






"""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		:       List[str]  				=		True
						from torch.cuda.amp import autocast


__A		:       str  				=		logging.getLogger(__name__)





@dataclass
class 					_UpperCAmelCase       :
					SCREAMING_SNAKE_CASE_		:				str							    =   field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
					SCREAMING_SNAKE_CASE_		:				Optional[str]							    =   field(
					    default=_A   ,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,						)
					SCREAMING_SNAKE_CASE_		:				Optional[bool]							    =   field(
					    default=_A   ,						metadata={"help": "Whether to freeze the feature extractor layers of the model."}							)
					SCREAMING_SNAKE_CASE_		:				Optional[bool]							    =   field(
					    default=_A   ,						metadata={"help": "Whether to log verbose messages or not."}   ,						)
					SCREAMING_SNAKE_CASE_		:				Optional[float]							    =   field(
					    default=2.0   ,						metadata={"help": "Maximum temperature for gumbel softmax."}							)
					SCREAMING_SNAKE_CASE_		:				Optional[float]							    =   field(
					    default=0.5   ,						metadata={"help": "Minimum temperature for gumbel softmax."}							)
					SCREAMING_SNAKE_CASE_		:				Optional[float]							    =   field(
					    default=0.999_995   ,						metadata={"help": "Decay of gumbel temperature during training."}							)



def 						lowercase    (				__snake_case				: ModelArguments		,					__snake_case				: TrainingArguments	):
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,					datefmt='''%m/%d/%Y %H:%M:%S'''		,					handlers=[logging.StreamHandler(sys.stdout	)]		,					)
		lowercase_ : int							       =   logging.WARNING
		if model_args.verbose_logging:
				lowercase_ : Any							       =   logging.DEBUG
		elif trainer_utils.is_main_process(training_args.local_rank	):
				lowercase_ : Tuple							       =   logging.INFO
		logger.setLevel(__snake_case	)





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





@dataclass
class 					_UpperCAmelCase       :
					SCREAMING_SNAKE_CASE_		:				WavaVecaForPreTraining
					SCREAMING_SNAKE_CASE_		:				WavaVecaFeatureExtractor
					SCREAMING_SNAKE_CASE_		:				Union[bool, str]							    =   "longest"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   None
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   None




					def __call__(				self     :						Union[str, Any]    ,						A     :						List[Dict[str, Union[List[int], torch.Tensor]]]   )  ->  Dict[str, torch.Tensor]:
							# reformat list to dict and set to pytorch format
							lowercase_ : List[str]							       =   self.feature_extractor.pad(
							    A    ,						max_length=self.max_length    ,						padding=self.padding    ,						pad_to_multiple_of=self.pad_to_multiple_of    ,						return_tensors='''pt'''    ,						)
							lowercase_ : Tuple							       =   self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]   )

							lowercase_ : 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
									lowercase_ : Any							       =   self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1   )   ).to(
									    torch.long   )

									lowercase_ : str							       =   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
									lowercase_ : Optional[Any]							       =   1
									lowercase_ : Tuple							       =   attention_mask.flip([-1]   ).cumsum(-1   ).flip([-1]   ).bool()

							# sample randomly masked indices
							lowercase_ : Union[str, Any]							       =   _compute_mask_indices(
							    (batch_size, mask_indices_seq_length)    ,						self.model.config.mask_time_prob    ,						self.model.config.mask_time_length    ,						attention_mask=A    ,						min_masks=2    ,						)

							return batch





class 					_UpperCAmelCase       ( _A							):




					def __init__(				self     :						Optional[Any]    ,						*A     :						Dict    ,						A     :						Any=1    ,						A     :						Dict=0    ,						A     :						Optional[int]=1.0    ,						**A     :						str   )  ->  Tuple:
							super().__init__(*A    ,						**A   )
							lowercase_ : Any							       =   0
							lowercase_ : Tuple							       =   max_gumbel_temp
							lowercase_ : int							       =   min_gumbel_temp
							lowercase_ : Optional[Any]							       =   gumbel_temp_decay






					def   A  (				self     :						str    ,						A     :						nn.Module    ,						A     :						Dict[str, Union[torch.Tensor, Any]]   )  ->  torch.Tensor:
							model.train()
							lowercase_ : Any							       =   self._prepare_inputs(A   )

							if self.use_amp:
									with autocast():
											lowercase_ : Union[str, Any]							       =   self.compute_loss(A    ,						A   )
							else:
									lowercase_ : str							       =   self.compute_loss(A    ,						A   )

							if self.args.n_gpu > 1 or self.deepspeed:
									if model.module.config.ctc_loss_reduction == "mean":
											lowercase_ : Optional[int]							       =   loss.mean()
									elif model.module.config.ctc_loss_reduction == "sum":
											lowercase_ : List[Any]							       =   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:
									lowercase_ : int							       =   loss / self.args.gradient_accumulation_steps

							if self.use_amp:
									self.scaler.scale(A   ).backward()
							elif self.use_apex:
									with amp.scale_loss(A    ,						self.optimizer   ) as scaled_loss:
											scaled_loss.backward()
							elif self.deepspeed:
									self.deepspeed.backward(A   )
							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 						lowercase    (				):
		# 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.

		lowercase_ : int							       =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)

		lowercase_       ,					lowercase_       ,					lowercase_ : Optional[Any]							       =   parser.parse_args_into_dataclasses()
		configure_logger(__snake_case		,					__snake_case	)

		# Downloading and loading a dataset from the hub.
		lowercase_ : Union[str, Any]							       =   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"
				lowercase_ : Optional[int]							       =   DatasetDict()
				lowercase_ : int							       =   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		,					)
				lowercase_ : 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"
				lowercase_ : Dict							       =   DatasetDict()
				lowercase_ : List[Any]							       =   load_dataset(
				    data_args.dataset_name		,					data_args.dataset_config_name		,					split='''validation'''		,					cache_dir=model_args.cache_dir		,					)
				lowercase_ : Union[str, Any]							       =   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
		lowercase_ : str							       =   WavaVecaFeatureExtractor.from_pretrained(
		    model_args.model_name_or_path		,					cache_dir=model_args.cache_dir		,					do_normalize=__snake_case	)

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

		# load audio files into numpy arrays
		lowercase_ : Dict							       =   datasets.map(
		    __snake_case		,					num_proc=data_args.preprocessing_num_workers		,					remove_columns=datasets['''train'''].column_names	)

		# filter audio files that are too long
		lowercase_ : Union[str, Any]							       =   vectorized_datasets.filter(
		    lambda __snake_case	: len(data['''speech''']	) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate	)	)

		def normalize(__snake_case				: Tuple	):
				return feature_extractor(batch['''speech''']		,					sampling_rate=feature_extractor.sampling_rate	)

		# normalize and transform to `BatchFeatures`
		lowercase_ : Union[str, Any]							       =   vectorized_datasets.map(
		    __snake_case		,					batched=__snake_case		,					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
		lowercase_ : str							       =   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\''''	)

		lowercase_ : Optional[int]							       =   WavaVecaForPreTraining(__snake_case	)

		lowercase_ : Union[str, Any]							       =   DataCollatorForWavaVecaPretraining(model=__snake_case		,					feature_extractor=__snake_case	)

		lowercase_ : Dict							       =   WavaVecaPreTrainer(
		    model=__snake_case		,					data_collator=__snake_case		,					args=__snake_case		,					train_dataset=vectorized_datasets['''train''']		,					eval_dataset=vectorized_datasets['''validation''']		,					tokenizer=__snake_case		,					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 math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__UpperCamelCase						=      {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 131072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						return torch.atana(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) / math.pi * 2


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						int:
						SCREAMING_SNAKE_CASE													=					torch.sin(t * math.pi / 2  ) ** 2
						SCREAMING_SNAKE_CASE													=					(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		pass





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							int:
								super().__init__()

								SCREAMING_SNAKE_CASE													=					DiffusionAttnUnetaD(lowerCAmelCase__						,       n_attn_layers=4				)
								SCREAMING_SNAKE_CASE													=					deepcopy(self.diffusion				)
								SCREAMING_SNAKE_CASE													=					torch.quasirandom.SobolEngine(1						,       scramble=lowerCAmelCase__				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['url']
						os.system(F'wget {url} ./'  )

						return F'./{model_name}.ckpt'


__UpperCamelCase						=      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
__UpperCamelCase						=      {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__UpperCamelCase						=      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
__UpperCamelCase						=      {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

__UpperCamelCase						=      {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

__UpperCamelCase						=      {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						List[Any]:
						if name.startswith('skip'  ):
												return name.replace('skip' ,			RES_CONV_MAP['skip']  )

						# name has to be of format main.{digit}
						if not name.startswith('main.'  ):
												raise ValueError(F'ResConvBlock error with {name}'  )

						return name.replace(name[:6] ,			RES_CONV_MAP[name[:6]]  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						for key, value in ATTN_MAP.items():
												if name.startswith(SCREAMING_SNAKE_CASE_  ) and not isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
																		return name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												elif name.startswith(SCREAMING_SNAKE_CASE_  ):
																		return [name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for v in value]
						raise ValueError(F'Attn error with {name}'  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple=13  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					input_string

						if string.split('.'  )[0] == "timestep_embed":
												return string.replace('timestep_embed' ,			'time_proj'  )

						SCREAMING_SNAKE_CASE													=					0
						if string.startswith('net.3.'  ):
												depth += 1
												SCREAMING_SNAKE_CASE													=					string[6:]
						elif string.startswith('net.'  ):
												SCREAMING_SNAKE_CASE													=					string[4:]

						while string.startswith('main.7.'  ):
												depth += 1
												SCREAMING_SNAKE_CASE													=					string[7:]

						if string.startswith('main.'  ):
												SCREAMING_SNAKE_CASE													=					string[5:]

						# mid block
						if string[:2].isdigit():
												SCREAMING_SNAKE_CASE													=					string[:2]
												SCREAMING_SNAKE_CASE													=					string[2:]
						else:
												SCREAMING_SNAKE_CASE													=					string[0]
												SCREAMING_SNAKE_CASE													=					string[1:]

						if depth == max_depth:
												SCREAMING_SNAKE_CASE													=					MID_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					'mid_block'
						elif depth > 0 and int(SCREAMING_SNAKE_CASE_  ) < 7:
												SCREAMING_SNAKE_CASE													=					DOWN_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					F'down_blocks.{depth}'
						elif depth > 0 and int(SCREAMING_SNAKE_CASE_  ) > 7:
												SCREAMING_SNAKE_CASE													=					UP_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					F'up_blocks.{max_depth - depth - 1}'
						elif depth == 0:
												SCREAMING_SNAKE_CASE													=					DEPTH_0_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					F'up_blocks.{max_depth - 1}' if int(SCREAMING_SNAKE_CASE_  ) > 3 else 'down_blocks.0'

						if not string_left.startswith('.'  ):
												raise ValueError(F'Naming error with {input_string} and string_left: {string_left}.'  )

						SCREAMING_SNAKE_CASE													=					string_left[1:]

						if "resnets" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_resconv_naming(SCREAMING_SNAKE_CASE_  )
						elif "attentions" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_attn_naming(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					new_string_left

						if not isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
												SCREAMING_SNAKE_CASE													=					prefix + '.' + new_layer + '.' + string_left
						else:
												SCREAMING_SNAKE_CASE													=					[prefix + '.' + new_layer + '.' + s for s in string_left]
						return new_string


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					{}
						for k, v in state_dict.items():
												if k.endswith('kernel'  ):
																		# up- and downsample layers, don't have trainable weights
																		continue

												SCREAMING_SNAKE_CASE													=					rename(SCREAMING_SNAKE_CASE_  )

												# check if we need to transform from Conv => Linear for attention
												if isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					transform_conv_attns(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												else:
																		SCREAMING_SNAKE_CASE													=					v

						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						str:
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												if len(v.shape  ) == 3:
																		# weight
																		SCREAMING_SNAKE_CASE													=					v[:, :, 0]
												else:
																		# bias
																		SCREAMING_SNAKE_CASE													=					v
						else:
												# qkv matrices
												SCREAMING_SNAKE_CASE													=					v.shape[0]
												SCREAMING_SNAKE_CASE													=					trippled_shape // 3
												for i in range(3  ):
																		if len(v.shape  ) == 3:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

						SCREAMING_SNAKE_CASE													=					args.model_path.split('/'  )[-1].split('.'  )[0]
						if not os.path.isfile(args.model_path  ):
												assert (
												    model_name == args.model_path
												), F'Make sure to provide one of the official model names {MODELS_MAP.keys()}'
												SCREAMING_SNAKE_CASE													=					download(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['sample_rate']
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['sample_size']

						SCREAMING_SNAKE_CASE													=					Object()
						SCREAMING_SNAKE_CASE													=					sample_size
						SCREAMING_SNAKE_CASE													=					sample_rate
						SCREAMING_SNAKE_CASE													=					0

						SCREAMING_SNAKE_CASE													=					UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_ ,			sample_rate=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					diffusers_model.state_dict()

						SCREAMING_SNAKE_CASE													=					DiffusionUncond(SCREAMING_SNAKE_CASE_  )
						orig_model.load_state_dict(torch.load(args.model_path ,			map_location=SCREAMING_SNAKE_CASE_  )['state_dict']  )
						SCREAMING_SNAKE_CASE													=					orig_model.diffusion_ema.eval()
						SCREAMING_SNAKE_CASE													=					orig_model.state_dict()
						SCREAMING_SNAKE_CASE													=					rename_orig_weights(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					set(renamed_state_dict.keys()  ) - set(diffusers_state_dict.keys()  )
						SCREAMING_SNAKE_CASE													=					set(diffusers_state_dict.keys()  ) - set(renamed_state_dict.keys()  )

						assert len(SCREAMING_SNAKE_CASE_  ) == 0, F'Problem with {renamed_minus_diffusers}'
						assert all(k.endswith('kernel'  ) for k in list(SCREAMING_SNAKE_CASE_  )  ), F'Problem with {diffusers_minus_renamed}'

						for key, value in renamed_state_dict.items():
												assert (
												    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
												), F'Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'
												if key == "time_proj.weight":
																		SCREAMING_SNAKE_CASE													=					value.squeeze()

												SCREAMING_SNAKE_CASE													=					value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					1_00
						SCREAMING_SNAKE_CASE													=					33

						SCREAMING_SNAKE_CASE													=					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.manual_seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					torch.randn([1, 2, config.sample_size] ,			generator=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.linspace(1 ,			0 ,			steps + 1 ,			device=SCREAMING_SNAKE_CASE_  )[:-1]
						SCREAMING_SNAKE_CASE													=					get_crash_schedule(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ ,			scheduler=SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.manual_seed(33  )
						SCREAMING_SNAKE_CASE													=					pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ ,			generator=SCREAMING_SNAKE_CASE_  ).audios

						SCREAMING_SNAKE_CASE													=					sampling.iplms_sample(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			{}  )
						SCREAMING_SNAKE_CASE													=					generated.clamp(-1 ,			1  )

						SCREAMING_SNAKE_CASE													=					(generated - audio).abs().sum()
						SCREAMING_SNAKE_CASE													=					(generated - audio).abs().max()

						if args.save:
												pipe.save_pretrained(args.checkpoint_path  )

						print('Diff sum' ,			SCREAMING_SNAKE_CASE_  )
						print('Diff max' ,			SCREAMING_SNAKE_CASE_  )

						assert diff_max < 1E-3, F'Diff max: {diff_max} is too much :-/'

						print(F'Conversion for {model_name} successful!'  )


if __name__ == "__main__":
	__UpperCamelCase						=      argparse.ArgumentParser()

	parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
	parser.add_argument(
	    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
	)
	parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
	__UpperCamelCase						=      parser.parse_args()

	main(args)


