






'''simple docstring'''







import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
							from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
							_A     :      Union[str, Any]   					=True
							from torch.cuda.amp import autocast


_A     :      Union[str, Any]   					=logging.getLogger(__name__)

@dataclass
class _lowercase     :
							a    				=	field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}		)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether to log verbose messages or not."""}    ,				)
							a    				=	field(
							    default=2.0    ,				metadata={"""help""": """Maximum temperature for gumbel softmax."""}		)
							a    				=	field(
							    default=0.5    ,				metadata={"""help""": """Minimum temperature for gumbel softmax."""}		)
							a    				=	field(
							    default=0.99_99_95    ,				metadata={"""help""": """Decay of gumbel temperature during training."""}		)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Union[str, Any]:
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,      datefmt="""%m/%d/%Y %H:%M:%S"""    ,      handlers=[logging.StreamHandler(sys.stdout				)]    ,      )
							lowerCamelCase__ :     Any	  =	logging.WARNING
							if model_args.verbose_logging:
														lowerCamelCase__ :     Optional[int]	  =	logging.DEBUG
							elif trainer_utils.is_main_process(training_args.local_rank				):
														lowerCamelCase__ :     Dict	  =	logging.INFO
							logger.setLevel(a__				)

@dataclass
class _lowercase     :
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}		)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
							a    				=	field(
							    default="""train"""    ,				metadata={
							        """help""": """The name of the training data set split to use (via the datasets library). Defaults to \'train\'"""
							    }    ,				)
							a    				=	field(
							    default="""validation"""    ,				metadata={
							        """help""": (
							            """The name of the validation data set split to use (via the datasets library). Defaults to \'validation\'"""
							        )
							    }    ,				)
							a    				=	field(
							    default="""file"""    ,				metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to \'file\'"""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}		)
							a    				=	field(
							    default=1    ,				metadata={
							        """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
							    }    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """The number of processes to use for the preprocessing."""}    ,				)
							a    				=	field(
							    default=20.0    ,				metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}		)



@dataclass
class _lowercase     :
							a    				=	42
							a    				=	42
							a    				=	"""longest"""
							a    				=	None
							a    				=	None




							def __call__(							self:    int      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Tuple	  =	self.feature_extractor.pad(
														    _snake_case      ,							max_length=self.max_length      ,							padding=self.padding      ,							pad_to_multiple_of=self.pad_to_multiple_of      ,							return_tensors="""pt"""      ,							)
														lowerCamelCase__ :     Optional[int]	  =	self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]					)

														lowerCamelCase__ :     Union[str, Any]	  =	batch["""input_values"""].shape[0]

														# make sure that no loss is computed on padded inputs
														if batch["attention_mask"] is not None:
																					# compute real output lengths according to convolution formula
																					lowerCamelCase__ :     Tuple	  =	self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1					)					).to(
																					    torch.long					)

																					lowerCamelCase__ :     Optional[int]	  =	torch.zeros(
																					    (batch_size, mask_indices_seq_length)      ,							dtype=torch.long      ,							device=batch["""input_values"""].device					)

																					# these two operations makes sure that all values
																					# before the output lengths indices are attended to
																					lowerCamelCase__ :     Union[str, Any]	  =	1
																					lowerCamelCase__ :     int	  =	attention_mask.flip([-1]					).cumsum(-1					).flip([-1]					).bool()

														# sample randomly masked indices
														lowerCamelCase__ :     str	  =	_compute_mask_indices(
														    (batch_size, mask_indices_seq_length)      ,							self.model.config.mask_time_prob      ,							self.model.config.mask_time_length      ,							attention_mask=_snake_case      ,							min_masks=2      ,							)

														return batch



class _lowercase     (    _lowercase		):




							def __init__(							self:    Tuple      ,							*UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]=1      ,							UpperCamelCase__:    List[str]=0      ,							UpperCamelCase__:    List[Any]=1.0      ,							**UpperCamelCase__:    List[str]					):
														super().__init__(*_snake_case      ,							**_snake_case					)
														lowerCamelCase__ :     Optional[Any]	  =	0
														lowerCamelCase__ :     Optional[int]	  =	max_gumbel_temp
														lowerCamelCase__ :     Any	  =	min_gumbel_temp
														lowerCamelCase__ :     Union[str, Any]	  =	gumbel_temp_decay




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Dict					):
														model.train()
														lowerCamelCase__ :     Any	  =	self._prepare_inputs(_snake_case					)

														if self.use_amp:
																					with autocast():
																												lowerCamelCase__ :     Any	  =	self.compute_loss(_snake_case      ,							_snake_case					)
														else:
																					lowerCamelCase__ :     int	  =	self.compute_loss(_snake_case      ,							_snake_case					)

														if self.args.n_gpu > 1 or self.deepspeed:
																					if model.module.config.ctc_loss_reduction == "mean":
																												lowerCamelCase__ :     Any	  =	loss.mean()
																					elif model.module.config.ctc_loss_reduction == "sum":
																												lowerCamelCase__ :     List[str]	  =	loss.sum() / (inputs["""mask_time_indices"""]).sum()
																					else:
																												raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''					)

														if self.args.gradient_accumulation_steps > 1:
																					lowerCamelCase__ :     Tuple	  =	loss / self.args.gradient_accumulation_steps

														if self.use_amp:
																					self.scaler.scale(_snake_case					).backward()
														elif self.use_apex:
																					with amp.scale_loss(_snake_case      ,							self.optimizer					) as scaled_loss:
																												scaled_loss.backward()
														elif self.deepspeed:
																					self.deepspeed.backward(_snake_case					)
														else:
																					loss.backward()

														self.num_update_step += 1
														# make sure gumbel softmax temperature is decayed
														if self.args.n_gpu > 1 or self.deepspeed:
																					model.module.set_gumbel_temperature(
																					    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,							self.min_gumbel_temp					)					)
														else:
																					model.set_gumbel_temperature(
																					    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,							self.min_gumbel_temp					)					)

														return loss.detach()





def 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							lowerCamelCase__ :     Tuple	  =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Optional[int]	  =	parser.parse_args_into_dataclasses()
							configure_logger(a__    ,      a__				)

							# Downloading and loading a dataset from the hub.
							lowerCamelCase__ :     Tuple	  =	load_dataset(data_args.dataset_name    ,      data_args.dataset_config_name    ,      cache_dir=model_args.cache_dir				)

							if "validation" not in datasets.keys():
														# make sure only "validation" and "train" keys remain"
														lowerCamelCase__ :     int	  =	DatasetDict()
														lowerCamelCase__ :     List[Any]	  =	load_dataset(
														    data_args.dataset_name    ,      data_args.dataset_config_name    ,      split=f'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''    ,      cache_dir=model_args.cache_dir    ,      )
														lowerCamelCase__ :     List[Any]	  =	load_dataset(
														    data_args.dataset_name    ,      data_args.dataset_config_name    ,      split=f'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''    ,      cache_dir=model_args.cache_dir    ,      )
							else:
														# make sure only "validation" and "train" keys remain"
														lowerCamelCase__ :     Tuple	  =	DatasetDict()
														lowerCamelCase__ :     List[Any]	  =	load_dataset(
														    data_args.dataset_name    ,      data_args.dataset_config_name    ,      split="""validation"""    ,      cache_dir=model_args.cache_dir    ,      )
														lowerCamelCase__ :     int	  =	load_dataset(
														    data_args.dataset_name    ,      data_args.dataset_config_name    ,      split=f'''{data_args.train_split_name}'''    ,      cache_dir=model_args.cache_dir    ,      )

							# only normalized-inputs-training is supported
							lowerCamelCase__ :     Optional[Any]	  =	WavaVecaFeatureExtractor.from_pretrained(
							    model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir    ,      do_normalize=a__				)

							def prepare_dataset(UpperCamelCase				):
														# check that all files have the correct sampling rate
														lowerCamelCase__  ,		lowerCamelCase__ :     List[Any]	  =	librosa.load(batch[data_args.speech_file_column]    ,      sr=feature_extractor.sampling_rate				)
														return batch

							# load audio files into numpy arrays
							lowerCamelCase__ :     Tuple	  =	datasets.map(
							    a__    ,      num_proc=data_args.preprocessing_num_workers    ,      remove_columns=datasets["""train"""].column_names				)

							# filter audio files that are too long
							lowerCamelCase__ :     List[str]	  =	vectorized_datasets.filter(
							    lambda UpperCamelCase				: len(data["""speech"""]				) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate				)				)

							def normalize(UpperCamelCase				):
														return feature_extractor(batch["""speech"""]    ,      sampling_rate=feature_extractor.sampling_rate				)

							# normalize and transform to `BatchFeatures`
							lowerCamelCase__ :     Any	  =	vectorized_datasets.map(
							    a__    ,      batched=a__    ,      num_proc=data_args.preprocessing_num_workers    ,      load_from_cache_file=not data_args.overwrite_cache    ,      remove_columns=vectorized_datasets["""train"""].column_names    ,      )

							# pretraining is only supported for "newer" stable layer norm architecture
							# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
							lowerCamelCase__ :     Optional[int]	  =	WavaVecaConfig.from_pretrained(
							    model_args.model_name_or_path    ,      cache_dir=model_args.cache_dir    ,      gradient_checkpointing=training_args.gradient_checkpointing    ,      )

							if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
														raise ValueError(
														    """PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"""
														    """ ``config.feat_extract_norm=\'layer\'"""				)

							lowerCamelCase__ :     Optional[int]	  =	WavaVecaForPreTraining(a__				)

							lowerCamelCase__ :     Dict	  =	DataCollatorForWavaVecaPretraining(model=a__    ,      feature_extractor=a__				)

							lowerCamelCase__ :     Optional[Any]	  =	WavaVecaPreTrainer(
							    model=a__    ,      data_collator=a__    ,      args=a__    ,      train_dataset=vectorized_datasets["""train"""]    ,      eval_dataset=vectorized_datasets["""validation"""]    ,      tokenizer=a__    ,      max_gumbel_temp=model_args.max_gumbel_temperature    ,      min_gumbel_temp=model_args.min_gumbel_temperature    ,      gumbel_temp_decay=model_args.gumbel_temperature_decay    ,      )
							trainer.train()


if __name__ == "__main__":
							main()









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     :      str   					={
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Union[str, Any]   					={
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      str   					={
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      int   					={
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if isinstance(UpperCamelCase    ,      UpperCamelCase				):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError("""boolean value expected"""				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.skip_connection.weight''']
														lowerCamelCase__ :     List[Any]	  =	checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	str:
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3    ,      dim=0				)
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3    ,      dim=0				)

							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.norm.bias''']

							lowerCamelCase__ :     List[Any]	  =	weight_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     List[Any]	  =	bias_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Any	  =	weight_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Optional[Any]	  =	bias_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Dict	  =	weight_v.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Union[str, Any]	  =	bias_v.squeeze(-1				).squeeze(-1				)

							lowerCamelCase__ :     Optional[Any]	  =	(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
							)
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)
							lowerCamelCase__ :     Optional[int]	  =	{}

							lowerCamelCase__ :     Optional[int]	  =	checkpoint["""time_embed.0.weight"""]
							lowerCamelCase__ :     List[Any]	  =	checkpoint["""time_embed.0.bias"""]
							lowerCamelCase__ :     int	  =	checkpoint["""time_embed.2.weight"""]
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""time_embed.2.bias"""]

							if unet_config["num_class_embeds"] is not None:
														lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""label_emb.weight"""]

							lowerCamelCase__ :     Tuple	  =	checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase__ :     List[str]	  =	checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase__ :     Optional[Any]	  =	unet_config["""down_block_types"""]
							lowerCamelCase__ :     Any	  =	unet_config["""layers_per_block"""]
							lowerCamelCase__ :     Any	  =	unet_config["""attention_head_dim"""]
							lowerCamelCase__ :     List[Any]	  =	unet_config["""block_out_channels"""]
							lowerCamelCase__ :     str	  =	1
							lowerCamelCase__ :     str	  =	channels_list[0]

							for i, layer_type in enumerate(UpperCamelCase				):
														lowerCamelCase__ :     List[Any]	  =	channels_list[i]
														lowerCamelCase__ :     List[Any]	  =	current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     int	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Tuple	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     List[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     str	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Any	  =	f'''down_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Tuple	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

														if i != len(UpperCamelCase				) - 1:
																					lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__ :     str	  =	f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					current_layer += 1

														lowerCamelCase__ :     Union[str, Any]	  =	current_channels

							# hardcoded the mid-block for now
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.0"""
							lowerCamelCase__ :     Optional[Any]	  =	"""middle_block.0"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	"""mid_block.attentions.0"""
							lowerCamelCase__ :     Dict	  =	"""middle_block.1"""
							lowerCamelCase__ :     int	  =	convert_attention(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.1"""
							lowerCamelCase__ :     Tuple	  =	"""middle_block.2"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	0
							lowerCamelCase__ :     Any	  =	unet_config["""up_block_types"""]

							for i, layer_type in enumerate(UpperCamelCase				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     int	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Any	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Dict	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     List[str]	  =	f'''output_blocks.{current_layer-1}.1'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     str	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     List[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Optional[Any]	  =	f'''up_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Any	  =	f'''output_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Optional[int]	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Tuple	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     Tuple	  =	f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__ :     List[str]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.weight"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.bias"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.2.weight"""]
							lowerCamelCase__ :     Tuple	  =	checkpoint["""out.2.bias"""]

							return new_checkpoint


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

			parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
			parser.add_argument(
			    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
			)
			parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

			_A     :      Tuple   					=parser.parse_args()
			_A     :      Optional[int]   					=strabool(args.class_cond)

			_A     :      List[str]   					=os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						_A     :      int   					=IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Tuple   					=LSUN_256_UNET_CONFIG
			elif "test" in ckpt_name:
						_A     :      Any   					=TEST_UNET_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			if not args.class_cond:
						_A     :      str   					=None

			_A     :      Optional[int]   					=con_pt_to_diffuser(args.unet_path, unet_config)

			_A     :      Optional[int]   					=UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						_A     :      Tuple   					=CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     :      int   					=CT_IMAGENET_64_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Union[str, Any]   					=CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_A     :      str   					=CMStochasticIterativeScheduler(**scheduler_config)

			_A     :      Optional[Any]   					=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)

