






"""simple docstring"""


from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
		import importlib_metadata
else:
		import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
__SCREAMING_SNAKE_CASE      =      '\\n'

__SCREAMING_SNAKE_CASE      =      '\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

__SCREAMING_SNAKE_CASE      =      '\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

__SCREAMING_SNAKE_CASE      =      ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
__SCREAMING_SNAKE_CASE      =      ''

try:
		from jiwer import transforms as tr

		__SCREAMING_SNAKE_CASE      =      True
except ImportError:
		__SCREAMING_SNAKE_CASE      =      False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):




		class 	__UpperCamelCase    (    tr.AbstractTransform	):
							def __init__(       self       :  str    ,   UpperCAmelCase       :  str = " "      )  ->					str:
													lowerCAmelCase      :Dict				  = sentence_delimiter
							def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  str      )  ->					Any:
													return list(UpperCAmelCase      )
							def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  List[str]      )  ->					Dict:
													lowerCAmelCase      :Tuple				  = []
													for sent_idx, sentence in enumerate(UpperCAmelCase      ):
																			chars.extend(self.process_string(UpperCAmelCase      )      )
																			if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase      ) - 1:
																									chars.append(self.sentence_delimiter      )
													return chars


		__SCREAMING_SNAKE_CASE      =      tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
elif _jiwer_available:
		__SCREAMING_SNAKE_CASE      =      tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)
else:
		__SCREAMING_SNAKE_CASE      =      None


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						return float((preds == labels).mean()   )


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						return {
						    "f1": float(fa_score(y_true=a__    ,			y_pred=a__    ,			average='macro'   )   ),
						    "accuracy": simple_accuracy(a__    ,			a__   ),
						}


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__="exp"    ,			a__=None    ,			a__=False    ,			a__=False    ,			a__=None    ,			a__=False    ,			):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = [[label] for label in labels]
						lowerCAmelCase      :int				  = list(a__   )
						try:
												import sacrebleu as scb
						except ImportError:
												raise ValueError(
												    'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
												    'You can install it via `pip install sacrebleu`.'   )

						if version.parse(scb.__version__   ) < version.parse('1.4.12'   ):
												raise ImportWarning(
												    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
												    'You can install it with `pip install "sacrebleu>=1.4.12"`.'   )

						lowerCAmelCase      :Dict				  = len(labels[0]   )
						if any(len(a__   ) != references_per_prediction for refs in labels   ):
												raise ValueError('Sacrebleu requires the same number of references for each prediction'   )
						lowerCAmelCase      :List[str]				  = [[refs[i] for refs in labels] for i in range(a__   )]
						lowerCAmelCase      :List[str]				  = scb.corpus_bleu(
						    a__    ,			a__    ,			smooth_method=a__    ,			smooth_value=a__    ,			force=a__    ,			lowercase=a__    ,			use_effective_order=a__    ,			**({'tokenize': tokenize} if tokenize else {})    ,			)
						return {"bleu": output.score}


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						try:
												from jiwer import compute_measures
						except ImportError:
												raise ValueError(
												    F"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
												    'You can install it via `pip install jiwer`.'   )

						if concatenate_texts:
												lowerCAmelCase      :str				  = compute_measures(a__    ,			a__   )['wer']

												lowerCAmelCase      :Any				  = compute_measures(a__    ,			a__    ,			truth_transform=a__    ,			hypothesis_transform=a__   )['wer']
												return {"wer": wer, "cer": cer}
						else:

												def compute_score(a__    ,			a__    ,			a__="wer"   ):
																		lowerCAmelCase      :int				  = 0
																		lowerCAmelCase      :str				  = 0
																		for prediction, reference in zip(a__    ,			a__   ):
																								if score_type == "wer":
																														lowerCAmelCase      :List[Any]				  = compute_measures(a__    ,			a__   )
																								elif score_type == "cer":
																														lowerCAmelCase      :int				  = compute_measures(
																														    a__    ,			a__    ,			truth_transform=a__    ,			hypothesis_transform=a__   )
																								incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																								total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																		return incorrect / total

												return {"wer": compute_score(a__    ,			a__    ,			'wer'   ), "cer": compute_score(a__    ,			a__    ,			'cer'   )}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION	)
class 	__UpperCamelCase    (    datasets.Metric	):
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					int:
											if self.config_name not in _CONFIG_NAMES:
																	raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""      )

											lowerCAmelCase      :Tuple				  = 'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

											return datasets.MetricInfo(
											    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
											        {'predictions': datasets.Value(UpperCAmelCase      ), 'references': datasets.Value(UpperCAmelCase      )}      )    ,   codebase_urls=[]    ,   reference_urls=[]    ,   format='numpy'    ,   )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Any=None    ,   UpperCAmelCase       :  Optional[int]=None      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = bleu_kwargs if bleu_kwargs is not None else {}
											lowerCAmelCase      :List[Any]				  = wer_kwargs if wer_kwargs is not None else {}

											if self.config_name == "fleurs-lang_id":
																	return {"accuracy": simple_accuracy(UpperCAmelCase    ,   UpperCAmelCase      )}
											elif self.config_name == "minds14":
																	return fa_and_simple_accuracy(UpperCAmelCase    ,   UpperCAmelCase      )
											elif self.config_name == "covost2":
																	lowerCAmelCase      :Any				  = bleu_kwargs.pop('smooth_method'    ,   'exp'      )
																	lowerCAmelCase      :List[Any]				  = bleu_kwargs.pop('smooth_value'    ,   UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = bleu_kwargs.pop('force'    ,   UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = bleu_kwargs.pop('lowercase'    ,   UpperCAmelCase      )
																	lowerCAmelCase      :List[Any]				  = bleu_kwargs.pop('tokenize'    ,   UpperCAmelCase      )
																	lowerCAmelCase      :Any				  = bleu_kwargs.pop('use_effective_order'    ,   UpperCAmelCase      )
																	return bleu(
																	    preds=UpperCAmelCase    ,   labels=UpperCAmelCase    ,   smooth_method=UpperCAmelCase    ,   smooth_value=UpperCAmelCase    ,   force=UpperCAmelCase    ,   lowercase=UpperCAmelCase    ,   tokenize=UpperCAmelCase    ,   use_effective_order=UpperCAmelCase    ,   )
											elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
																	lowerCAmelCase      :Any				  = wer_kwargs.pop('concatenate_texts'    ,   UpperCAmelCase      )
																	return wer_and_cer(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   self.config_name      )
											else:
																	raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""      )







"""simple docstring"""


import argparse
import json

import torch

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


def 					UpperCAmelCase			(				a__    ,			a__=1   ):





						'''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			(				a__    ,			a__=0   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = []
						for old_item in old_list:
												lowerCAmelCase      :Union[str, Any]				  = old_item
												lowerCAmelCase      :str				  = new_item.replace('block.'    ,			'resnets.'   )
												lowerCAmelCase      :int				  = new_item.replace('conv_shorcut'    ,			'conv1'   )
												lowerCAmelCase      :int				  = new_item.replace('in_shortcut'    ,			'conv_shortcut'   )
												lowerCAmelCase      :Union[str, Any]				  = new_item.replace('temb_proj'    ,			'time_emb_proj'   )

												lowerCAmelCase      :List[str]				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )

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

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__=0    ,			a__=False   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = []
						for old_item in old_list:
												lowerCAmelCase      :int				  = old_item

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

												lowerCAmelCase      :Optional[Any]				  = new_item.replace('proj_out'    ,			'proj_attn'   )
												lowerCAmelCase      :List[str]				  = new_item.replace('norm'    ,			'group_norm'   )

												lowerCAmelCase      :Any				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )
												mapping.append({'old': old_item, 'new': new_item}   )

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__=None    ,			a__=None    ,			a__=None   ):





						'''simple docstring'''






						assert isinstance(a__    ,			a__   ), "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      :Union[str, Any]				  = old_checkpoint[path]
																		lowerCAmelCase      :List[Any]				  = old_tensor.shape[0] // 3

																		lowerCAmelCase      :int				  = (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

																		lowerCAmelCase      :Optional[int]				  = old_tensor.shape[0] // config.get('num_head_channels'    ,			1   ) // 3

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

																		lowerCAmelCase      :str				  = query.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Dict				  = key.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Union[str, Any]				  = value.reshape(a__   ).squeeze()

						for path in paths:
												lowerCAmelCase      :Union[str, Any]				  = path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase      :Dict				  = new_path.replace('down.'    ,			'down_blocks.'   )
												lowerCAmelCase      :int				  = new_path.replace('up.'    ,			'up_blocks.'   )

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

												if "attentions" in new_path:
																		lowerCAmelCase      :Tuple				  = old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase      :Optional[int]				  = old_checkpoint[path['old']]


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['temb.dense.0.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['temb.dense.0.bias']
						lowerCAmelCase      :str				  = checkpoint['temb.dense.1.weight']
						lowerCAmelCase      :int				  = checkpoint['temb.dense.1.bias']

						lowerCAmelCase      :Dict				  = checkpoint['norm_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['norm_out.bias']

						lowerCAmelCase      :Optional[int]				  = checkpoint['conv_in.weight']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_in.bias']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['conv_out.bias']

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

						lowerCAmelCase      :Dict				  = len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'up' in layer}   )
						lowerCAmelCase      :List[Any]				  = {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(a__   )}

						for i in range(a__   ):
												lowerCAmelCase      :Union[str, Any]				  = (i - 1) // (config['layers_per_block'] + 1)

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

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

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

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Optional[Any]				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :str				  = [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]    ,			)

						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

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

						for i in range(a__   ):
												lowerCAmelCase      :Optional[Any]				  = num_up_blocks - 1 - i

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

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

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

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

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

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


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['encoder.norm_out.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['encoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['encoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['encoder.conv_in.bias']
						lowerCAmelCase      :Dict				  = checkpoint['encoder.conv_out.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['encoder.conv_out.bias']

						lowerCAmelCase      :Dict				  = checkpoint['decoder.norm_out.weight']
						lowerCAmelCase      :List[str]				  = checkpoint['decoder.norm_out.bias']

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

						lowerCAmelCase      :List[str]				  = len({'.'.join(layer.split('.'   )[:3]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :int				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

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

						for i in range(a__   ):
												lowerCAmelCase      :Tuple				  = (i - 1) // (config['layers_per_block'] + 1)

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

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

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :List[Any]				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__   )

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

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

						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Tuple				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.attn_1' in key]

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

						lowerCAmelCase      :List[str]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

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

						for i in range(a__   ):
												lowerCAmelCase      :Optional[int]				  = num_up_blocks - 1 - i

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

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

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

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

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

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

						return new_checkpoint


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

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

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

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

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

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

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

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