





"""simple docstring"""




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor




class 				_a		:






				def __init__(				self	:					Optional[int],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					str							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					cuda
										_UpperCamelCase    :      Any						     =					AutoConfig.from_pretrained(lowerCAmelCase__							)
										_UpperCamelCase    :      Dict						     =					AutoModelForCTC.from_pretrained(lowerCAmelCase__							)
										self.model.eval()
										if self.cuda:
																self.model.to(device='''cuda'''							)
										_UpperCamelCase    :      Any						     =					AutoProcessor.from_pretrained(lowerCAmelCase__							)
										_UpperCamelCase    :      List[str]						     =					torchaudio.transforms.Resample(lowerCAmelCase__,				1_6_0_0_0							)
										_UpperCamelCase    :      Union[str, Any]						     =					0
										_UpperCamelCase    :      str						     =					list(self.processor.tokenizer.get_vocab().keys()							)
										for i in range(len(lowerCAmelCase__							)							):
																if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																						_UpperCamelCase    :      Optional[int]						     =					i
										print('''Blank Token id [PAD]/<pad>''',				lowerCAmelCase__							)
										_UpperCamelCase    :      Tuple						     =					blank_id






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




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      str						     =					torchaudio.load(lowerCAmelCase__							)
										_UpperCamelCase    :      List[str]						     =					self.resampler(lowerCAmelCase__							).squeeze().numpy()
										return speech






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




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					self.blank_id
										_UpperCamelCase    :      Tuple						     =					'''|'''.join(item['''sent'''].split(''' '''							)							)
										if not os.path.isfile(item['''wav_path''']							):
																print(item['''wav_path'''],				'''not found in wavs directory'''							)

										_UpperCamelCase    :      int						     =					self.speech_file_to_array_fn(item['''wav_path''']							)
										_UpperCamelCase    :      str						     =					self.processor(lowerCAmelCase__,				sampling_rate=1_6_0_0_0,				return_tensors='''pt''',				padding=lowerCAmelCase__							)
										if self.cuda:
																_UpperCamelCase    :      Union[str, Any]						     =					inputs.to(device='''cuda'''							)

										with torch.no_grad():
																_UpperCamelCase    :      List[Any]						     =					self.model(inputs.input_values							).logits

										# get the emission probability at frame level
										_UpperCamelCase    :      Optional[int]						     =					torch.log_softmax(lowerCAmelCase__,				dim=-1							)
										_UpperCamelCase    :      Optional[int]						     =					emissions[0].cpu().detach()

										# get labels from vocab
										_UpperCamelCase    :      Tuple						     =					([''''''] + list(self.processor.tokenizer.get_vocab().keys()							))[
										    :-1
										]  # logits don't align with the tokenizer's vocab

										_UpperCamelCase    :      Dict						     =					{c: i for i, c in enumerate(lowerCAmelCase__							)}
										_UpperCamelCase    :      Optional[int]						     =					[]
										for c in transcript:
																if c in dictionary:
																						tokens.append(dictionary[c]							)

										def get_trellis(lowerCAmelCase__	:					int,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					List[str]=0							):
																_UpperCamelCase    :      Optional[Any]						     =					emission.size(0							)
																_UpperCamelCase    :      Optional[int]						     =					len(lowerCAmelCase__							)

																# Trellis has extra diemsions for both time axis and tokens.
																# The extra dim for tokens represents <SoS> (start-of-sentence)
																# The extra dim for time axis is for simplification of the code.
																_UpperCamelCase    :      List[str]						     =					torch.full((num_frames + 1, num_tokens + 1),				-float('''inf'''							)							)
																_UpperCamelCase    :      Union[str, Any]						     =					0
																for t in range(lowerCAmelCase__							):
																						_UpperCamelCase    :      Any						     =					torch.maximum(
																						    # Score for staying at the same token
																						    trellis[t, 1:] + emission[t, blank_id],				trellis[t, :-1] + emission[t, tokens],				)
																return trellis

										_UpperCamelCase    :      Tuple						     =					get_trellis(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)




										@dataclass
										class 				_a		:
														UpperCamelCase			=					42
														UpperCamelCase			=					42
														UpperCamelCase			=					42

										def backtrack(lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Tuple=0							):
																_UpperCamelCase    :      List[Any]						     =					trellis.size(1							) - 1
																_UpperCamelCase    :      Optional[int]						     =					torch.argmax(trellis[:, j]							).item()

																_UpperCamelCase    :      Tuple						     =					[]
																for t in range(lowerCAmelCase__,				0,				-1							):
																						# 1. Figure out if the current position was stay or change
																						# Note (again):
																						# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
																						# Score for token staying the same from time frame J-1 to T.
																						_UpperCamelCase    :      str						     =					trellis[t - 1, j] + emission[t - 1, blank_id]
																						# Score for token changing from C-1 at T-1 to J at T.
																						_UpperCamelCase    :      Tuple						     =					trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																						# 2. Store the path with frame-wise probability.
																						_UpperCamelCase    :      Optional[Any]						     =					emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
																						# Return token index and time index in non-trellis coordinate.
																						path.append(Point(j - 1,				t - 1,				lowerCAmelCase__							)							)

																						# 3. Update the token
																						if changed > stayed:
																												j -= 1
																												if j == 0:
																																		break
																else:
																						raise ValueError('''Failed to align'''							)
																return path[::-1]

										_UpperCamelCase    :      int						     =					backtrack(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)




										@dataclass
										class 				_a		:
														UpperCamelCase			=					42
														UpperCamelCase			=					42
														UpperCamelCase			=					42
														UpperCamelCase			=					42






														def __repr__(				self	:					List[Any]							)			->   Tuple:




																				'''simple docstring'''



																				return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""






														@property
														def        snake_case			(				self	:					Tuple							)			->   str:




																				'''simple docstring'''



																				return self.end - self.start

										def merge_repeats(lowerCAmelCase__	:					str							):
																_UpperCamelCase     ,							_UpperCamelCase    :      Optional[Any]						     =					0, 0
																_UpperCamelCase    :      List[Any]						     =					[]
																while ia < len(lowerCAmelCase__							):
																						while ia < len(lowerCAmelCase__							) and path[ia].token_index == path[ia].token_index:
																												ia += 1
																						_UpperCamelCase    :      Union[str, Any]						     =					sum(path[k].score for k in range(lowerCAmelCase__,				lowerCAmelCase__							)							) / (ia - ia)
																						segments.append(
																						    Segment(
																						        transcript[path[ia].token_index],				path[ia].time_index,				path[ia - 1].time_index + 1,				lowerCAmelCase__,				)							)
																						_UpperCamelCase    :      Tuple						     =					ia
																return segments

										_UpperCamelCase    :      List[str]						     =					merge_repeats(lowerCAmelCase__							)
										with open(item['''out_path'''],				'''w'''							) as out_align:
																for seg in segments:
																						out_align.write(str(lowerCAmelCase__							) + '''\n'''							)






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




										'''simple docstring'''



										if not os.path.exists(lowerCAmelCase__							):
																os.makedirs(lowerCAmelCase__							)

										# load text file
										_UpperCamelCase    :      Dict						     =					open(lowerCAmelCase__,				encoding='''utf8'''							).readlines()

										_UpperCamelCase    :      Union[str, Any]						     =					[]
										for line in lines:
																if len(line.strip().split('''\t'''							)							) != 2:
																						print('''Script must be in format: 00001  this is my sentence'''							)
																						exit()

																_UpperCamelCase     ,							_UpperCamelCase    :      Tuple						     =					line.strip().split('''\t'''							)
																_UpperCamelCase    :      str						     =					os.path.join(lowerCAmelCase__,				wav_name + '''.wav'''							)
																_UpperCamelCase    :      List[Any]						     =					os.path.join(lowerCAmelCase__,				wav_name + '''.txt'''							)

																items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path}							)
										print('''Number of samples found in script file''',				len(lowerCAmelCase__							)							)

										for item in tqdm(lowerCAmelCase__							):
																self.align_single_sample(lowerCAmelCase__							)





def  a_       (							):
						_UpperCamelCase    :      Dict						     =					argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''' ,		type=_lowercase ,		default='''arijitx/wav2vec2-xls-r-300m-bengali''' ,		help='''wav2vec model name'''     )
						parser.add_argument('''--wav_dir''' ,		type=_lowercase ,		default='''./wavs''' ,		help='''directory containing wavs'''     )
						parser.add_argument('''--text_file''' ,		type=_lowercase ,		default='''script.txt''' ,		help='''file containing text'''     )
						parser.add_argument('''--input_wavs_sr''' ,		type=_lowercase ,		default=1_6000 ,		help='''sampling rate of input audios'''     )
						parser.add_argument(
						    '''--output_dir''' ,		type=_lowercase ,		default='''./out_alignment''' ,		help='''output directory containing the alignment files'''     )
						parser.add_argument('''--cuda''' ,		action='''store_true'''     )

						_UpperCamelCase    :      str						     =					parser.parse_args()

						_UpperCamelCase    :      Any						     =					WavaVecaAligner(args.model_name ,		args.input_wavs_sr ,		args.cuda     )
						aligner.align_data(args.wav_dir ,		args.text_file ,		args.output_dir     )


if __name__ == "__main__":
						main()










"""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)



