




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (DDIMParallelScheduler,)
		__snake_case									=   (('eta', 0.0), ('num_inference_steps', 50))



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(**UpperCamelCase__   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_			,					lowerCAmelCase_      			=  10, 0.0

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter

								scheduler.set_timesteps(UpperCamelCase__   )

								for t in scheduler.timesteps:
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ).prev_sample

								return sample



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [100, 500, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=UpperCamelCase__   )

								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(steps_offset=1   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )
								scheduler.set_timesteps(5   )
								assert torch.equal(scheduler.timesteps,	torch.LongTensor([801, 601, 401, 201, 1]   )   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1],	[0.002, 0.02, 0.2, 2]   ):
														self.check_over_configs(beta_start=UpperCamelCase__,	beta_end=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timestep_spacing in ["trailing", "leading"]:
														self.check_over_configs(timestep_spacing=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for rescale_betas_zero_snr in [True, False]:
														self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.check_over_configs(thresholding=UpperCamelCase__   )
								for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(
																				    thresholding=UpperCamelCase__,	prediction_type=UpperCamelCase__,	sample_max_value=UpperCamelCase__,	)



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for t in [1, 10, 49]:
														self.check_over_forward(time_step=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for t, num_inference_steps in zip([1, 10, 50],	[10, 50, 500]   ):
														self.check_over_forward(time_step=UpperCamelCase__,	num_inference_steps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for t, eta in zip([1, 10, 49],	[0.0, 0.5, 1.0]   ):
														self.check_over_forward(time_step=UpperCamelCase__,	eta=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0,	0   ) - 0.0   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(420,	400   ) - 0.14_771   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(980,	960   ) - 0.32_460   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0,	0   ) - 0.0   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487,	486   ) - 0.00_979   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999,	998   ) - 0.02   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_			,					lowerCAmelCase_      			=  10, 0.0
								scheduler.set_timesteps(UpperCamelCase__   )

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  self.dummy_sample_deter + 0.1
								lowerCAmelCase_      			=  self.dummy_sample_deter - 0.1

								lowerCAmelCase_      			=  samplea.shape[0]
								lowerCAmelCase_      			=  torch.stack([samplea, samplea, samplea],	dim=0   )
								lowerCAmelCase_      			=  torch.arange(UpperCamelCase__   )[0:3, None].repeat(1,	UpperCamelCase__   )

								lowerCAmelCase_      			=  model(samples.flatten(0,	1   ),	timesteps.flatten(0,	1   )   )
								lowerCAmelCase_      			=  scheduler.batch_step_no_noise(UpperCamelCase__,	timesteps.flatten(0,	1   ),	samples.flatten(0,	1   ),	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 1_147.7_904   ) < 1E-2
								assert abs(result_mean.item() - 0.4_982   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.full_loop()

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 172.0_067   ) < 1E-2
								assert abs(result_mean.item() - 0.223_967   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.full_loop(prediction_type='''v_prediction'''   )

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 52.5_302   ) < 1E-2
								assert abs(result_mean.item() - 0.0_684   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.full_loop(set_alpha_to_one=UpperCamelCase__,	beta_start=0.01   )
								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 149.8_295   ) < 1E-2
								assert abs(result_mean.item() - 0.1_951   ) < 1E-3







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.full_loop(set_alpha_to_one=UpperCamelCase__,	beta_start=0.01   )
								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 149.0_784   ) < 1E-2
								assert abs(result_mean.item() - 0.1_941   ) < 1E-3










import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline





def __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  args.log_outputs
						lowerCAmelCase_      			=  '''_'''.join(args.dataset.split('''/'''  ) + [args.config, args.split]  )

						# load metric
						lowerCAmelCase_      			=  load_metric('''wer'''  )
						lowerCAmelCase_      			=  load_metric('''cer'''  )

						# compute metrics
						lowerCAmelCase_      			=  wer.compute(references=result['''target'''] ,  predictions=result['''prediction''']  )
						lowerCAmelCase_      			=  cer.compute(references=result['''target'''] ,  predictions=result['''prediction''']  )

						# print & log results
						lowerCAmelCase_      			=  f"WER: {wer_result}\nCER: {cer_result}"
						print(_A  )

						with open(f"{dataset_id}_eval_results.txt" ,  '''w'''  ) as f:
												f.write(_A  )

						# log all results in text file. Possibly interesting for analysis
						if log_outputs is not None:
												lowerCAmelCase_      			=  f"log_{dataset_id}_predictions.txt"
												lowerCAmelCase_      			=  f"log_{dataset_id}_targets.txt"

												with open(_A ,  '''w'''  ) as p, open(_A ,  '''w'''  ) as t:
																		# mapping function to write output
																		def write_to_file(_A ,  _A  ):
																								p.write(f"{i}" + '''\n'''  )
																								p.write(batch['''prediction'''] + '''\n'''  )
																								t.write(f"{i}" + '''\n'''  )
																								t.write(batch['''target'''] + '''\n'''  )

																		result.map(_A ,  with_indices=_A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

						lowerCAmelCase_      			=  re.sub(_A ,  '''''' ,  text.lower()  )

						# In addition, we can normalize the target text, e.g. removing new lines characters etc...
						# note that order is important here!
						lowerCAmelCase_      			=  ['''\n\n''', '''\n''', '''   ''', '''  ''']

						for t in token_sequences_to_ignore:
												lowerCAmelCase_      			=  ''' '''.join(text.split(_A  )  )

						return text





def __UpperCamelCase			(       _A  ):
						# load dataset
						lowerCAmelCase_      			=  load_dataset(args.dataset ,  args.config ,  split=args.split ,  use_auth_token=_A  )

						# for testing: only process the first two examples as a test
						# dataset = dataset.select(range(10))

						# load processor
						lowerCAmelCase_      			=  AutoFeatureExtractor.from_pretrained(args.model_id  )
						lowerCAmelCase_      			=  feature_extractor.sampling_rate

						# resample audio
						lowerCAmelCase_      			=  dataset.cast_column('''audio''' ,  Audio(sampling_rate=_A  )  )

						# load eval pipeline
						if args.device is None:
												lowerCAmelCase_      			=  0 if torch.cuda.is_available() else -1
						lowerCAmelCase_      			=  pipeline('''automatic-speech-recognition''' ,  model=args.model_id ,  device=args.device  )

						# map function to decode audio
						def map_to_pred(_A  ):
												lowerCAmelCase_      			=  asr(
												    batch['''audio''']['''array'''] ,  chunk_length_s=args.chunk_length_s ,  stride_length_s=args.stride_length_s  )

												lowerCAmelCase_      			=  prediction['''text''']
												lowerCAmelCase_      			=  normalize_text(batch['''sentence''']  )
												return batch

						# run inference on all examples
						lowerCAmelCase_      			=  dataset.map(_A ,  remove_columns=dataset.column_names  )

						# compute and log_results
						# do not change function below
						log_results(_A ,  _A  )


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

						parser.add_argument(
						    '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
						)
						parser.add_argument(
						    '''--dataset''',
						    type=str,
						    required=True,
						    help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
						)
						parser.add_argument(
						    '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
						)
						parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
						parser.add_argument(
						    '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
						)
						parser.add_argument(
						    '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
						)
						parser.add_argument(
						    '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
						)
						parser.add_argument(
						    '''--device''',
						    type=int,
						    default=None,
						    help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
						)
						_A     =			parser.parse_args()

						main(args)




