
"""simple docstring"""






import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__SCREAMING_SNAKE_CASE							=     logging.getLogger(__name__)





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''token-classification'''



     def __init__(							self :Optional[Any]      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      if type(_lowerCamelCase   ) == dict:
       UpperCamelCase_     :					int   	=Namespace(**_lowerCamelCase   )
      UpperCamelCase_     :					Dict   	=import_module('tasks'   )
      try:
       UpperCamelCase_     :					Union[str, Any]   	=getattr(_lowerCamelCase      ,  hparams.task_type   )
       UpperCamelCase_     :					TokenClassificationTask   	=token_classification_task_clazz()
      except AttributeError:
       raise ValueError(
           f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
           f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''   )
      UpperCamelCase_     :					Optional[Any]   	=self.token_classification_task.get_labels(hparams.labels   )
      UpperCamelCase_     :					Dict   	=CrossEntropyLoss().ignore_index
      super().__init__(_lowerCamelCase      ,  len(self.labels   )      ,  self.mode   )



     def  lowerCamelCase_						(							self :int      ,  **_lowerCamelCase :List[str]   ):







      '''simple docstring'''



      return self.model(**_lowerCamelCase   )



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
      if self.config.model_type != "distilbert":
       UpperCamelCase_     :					int   	=(
           batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
       )  # XLM and RoBERTa don"t use token_type_ids

      UpperCamelCase_     :					Tuple   	=self(**_lowerCamelCase   )
      UpperCamelCase_     :					List[Any]   	=outputs[0]
      # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
      return {"loss": loss}



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.hparams
      for mode in ["train", "dev", "test"]:
       UpperCamelCase_     :					List[str]   	=self._feature_file(_lowerCamelCase   )
       if os.path.exists(_lowerCamelCase   ) and not args.overwrite_cache:
        logger.info('Loading features from cached file %s'      ,  _lowerCamelCase   )
        UpperCamelCase_     :					List[str]   	=torch.load(_lowerCamelCase   )
       else:
        logger.info('Creating features from dataset file at %s'      ,  args.data_dir   )
        UpperCamelCase_     :					Union[str, Any]   	=self.token_classification_task.read_examples_from_file(args.data_dir      ,  _lowerCamelCase   )
        UpperCamelCase_     :					Any   	=self.token_classification_task.convert_examples_to_features(
            _lowerCamelCase      ,  self.labels      ,  args.max_seq_length      ,  self.tokenizer      ,  cls_token_at_end=bool(self.config.model_type in ['xlnet']   )      ,  cls_token=self.tokenizer.cls_token      ,  cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0      ,  sep_token=self.tokenizer.sep_token      ,  sep_token_extra=_lowerCamelCase      ,  pad_on_left=bool(self.config.model_type in ['xlnet']   )      ,  pad_token=self.tokenizer.pad_token_id      ,  pad_token_segment_id=self.tokenizer.pad_token_type_id      ,  pad_token_label_id=self.pad_token_label_id      ,  )
        logger.info('Saving features into cached file %s'      ,  _lowerCamelCase   )
        torch.save(_lowerCamelCase      ,  _lowerCamelCase   )



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=self._feature_file(_lowerCamelCase   )
      logger.info('Loading features from cached file %s'      ,  _lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=torch.load(_lowerCamelCase   )
      UpperCamelCase_     :					int   	=torch.tensor([f.input_ids for f in features]      ,  dtype=torch.long   )
      UpperCamelCase_     :					Optional[Any]   	=torch.tensor([f.attention_mask for f in features]      ,  dtype=torch.long   )
      if features[0].token_type_ids is not None:
       UpperCamelCase_     :					Any   	=torch.tensor([f.token_type_ids for f in features]      ,  dtype=torch.long   )
      else:
       UpperCamelCase_     :					int   	=torch.tensor([0 for f in features]      ,  dtype=torch.long   )
       # HACK(we will not use this anymore soon)
      UpperCamelCase_     :					str   	=torch.tensor([f.label_ids for f in features]      ,  dtype=torch.long   )
      return DataLoader(
          TensorDataset(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase   )      ,  batch_size=_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      """Compute validation""" ""
      UpperCamelCase_     :					str   	={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
      if self.config.model_type != "distilbert":
       UpperCamelCase_     :					Optional[int]   	=(
           batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
       )  # XLM and RoBERTa don"t use token_type_ids
      UpperCamelCase_     :					Optional[int]   	=self(**_lowerCamelCase   )
      UpperCamelCase_	,     UpperCamelCase_     :					List[Any]   	=outputs[:2]
      UpperCamelCase_     :					Any   	=logits.detach().cpu().numpy()
      UpperCamelCase_     :					Optional[int]   	=inputs['labels'].detach().cpu().numpy()
      return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=torch.stack([x['val_loss'] for x in outputs]   ).mean()
      UpperCamelCase_     :					List[Any]   	=np.concatenate([x['pred'] for x in outputs]      ,  axis=0   )
      UpperCamelCase_     :					str   	=np.argmax(_lowerCamelCase      ,  axis=2   )
      UpperCamelCase_     :					Any   	=np.concatenate([x['target'] for x in outputs]      ,  axis=0   )

      UpperCamelCase_     :					Dict   	=dict(enumerate(self.labels   )   )
      UpperCamelCase_     :					List[Any]   	=[[] for _ in range(out_label_ids.shape[0]   )]
      UpperCamelCase_     :					str   	=[[] for _ in range(out_label_ids.shape[0]   )]

      for i in range(out_label_ids.shape[0]   ):
       for j in range(out_label_ids.shape[1]   ):
        if out_label_ids[i, j] != self.pad_token_label_id:
         out_label_list[i].append(label_map[out_label_ids[i][j]]   )
         preds_list[i].append(label_map[preds[i][j]]   )

      UpperCamelCase_     :					int   	={
          'val_loss': val_loss_mean,
          'accuracy_score': accuracy_score(_lowerCamelCase      ,  _lowerCamelCase   ),
          'precision': precision_score(_lowerCamelCase      ,  _lowerCamelCase   ),
          'recall': recall_score(_lowerCamelCase      ,  _lowerCamelCase   ),
          'f1': fa_score(_lowerCamelCase      ,  _lowerCamelCase   ),
      }

      UpperCamelCase_     :					Any   	=dict(results.items()   )
      UpperCamelCase_     :					Dict   	=results
      return ret, preds_list, out_label_list



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=self._eval_end(_lowerCamelCase   )
      UpperCamelCase_     :					str   	=ret['log']
      return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					Optional[int]   	=self._eval_end(_lowerCamelCase   )

      # Converting to the dict required by pl
      # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
      # pytorch_lightning/trainer/logging.py#L139
      UpperCamelCase_     :					Optional[Any]   	=ret['log']
      # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
      return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




     @staticmethod
     def  lowerCamelCase_						(							_lowerCamelCase :Tuple      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      BaseTransformer.add_model_specific_args(_lowerCamelCase      ,  _lowerCamelCase   )
      parser.add_argument(
          '--task_type'      ,  default='NER'      ,  type=_lowerCamelCase      ,  help='Task type to fine tune in training (e.g. NER, POS, etc)'   )
      parser.add_argument(
          '--max_seq_length'      ,  default=128      ,  type=_lowerCamelCase      ,  help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )      ,  )

      parser.add_argument(
          '--labels'      ,  default=''      ,  type=_lowerCamelCase      ,  help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'      ,  )
      parser.add_argument(
          '--gpus'      ,  default=0      ,  type=_lowerCamelCase      ,  help='The number of GPUs allocated for this, it is by default 0 meaning none'      ,  )

      parser.add_argument(
          '--overwrite_cache'      ,  action='store_true'      ,  help='Overwrite the cached training and evaluation sets'   )

      return parser


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     argparse.ArgumentParser()
 add_generic_args(parser, os.getcwd())
 __SCREAMING_SNAKE_CASE							=     NERTransformer.add_model_specific_args(parser, os.getcwd())
 __SCREAMING_SNAKE_CASE							=     parser.parse_args()
 __SCREAMING_SNAKE_CASE							=     NERTransformer(args)
 __SCREAMING_SNAKE_CASE							=     generic_train(model, args)

 if args.do_predict:
  # See https://github.com/huggingface/transformers/issues/3159
  # pl use this default format to create a checkpoint:
  # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
  # /pytorch_lightning/callbacks/model_checkpoint.py#L322
  __SCREAMING_SNAKE_CASE							=     sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
  __SCREAMING_SNAKE_CASE							=     model.load_from_checkpoint(checkpoints[-1])
  trainer.test(model)


"""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


__SCREAMING_SNAKE_CASE							=     {
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 65_536,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 65_536,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 131_072,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
}




def       A_  (				__lowercase						,	__lowercase	):
 return torch.atana(__lowercase						,	__lowercase	) / math.pi * 2




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=torch.sin(t * math.pi / 2	) ** 2
 UpperCamelCase_     :					Union[str, Any]   	=(1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(__lowercase						,	__lowercase	)





class 				a__					(						A__       ):
     pass





class 				a__					(						nn.Module       ):



     def __init__(							self :Any      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__()

      UpperCamelCase_     :					Optional[int]   	=DiffusionAttnUnetaD(_lowerCamelCase      ,  n_attn_layers=4   )
      UpperCamelCase_     :					List[str]   	=deepcopy(self.diffusion   )
      UpperCamelCase_     :					int   	=torch.quasirandom.SobolEngine(1      ,  scramble=_lowerCamelCase   )




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=MODELS_MAP[model_name]['url']
 os.system(F'''wget {url} ./'''	)

 return F'''./{model_name}.ckpt'''


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

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

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




def       A_  (				__lowercase	):
 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       A_  (				__lowercase	):
 for key, value in ATTN_MAP.items():
  if name.startswith(__lowercase	) and not isinstance(__lowercase						,	__lowercase	):
   return name.replace(__lowercase						,	__lowercase	)
  elif name.startswith(__lowercase	):
   return [name.replace(__lowercase						,	__lowercase	) for v in value]
 raise ValueError(F'''Attn error with {name}'''	)




def       A_  (				__lowercase						,	__lowercase=13	):
 UpperCamelCase_     :					Optional[Any]   	=input_string

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

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

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

 if string.startswith('main.'	):
  UpperCamelCase_     :					Union[str, Any]   	=string[5:]

 # mid block
 if string[:2].isdigit():
  UpperCamelCase_     :					List[str]   	=string[:2]
  UpperCamelCase_     :					Any   	=string[2:]
 else:
  UpperCamelCase_     :					List[Any]   	=string[0]
  UpperCamelCase_     :					Tuple   	=string[1:]

 if depth == max_depth:
  UpperCamelCase_     :					Optional[Any]   	=MID_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Optional[int]   	='mid_block'
 elif depth > 0 and int(__lowercase	) < 7:
  UpperCamelCase_     :					int   	=DOWN_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Dict   	=F'''down_blocks.{depth}'''
 elif depth > 0 and int(__lowercase	) > 7:
  UpperCamelCase_     :					Optional[Any]   	=UP_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Tuple   	=F'''up_blocks.{max_depth - depth - 1}'''
 elif depth == 0:
  UpperCamelCase_     :					Optional[int]   	=DEPTH_0_TO_LAYER[layer_num]
  UpperCamelCase_     :					List[str]   	=F'''up_blocks.{max_depth - 1}''' if int(__lowercase	) > 3 else 'down_blocks.0'

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

 UpperCamelCase_     :					Union[str, Any]   	=string_left[1:]

 if "resnets" in new_layer:
  UpperCamelCase_     :					Dict   	=convert_resconv_naming(__lowercase	)
 elif "attentions" in new_layer:
  UpperCamelCase_     :					List[Any]   	=convert_attn_naming(__lowercase	)
  UpperCamelCase_     :					str   	=new_string_left

 if not isinstance(__lowercase						,	__lowercase	):
  UpperCamelCase_     :					int   	=prefix + '.' + new_layer + '.' + string_left
 else:
  UpperCamelCase_     :					List[Any]   	=[prefix + '.' + new_layer + '.' + s for s in string_left]
 return new_string




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	={}
 for k, v in state_dict.items():
  if k.endswith('kernel'	):
   # up- and downsample layers, don't have trainable weights
   continue

  UpperCamelCase_     :					Optional[Any]   	=rename(__lowercase	)

  # check if we need to transform from Conv => Linear for attention
  if isinstance(__lowercase						,	__lowercase	):
   UpperCamelCase_     :					Union[str, Any]   	=transform_conv_attns(__lowercase						,	__lowercase						,	__lowercase	)
  else:
   UpperCamelCase_     :					Union[str, Any]   	=v

 return new_state_dict




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) == 1:
  if len(v.shape	) == 3:
   # weight
   UpperCamelCase_     :					Tuple   	=v[:, :, 0]
  else:
   # bias
   UpperCamelCase_     :					Any   	=v
 else:
  # qkv matrices
  UpperCamelCase_     :					Tuple   	=v.shape[0]
  UpperCamelCase_     :					Optional[Any]   	=trippled_shape // 3
  for i in range(3	):
   if len(v.shape	) == 3:
    UpperCamelCase_     :					str   	=v[i * single_shape : (i + 1) * single_shape, :, 0]
   else:
    UpperCamelCase_     :					List[str]   	=v[i * single_shape : (i + 1) * single_shape]
 return new_state_dict




def       A_  (				__lowercase	):
 UpperCamelCase_     :					int   	=torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

 UpperCamelCase_     :					Dict   	=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()}'''
  UpperCamelCase_     :					Optional[Any]   	=download(__lowercase	)

 UpperCamelCase_     :					str   	=MODELS_MAP[model_name]['sample_rate']
 UpperCamelCase_     :					Any   	=MODELS_MAP[model_name]['sample_size']

 UpperCamelCase_     :					str   	=Object()
 UpperCamelCase_     :					List[str]   	=sample_size
 UpperCamelCase_     :					Optional[int]   	=sample_rate
 UpperCamelCase_     :					Tuple   	=0

 UpperCamelCase_     :					Optional[int]   	=UNetaDModel(sample_size=__lowercase						,	sample_rate=__lowercase	)
 UpperCamelCase_     :					Union[str, Any]   	=diffusers_model.state_dict()

 UpperCamelCase_     :					Any   	=DiffusionUncond(__lowercase	)
 orig_model.load_state_dict(torch.load(args.model_path						,	map_location=__lowercase	)['state_dict']	)
 UpperCamelCase_     :					List[str]   	=orig_model.diffusion_ema.eval()
 UpperCamelCase_     :					Tuple   	=orig_model.state_dict()
 UpperCamelCase_     :					Optional[int]   	=rename_orig_weights(__lowercase	)

 UpperCamelCase_     :					Any   	=set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
 UpperCamelCase_     :					Tuple   	=set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

 assert len(__lowercase	) == 0, F'''Problem with {renamed_minus_diffusers}'''
 assert all(k.endswith('kernel'	) for k in list(__lowercase	)	), 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":
   UpperCamelCase_     :					List[str]   	=value.squeeze()

  UpperCamelCase_     :					str   	=value

 diffusers_model.load_state_dict(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=1_00
 UpperCamelCase_     :					List[str]   	=33

 UpperCamelCase_     :					Any   	=IPNDMScheduler(num_train_timesteps=__lowercase	)

 UpperCamelCase_     :					List[Any]   	=torch.manual_seed(__lowercase	)
 UpperCamelCase_     :					List[str]   	=torch.randn([1, 2, config.sample_size]						,	generator=__lowercase	).to(__lowercase	)

 UpperCamelCase_     :					str   	=torch.linspace(1						,	0						,	steps + 1						,	device=__lowercase	)[:-1]
 UpperCamelCase_     :					Dict   	=get_crash_schedule(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=DanceDiffusionPipeline(unet=__lowercase						,	scheduler=__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=torch.manual_seed(33	)
 UpperCamelCase_     :					Optional[Any]   	=pipe(num_inference_steps=__lowercase						,	generator=__lowercase	).audios

 UpperCamelCase_     :					Dict   	=sampling.iplms_sample(__lowercase						,	__lowercase						,	__lowercase						,	{}	)
 UpperCamelCase_     :					List[Any]   	=generated.clamp(-1						,	1	)

 UpperCamelCase_     :					Optional[int]   	=(generated - audio).abs().sum()
 UpperCamelCase_     :					Union[str, Any]   	=(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     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.')
 __SCREAMING_SNAKE_CASE							=     parser.parse_args()

 main(args)
