





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__(       UpperCamelCase__			:       Dataset ,     UpperCamelCase__			:       Dict[str, str]  )->Any:
       A__											=						args.log_outputs
       A__											=						'''_'''.join(args.dataset.split('''/'''  ) + [args.config, args.split]  )

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

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

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

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

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

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

                     result.map(UpperCamelCase__ ,     with_indices=UpperCamelCase__  )


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

       A__											=						re.sub(UpperCamelCase__ ,     '''''' ,     text.lower()  )

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

       for t in token_sequences_to_ignore:
              A__											=						''' '''.join(text.split(UpperCamelCase__  )  )

       return text


def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # load dataset
       A__											=						load_dataset(args.dataset ,     args.config ,     split=args.split ,     use_auth_token=UpperCamelCase__  )

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

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

       # resample audio
       A__											=						dataset.cast_column('''audio''' ,     Audio(sampling_rate=UpperCamelCase__  )  )

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

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

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

       # run inference on all examples
       A__											=						dataset.map(UpperCamelCase__ ,     remove_columns=dataset.column_names  )

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


if __name__ == "__main__":
       a__:      Tuple									=		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__:      Tuple									=		parser.parse_args()

       main(args)












import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



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



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

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



 def 				UpperCamelCase			(							self ):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],[0.002, 0.02, 0.2, 2] ):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase )



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



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



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



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



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



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



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 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 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

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

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

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





