



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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __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   , _UpperCAmelCase				):
                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      __snake_case	(			_UpperCAmelCase				):
    __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      __snake_case	(			_UpperCAmelCase				):
    # 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				):
        __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__":
 __snake_case						:Optional[int]      		=     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.''',
 )
 __snake_case						:Tuple      		=     parser.parse_args()

 main(args)












"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:int     ,       __UpperCamelCase			:Distribution     ,       __UpperCamelCase			:Dict=None     ,       __UpperCamelCase			:Optional[int]=None     ,       __UpperCamelCase			:List[str]=0		):
												A		=  1.0 if scale is None else scale
												A		=  0.0 if loc is None else loc

												super().__init__(__UpperCamelCase     ,       [AffineTransform(loc=self.loc     ,       scale=self.scale     ,       event_dim=__UpperCamelCase		)]		)


					@property
					def 	lowerCamelCase       (					self			:Any		):
												return self.base_dist.mean * self.scale + self.loc


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												return self.base_dist.variance * self.scale**2


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												return self.variance.sqrt()







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:Callable[..., Tuple[torch.Tensor]]     ,       **__UpperCamelCase			:str		):
												super().__init__(**__UpperCamelCase		)
												A		=  args_dim
												A		=  nn.ModuleList([nn.Linear(__UpperCamelCase     ,       __UpperCamelCase		) for dim in args_dim.values()]		)
												A		=  domain_map


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  [proj(__UpperCamelCase		) for proj in self.proj]

												return self.domain_map(*__UpperCamelCase		)







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int		):
												super().__init__()
												A		=  function


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Any     ,       *__UpperCamelCase			:Any		):
												return self.function(__UpperCamelCase     ,       *__UpperCamelCase		)







class 					_UpperCAmelCase   :
					UpperCamelCase      =							42
					UpperCamelCase      =							42
					UpperCamelCase      =							42


					def __init__(					self			:Any     ,       __UpperCamelCase			:int = 1		):
												A		=  dim
												A		=  {k: dim * self.args_dim[k] for k in self.args_dim}


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Dict		):
												if self.dim == 1:
																			return self.distribution_class(*__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(*__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       ):
												A		=  self._base_distribution(__UpperCamelCase		)
												if loc is None and scale is None:
																			return distr
												else:
																			return AffineTransformed(__UpperCamelCase     ,       loc=__UpperCamelCase     ,       scale=__UpperCamelCase     ,       event_dim=self.event_dim		)


					@property
					def 	lowerCamelCase       (					self			:List[Any]		):
												return () if self.dim == 1 else (self.dim,)


					@property
					def 	lowerCamelCase       (					self			:Tuple		):
												return len(self.event_shape		)


					@property
					def 	lowerCamelCase       (					self			:int		):
												return 0.0


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:int		):
												return ParameterProjection(
												    in_features=__UpperCamelCase     ,       args_dim=self.args_dim     ,       domain_map=LambdaLayer(self.domain_map		)     ,       )


					def 	lowerCamelCase       (					self			:List[Any]     ,       *__UpperCamelCase			:torch.Tensor		):
												raise NotImplementedError()


					@staticmethod
					def 	lowerCamelCase       (					__UpperCamelCase			:torch.Tensor		):
												return (x + torch.sqrt(torch.square(__UpperCamelCase		) + 4.0		)) / 2.0







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"df": 1, "loc": 1, "scale": 1}
					UpperCamelCase      =							StudentT


					@classmethod
					def 	lowerCamelCase       (					cls			:List[str]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												A		=  2.0 + cls.squareplus(__UpperCamelCase		)
												return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"loc": 1, "scale": 1}
					UpperCamelCase      =							Normal


					@classmethod
					def 	lowerCamelCase       (					cls			:List[Any]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												return loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"total_count": 1, "logits": 1}
					UpperCamelCase      =							NegativeBinomial


					@classmethod
					def 	lowerCamelCase       (					cls			:str     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		)
												return total_count.squeeze(-1		), logits.squeeze(-1		)


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:List[str]		):
												A,	A		=  distr_args
												if self.dim == 1:
																			return self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None		):
												A,	A		=  distr_args

												if scale is not None:
																			# See scaling property of Gamma.
																			logits += scale.log()

												return self._base_distribution((total_count, logits)		)


