


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

       # load metric
       lowercase		:str       							=				load_metric('''wer''')
       lowercase		:Tuple       							=				load_metric('''cer''')

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

       # print & log results
       lowercase		:str       							=				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:
              lowercase		:Optional[int]       							=				F"""log_{dataset_id}_predictions.txt"""
              lowercase		:Any       							=				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_						:Optional[int]				,				a_						:Any):
                            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)     ->							str:
       lowercase		:int       							=				'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       lowercase		:Optional[int]       							=				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!
       lowercase		:Union[str, Any]       							=				['''\n\n''', '''\n''', '''   ''', '''  ''']

       for t in token_sequences_to_ignore:
              lowercase		:Optional[int]       							=				''' '''.join(text.split(a_))

       return text






def       lowerCamelCase					(a_						:str)     ->							Optional[Any]:
       # load dataset
       lowercase		:Any       							=				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
       lowercase		:int       							=				AutoFeatureExtractor.from_pretrained(args.model_id)
       lowercase		:Optional[int]       							=				feature_extractor.sampling_rate

       # resample audio
       lowercase		:Tuple       							=				dataset.cast_column('''audio'''				,				Audio(sampling_rate=a_))

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

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

              lowercase		:Tuple       							=				prediction['''text''']
              lowercase		:List[str]       							=				normalize_text(batch['''sentence'''])
              return batch

       # run inference on all examples
       lowercase		: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__":
 UpperCAmelCase      		=			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.''',
 )
 UpperCAmelCase      		=			parser.parse_args()

 main(args)





"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    UNetaDModel  ,     snake_case__  :    ScoreSdeVeScheduler				):

              '''simple docstring'''






              super().__init__()
              self.register_modules(unet=snake_case__  ,     scheduler=snake_case__				)







       @torch.no_grad()
       def __call__(   self  :    Tuple  ,     snake_case__  :    int = 1  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				sample.to(self.device				)

              self.scheduler.set_timesteps(snake_case__				)
              self.scheduler.set_sigmas(snake_case__				)

              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

                     # correction step
                     for _ in range(self.scheduler.config.correct_steps				):
                            lowercase		:str       							=				self.unet(snake_case__  ,     snake_case__				).sample
                            lowercase		:Optional[Any]       							=				self.scheduler.step_correct(snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

