'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowercase__ )
         return config






    def       _UpperCamelCase    (       self )							-> int:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
              self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.check_over_configs(thresholding=lowercase__ )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> Dict:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
         assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
         assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
         assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

         for i, timestep in enumerate(lowercase__ ):
              if i == len(lowercase__ ) - 1:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

              SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

              self.assertEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

         with self.assertRaises(lowercase__    ,							msg='`custom_timesteps` must be in descending order.' ):
              scheduler.set_timesteps(timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

         with self.assertRaises(lowercase__    ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
              scheduler.set_timesteps(num_inference_steps=lowercase__    ,							timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             lowercase__    ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,							):
              scheduler.set_timesteps(timesteps=lowercase__ )




'''simple docstring'''





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     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       args.log_outputs
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       '_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     SCREAMING_SNAKE_CASE					:			int    	=       load_metric('wer'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_metric('cer'		)

     # compute metrics
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       wer.compute(references=result['target']       ,      predictions=result['prediction']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cer.compute(references=result['target']       ,      predictions=result['prediction']		)

     # print & log results
     SCREAMING_SNAKE_CASE					:			int    	=       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:
          SCREAMING_SNAKE_CASE					:			Any    	=       f"""log_{dataset_id}_predictions.txt"""
          SCREAMING_SNAKE_CASE					:			int    	=       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     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





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

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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!
     SCREAMING_SNAKE_CASE					:			str    	=       ['\n\n', '\n', '   ', '  ']

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       ' '.join(text.split(a_		)		)

     return text


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       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
     SCREAMING_SNAKE_CASE					:			str    	=       AutoFeatureExtractor.from_pretrained(args.model_id		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       dataset.cast_column('audio'       ,      Audio(sampling_rate=a_		)		)

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

     # map function to decode audio
     def map_to_pred(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       asr(
              batch['audio']['array']       ,      chunk_length_s=args.chunk_length_s       ,      stride_length_s=args.stride_length_s		)

          SCREAMING_SNAKE_CASE					:			str    	=       prediction['text']
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset.map(a_       ,      remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[Any]  				=    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.""",
 )
 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 main(args)



