =					self.get_rust_tokenizer()
										_UpperCamelCase    :      List[str]						     =					tokenizer.encode(lowerCAmelCase__							)
										_UpperCamelCase    :      Union[str, Any]						     =					rust_tokenizer.encode(lowerCAmelCase__							)
										self.assertListEqual(lowerCAmelCase__,				lowerCAmelCase__							)






				def        snake_case			(				self	:					Any							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					BasicTokenizer()

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






				def        snake_case			(				self	:					List[str]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__							)

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






				def        snake_case			(				self	:					Optional[Any]							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__,				strip_accents=lowerCAmelCase__							)

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






				def        snake_case			(				self	:					str							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__,				strip_accents=lowerCAmelCase__							)

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






				def        snake_case			(				self	:					str							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__							)

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






				def        snake_case			(				self	:					Any							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__							)

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






				def        snake_case			(				self	:					str							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__,				strip_accents=lowerCAmelCase__							)

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






				def        snake_case			(				self	:					Optional[int]							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__,				strip_accents=lowerCAmelCase__							)

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






				def        snake_case			(				self	:					Dict							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					BasicTokenizer(do_lower_case=lowerCAmelCase__,				never_split=['''[UNK]''']							)

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






				def        snake_case			(				self	:					Dict							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

										_UpperCamelCase    :      List[Any]						     =					{}
										for i, token in enumerate(lowerCAmelCase__							):
																_UpperCamelCase    :      Optional[Any]						     =					i
										_UpperCamelCase    :      Any						     =					WordpieceTokenizer(vocab=lowerCAmelCase__,				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        snake_case			(				self	:					Union[str, Any]							)			->   Any:




										'''simple docstring'''



										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        snake_case			(				self	:					Union[str, Any]							)			->   Optional[int]:




										'''simple docstring'''



										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        snake_case			(				self	:					str							)			->   Union[str, Any]:




										'''simple docstring'''



										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        snake_case			(				self	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					self.get_tokenizer()

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

										if self.test_rust_tokenizer:
																_UpperCamelCase    :      List[Any]						     =					self.get_rust_tokenizer()
																self.assertListEqual(
																    [rust_tokenizer.tokenize(lowerCAmelCase__							) for t in ['''Test''', '''\xad''', '''test''']],				[['''[UNK]'''], [], ['''[UNK]''']]							)






				@slow
				def        snake_case			(				self	:					Union[str, Any]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					self.tokenizer_class.from_pretrained('''bert-base-uncased'''							)

										_UpperCamelCase    :      Optional[Any]						     =					tokenizer.encode('''sequence builders''',				add_special_tokens=lowerCAmelCase__							)
										_UpperCamelCase    :      str						     =					tokenizer.encode('''multi-sequence build''',				add_special_tokens=lowerCAmelCase__							)

										_UpperCamelCase    :      str						     =					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[Any]						     =					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__,				lowerCAmelCase__							)

										assert encoded_sentence == [1_0_1] + text + [1_0_2]
										assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]






				def        snake_case			(				self	:					str							)			->   int:




										'''simple docstring'''



										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																						_UpperCamelCase    :      List[Any]						     =					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__,				**lowerCAmelCase__							)

																						_UpperCamelCase    :      Optional[Any]						     =					f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																						_UpperCamelCase    :      Dict						     =					tokenizer_r.encode_plus(
																						    lowerCAmelCase__,				return_attention_mask=lowerCAmelCase__,				return_token_type_ids=lowerCAmelCase__,				return_offsets_mapping=lowerCAmelCase__,				add_special_tokens=lowerCAmelCase__,				)

																						_UpperCamelCase    :      List[Any]						     =					tokenizer_r.do_lower_case if hasattr(lowerCAmelCase__,				'''do_lower_case'''							) else False
																						_UpperCamelCase    :      Any						     =					(
																						    [
																						        ((0, 0), tokenizer_r.cls_token),
																						        ((0, 1), '''A'''),
																						        ((1, 2), ''','''),
																						        ((3, 5), '''na'''),
																						        ((5, 6), '''##ï'''),
																						        ((6, 8), '''##ve'''),
																						        ((9, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), '''Allen'''),
																						        ((2_1, 2_3), '''##NL'''),
																						        ((2_3, 2_4), '''##P'''),
																						        ((2_5, 3_3), '''sentence'''),
																						        ((3_3, 3_4), '''.'''),
																						        ((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, 1_5), tokenizer_r.mask_token),
																						        ((1_6, 2_1), '''allen'''),
																						        ((2_1, 2_3), '''##nl'''),
																						        ((2_3, 2_4), '''##p'''),
																						        ((2_5, 3_3), '''sentence'''),
																						        ((3_3, 3_4), '''.'''),
																						        ((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''']							)






				@slow
				def        snake_case			(				self	:					int							)			->   str:




										'''simple docstring'''



										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																						_UpperCamelCase    :      List[Any]						     =					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__,				**lowerCAmelCase__							)
																						_UpperCamelCase    :      Optional[int]						     =					self.tokenizer_class.from_pretrained(lowerCAmelCase__,				**lowerCAmelCase__							)
																						_UpperCamelCase    :      Optional[Any]						     =					[['''Hello world!''', '''Nice to meet you!'''], ['''The cute cat.''', '''The adorable dog.''']]

																						_UpperCamelCase    :      str						     =					tokenizer_r.batch_encode_candidates(lowerCAmelCase__,				max_length=1_0,				return_tensors='''np'''							)
																						_UpperCamelCase    :      Optional[Any]						     =					tokenizer_p.batch_encode_candidates(lowerCAmelCase__,				max_length=1_0,				return_tensors='''np'''							)

																						_UpperCamelCase    :      List[str]						     =					(2, 2, 1_0)

																						self.assertEqual(encoded_sentence_r['''input_ids'''].shape,				lowerCAmelCase__							)
																						self.assertEqual(encoded_sentence_r['''attention_mask'''].shape,				lowerCAmelCase__							)
																						self.assertEqual(encoded_sentence_r['''token_type_ids'''].shape,				lowerCAmelCase__							)

																						self.assertEqual(encoded_sentence_p['''input_ids'''].shape,				lowerCAmelCase__							)
																						self.assertEqual(encoded_sentence_p['''attention_mask'''].shape,				lowerCAmelCase__							)
																						self.assertEqual(encoded_sentence_p['''token_type_ids'''].shape,				lowerCAmelCase__							)










"""simple docstring"""




import argparse
import json

import torch

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





def  a_       (							_lowercase ,		_lowercase=1     ):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('''.'''     )[n_shave_prefix_segments:]     )
						else:
												return ".".join(path.split('''.'''     )[:n_shave_prefix_segments]     )





def  a_       (							_lowercase ,		_lowercase=0     ):
						_UpperCamelCase    :      Dict						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item
												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''block.''' ,		'''resnets.'''     )
												_UpperCamelCase    :      int						     =					new_item.replace('''conv_shorcut''' ,		'''conv1'''     )
												_UpperCamelCase    :      List[str]						     =					new_item.replace('''in_shortcut''' ,		'''conv_shortcut'''     )
												_UpperCamelCase    :      Union[str, Any]						     =					new_item.replace('''temb_proj''' ,		'''time_emb_proj'''     )

												_UpperCamelCase    :      List[Any]						     =					shave_segments(_lowercase ,		n_shave_prefix_segments=_lowercase     )

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

						return mapping





def  a_       (							_lowercase ,		_lowercase=0 ,		_lowercase=False     ):
						_UpperCamelCase    :      str						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		_UpperCamelCase    :      str						     =					new_item.replace('''attn''' ,		'''attentions'''     )
												_UpperCamelCase    :      Dict						     =					new_item.replace('''.k.''' ,		'''.key.'''     )
												_UpperCamelCase    :      Optional[int]						     =					new_item.replace('''.v.''' ,		'''.value.'''     )
												_UpperCamelCase    :      Optional[int]						     =					new_item.replace('''.q.''' ,		'''.query.'''     )

												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''proj_out''' ,		'''proj_attn'''     )
												_UpperCamelCase    :      Dict						     =					new_item.replace('''norm''' ,		'''group_norm'''     )

												_UpperCamelCase    :      str						     =					shave_segments(_lowercase ,		n_shave_prefix_segments=_lowercase     )
												mapping.append({'''old''': old_item, '''new''': new_item}     )

						return mapping





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase=None ,		_lowercase=None ,		_lowercase=None     ):
						assert isinstance(_lowercase ,		_lowercase     ), "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():
																		_UpperCamelCase    :      Union[str, Any]						     =					old_checkpoint[path]
																		_UpperCamelCase    :      Any						     =					old_tensor.shape[0] // 3

																		_UpperCamelCase    :      List[str]						     =					(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)

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

																		_UpperCamelCase    :      List[Any]						     =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
																		_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase    :      Optional[int]						     =					old_tensor.split(channels // num_heads ,		dim=1     )

																		_UpperCamelCase    :      Union[str, Any]						     =					query.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      Any						     =					key.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      str						     =					value.reshape(_lowercase     ).squeeze()

						for path in paths:
												_UpperCamelCase    :      List[str]						     =					path['''new''']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												_UpperCamelCase    :      List[str]						     =					new_path.replace('''down.''' ,		'''down_blocks.'''     )
												_UpperCamelCase    :      Optional[int]						     =					new_path.replace('''up.''' ,		'''up_blocks.'''     )

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

												if "attentions" in new_path:
																		_UpperCamelCase    :      Optional[int]						     =					old_checkpoint[path['''old''']].squeeze()
												else:
																		_UpperCamelCase    :      Union[str, Any]						     =					old_checkpoint[path['''old''']]





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Union[str, Any]						     =					{}

						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''temb.dense.1.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.1.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''norm_out.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''norm_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_in.bias''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_out.weight''']
						_UpperCamelCase    :      str						     =					checkpoint['''conv_out.bias''']

						_UpperCamelCase    :      Optional[int]						     =					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Union[str, Any]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

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

						for i in range(_lowercase     ):
												_UpperCamelCase    :      Optional[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Tuple						     =					checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		_UpperCamelCase    :      Union[str, Any]						     =					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]     ):
																		_UpperCamelCase    :      Optional[int]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in down_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      Optional[int]						     =					{
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(_lowercase     )
																		}

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

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

																		if num_attn > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Union[str, Any]						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		config=_lowercase     )

						_UpperCamelCase    :      Dict						     =					[key for key in checkpoint if '''mid.block_1''' in key]
						_UpperCamelCase    :      int						     =					[key for key in checkpoint if '''mid.block_2''' in key]
						_UpperCamelCase    :      Tuple						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

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

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

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

						for i in range(_lowercase     ):
												_UpperCamelCase    :      List[str]						     =					num_up_blocks - 1 - i

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

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

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

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

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

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





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      List[Any]						     =					{}

						_UpperCamelCase    :      List[str]						     =					checkpoint['''encoder.norm_out.weight''']
						_UpperCamelCase    :      Tuple						     =					checkpoint['''encoder.norm_out.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_in.weight''']
						_UpperCamelCase    :      Any						     =					checkpoint['''encoder.conv_in.bias''']
						_UpperCamelCase    :      Optional[int]						     =					checkpoint['''encoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''decoder.norm_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.norm_out.bias''']

						_UpperCamelCase    :      str						     =					checkpoint['''decoder.conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_in.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''decoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_out.bias''']

						_UpperCamelCase    :      Tuple						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Optional[int]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

						_UpperCamelCase    :      List[str]						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''up''' in layer}     )
						_UpperCamelCase    :      Tuple						     =					{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowercase     )}

						for i in range(_lowercase     ):
												_UpperCamelCase    :      List[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

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

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

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Optional[int]						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase     )

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

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

						_UpperCamelCase    :      List[Any]						     =					[key for key in checkpoint if '''mid.block_1''' in key]
						_UpperCamelCase    :      Union[str, Any]						     =					[key for key in checkpoint if '''mid.block_2''' in key]
						_UpperCamelCase    :      int						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

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

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

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

						for i in range(_lowercase     ):
												_UpperCamelCase    :      Dict						     =					num_up_blocks - 1 - i

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

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

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

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

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

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

						return new_checkpoint


if __name__ == "__main__":
						UpperCamelCase_     =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.""")

						UpperCamelCase_     =parser.parse_args()
						UpperCamelCase_     =torch.load(args.checkpoint_path)

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

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

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

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

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

												UpperCamelCase_     =DDPMPipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)



