





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__			(__lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							args.log_outputs
     _SCREAMING_SNAKE_CASE       :	int						    =							"_".join(args.dataset.split("/"	) + [args.config, args.split]	)

     # load metric
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							load_metric("wer"	)
     _SCREAMING_SNAKE_CASE       :	Any						    =							load_metric("cer"	)

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

     # print & log results
     _SCREAMING_SNAKE_CASE       :	int						    =							f"""WER: {wer_result}\nCER: {cer_result}"""
     print(__lowerCamelCase	)

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

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

          with open(__lowerCamelCase,       "w"	) as p, open(__lowerCamelCase,       "w"	) as t:
               # mapping function to write output
               def write_to_file(__lowerCamelCase,       __lowerCamelCase	):
                    p.write(f"""{i}""" + "\n"	)
                    p.write(batch["prediction"] + "\n"	)
                    t.write(f"""{i}""" + "\n"	)
                    t.write(batch["target"] + "\n"	)

               result.map(__lowerCamelCase,       with_indices=__lowerCamelCase	)






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

     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							re.sub(__lowerCamelCase,       "",       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       :	Dict						    =							["\n\n", "\n", "   ", "  "]

     for t in token_sequences_to_ignore:
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							" ".join(text.split(__lowerCamelCase	)	)

     return text






def     lowerCamelCase__			(__lowerCamelCase	):
     # load dataset
     _SCREAMING_SNAKE_CASE       :	int						    =							load_dataset(args.dataset,       args.config,       split=args.split,       use_auth_token=__lowerCamelCase	)

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

     # load processor
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							AutoFeatureExtractor.from_pretrained(args.model_id	)
     _SCREAMING_SNAKE_CASE       :	Any						    =							feature_extractor.sampling_rate

     # resample audio
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							dataset.cast_column("audio",       Audio(sampling_rate=__lowerCamelCase	)	)

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

     # map function to decode audio
     def map_to_pred(__lowerCamelCase	):
          _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       :	List[Any]						    =							prediction["text"]
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							normalize_text(batch["sentence"]	)
          return batch

     # run inference on all examples
     _SCREAMING_SNAKE_CASE       :	Any						    =							dataset.map(__lowerCamelCase,       remove_columns=dataset.column_names	)

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


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)






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    42





class 			lowerCAmelCase__( __lowercase  ,     __lowercase    ):






       '''simple docstring'''


       @register_to_config
       def __init__(      self    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = ("DownEncoderBlock2D",)    ,						__lowerCamelCase = ("UpDecoderBlock2D",)    ,						__lowerCamelCase = (6_4,)    ,						__lowerCamelCase = 1    ,						__lowerCamelCase = "silu"    ,						__lowerCamelCase = 3    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = 2_5_6    ,						__lowerCamelCase = 3_2    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0.1_8215    ,						__lowerCamelCase = "group"    ,						)			->     List[str]:
            super().__init__()

            # pass init params to Encoder
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							Encoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						down_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						double_z=__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							vq_embed_dim if vq_embed_dim is not None else latent_channels

            _SCREAMING_SNAKE_CASE       :	str						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							VectorQuantizer(__lowerCamelCase    ,						__lowerCamelCase    ,						beta=0.25    ,						remap=__lowerCamelCase    ,						sane_index_shape=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							nn.Convad(__lowerCamelCase    ,						__lowerCamelCase    ,						1							)

            # pass init params to Decoder
            _SCREAMING_SNAKE_CASE       :	Dict						    =							Decoder(
                in_channels=__lowerCamelCase    ,						out_channels=__lowerCamelCase    ,						up_block_types=__lowerCamelCase    ,						block_out_channels=__lowerCamelCase    ,						layers_per_block=__lowerCamelCase    ,						act_fn=__lowerCamelCase    ,						norm_num_groups=__lowerCamelCase    ,						norm_type=__lowerCamelCase    ,						)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     VQEncoderOutput:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.encoder(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.quant_conv(__lowerCamelCase							)

            if not return_dict:
                 return (h,)

            return VQEncoderOutput(latents=__lowerCamelCase							)




       @apply_forward_hook
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.quantize(__lowerCamelCase							)
            else:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							h
            _SCREAMING_SNAKE_CASE       :	str						    =							self.post_quant_conv(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.decoder(__lowerCamelCase    ,						quant if self.config.norm_type == "spatial" else None							)

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = True							)			->     Union[DecoderOutput, torch.FloatTensor]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							sample
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.encode(__lowerCamelCase							).latents
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.decode(__lowerCamelCase							).sample

            if not return_dict:
                 return (dec,)

            return DecoderOutput(sample=__lowerCamelCase							)