
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		List[Any]						     =     ["input_features"]



   def __init__(self		:					List[str]    ,						snake_case__		:					int=80    ,						snake_case__		:					List[Any]=1_60_00    ,						snake_case__		:					List[Any]=1_60    ,						snake_case__		:					Tuple=30    ,						snake_case__		:					Dict=4_00    ,						snake_case__		:					str=0.0    ,						snake_case__		:					Optional[int]=False    ,						**snake_case__		:					Dict    ,						)		->	List[str]:


          '''simple docstring'''


          super().__init__(
              feature_size=snake_case__    ,						sampling_rate=snake_case__    ,						padding_value=snake_case__    ,						return_attention_mask=snake_case__    ,						**snake_case__    ,						)
          snake_case       :   Dict							=      n_fft
          snake_case       :   List[Any]							=      hop_length
          snake_case       :   List[Any]							=      chunk_length
          snake_case       :   List[str]							=      chunk_length * sampling_rate
          snake_case       :   int							=      self.n_samples // hop_length
          snake_case       :   List[Any]							=      sampling_rate
          snake_case       :   List[str]							=      mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2    ,						num_mel_filters=snake_case__    ,						min_frequency=0.0    ,						max_frequency=8000.0    ,						sampling_rate=snake_case__    ,						norm="slaney"    ,						mel_scale="slaney"    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					np.array )		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Any							=      spectrogram(
              snake_case__    ,						window_function(self.n_fft    ,						"hann" )    ,						frame_length=self.n_fft    ,						hop_length=self.hop_length    ,						power=2.0    ,						mel_filters=self.mel_filters    ,						log_mel="log10"    ,						)
          snake_case       :   str							=      log_spec[:, :-1]
          snake_case       :   Any							=      np.maximum(snake_case__    ,						log_spec.max() - 8.0 )
          snake_case       :   List[str]							=      (log_spec + 4.0) / 4.0
          return log_spec



   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 				_SCREAMING_SNAKE_CASE   (snake_case__		:					List[np.ndarray]    ,						snake_case__		:					List[np.ndarray]    ,						snake_case__		:					float = 0.0 )		->	List[np.ndarray]:


          '''simple docstring'''


          if attention_mask is not None:
                 snake_case       :   Any							=      np.array(snake_case__    ,						np.intaa )
                 snake_case       :   Dict							=      []

                 for vector, length in zip(snake_case__    ,						attention_mask.sum(-1 ) ):
                        snake_case       :   Dict							=      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
                        if length < normed_slice.shape[0]:
                               snake_case       :   Union[str, Any]							=      padding_value

                        normed_input_values.append(snake_case__ )
          else:
                 snake_case       :   List[str]							=      [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]

          return normed_input_values



   def __call__(self		:					str    ,						snake_case__		:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[str] = "max_length"    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[bool] = None    ,						**snake_case__		:					List[Any]    ,						)		->	BatchFeature:


          '''simple docstring'''


          if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                        raise ValueError(
                            f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                            f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                            f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
          else:
                 logger.warning(
                     "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                     "Failing to do so can result in silent errors that might be hard to debug." )

          snake_case       :   Optional[int]							=      isinstance(snake_case__    ,						np.ndarray ) and len(raw_speech.shape ) > 1
          if is_batched_numpy and len(raw_speech.shape ) > 2:
                 raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
          snake_case       :   List[Any]							=      is_batched_numpy or (
              isinstance(snake_case__    ,						(list, tuple) ) and (isinstance(raw_speech[0]    ,						(np.ndarray, tuple, list) ))
          )

          if is_batched:
                 snake_case       :   str							=      [np.asarray([speech]    ,						dtype=np.floataa ).T for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case__    ,						np.ndarray ):
                 snake_case       :   List[str]							=      np.asarray(snake_case__    ,						dtype=np.floataa )
          elif isinstance(snake_case__    ,						np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
                 snake_case       :   List[str]							=      raw_speech.astype(np.floataa )

          # always return batch
          if not is_batched:
                 snake_case       :   Dict							=      [np.asarray([raw_speech] ).T]

          snake_case       :   Optional[int]							=      BatchFeature({"input_features": raw_speech} )

          # convert into correct format for padding

          snake_case       :   Optional[Any]							=      self.pad(
              snake_case__    ,						padding=snake_case__    ,						max_length=max_length if max_length else self.n_samples    ,						truncation=snake_case__    ,						pad_to_multiple_of=snake_case__    ,						return_attention_mask=return_attention_mask or do_normalize    ,						)

          # zero-mean and unit-variance normalization
          if do_normalize:
                 snake_case       :   Optional[Any]							=      self.zero_mean_unit_var_norm(
                     padded_inputs["input_features"]    ,						attention_mask=padded_inputs["attention_mask"]    ,						padding_value=self.padding_value    ,						)
                 snake_case       :   Union[str, Any]							=      np.stack(padded_inputs["input_features"]    ,						axis=0 )

          # make sure list is in array format
          snake_case       :   List[Any]							=      padded_inputs.get("input_features" ).transpose(2    ,						0    ,						1 )

          snake_case       :   str							=      [self._np_extract_fbank_features(snake_case__ ) for waveform in input_features[0]]

          if isinstance(input_features[0]    ,						snake_case__ ):
                 snake_case       :   Dict							=      [np.asarray(snake_case__    ,						dtype=np.floataa ) for feature in input_features]
          else:
                 snake_case       :   Tuple							=      input_features

          if return_attention_mask:
                 # rescale from sample (48000) to feature (3000)
                 snake_case       :   str							=      padded_inputs["attention_mask"][:, :: self.hop_length]

          if return_tensors is not None:
                 snake_case       :   int							=      padded_inputs.convert_to_tensors(snake_case__ )

          return padded_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Dict[str, Any]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      copy.deepcopy(self.__dict__ )
          snake_case       :   Dict							=      self.__class__.__name__
          if "mel_filters" in output:
                 del output["mel_filters"]
          return output







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					(    __lowerCamelCase     : Dataset				,					__lowerCamelCase     : Dict[str, str]    ):
       snake_case       :   int							=      args.log_outputs
       snake_case       :   Dict							=      "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

       # load metric
       snake_case       :   List[str]							=      load_metric("wer"    )
       snake_case       :   Tuple							=      load_metric("cer"    )

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

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

       snake_case       :   List[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!
       snake_case       :   Optional[Any]							=      ["\n\n", "\n", "   ", "  "]

       for t in token_sequences_to_ignore:
              snake_case       :   Dict							=      " ".join(text.split(__lowerCamelCase    )    )

       return text


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       # load dataset
       snake_case       :   str							=      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
       snake_case       :   List[Any]							=      AutoFeatureExtractor.from_pretrained(args.model_id    )
       snake_case       :   Union[str, Any]							=      feature_extractor.sampling_rate

       # resample audio
       snake_case       :   Union[str, Any]							=      dataset.cast_column("audio"				,					Audio(sampling_rate=__lowerCamelCase    )    )

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

       # map function to decode audio
       def map_to_pred(__lowerCamelCase     : int    ):
              snake_case       :   Dict							=      asr(
                  batch["audio"]["array"]				,					chunk_length_s=args.chunk_length_s				,					stride_length_s=args.stride_length_s    )

              snake_case       :   str							=      prediction["text"]
              snake_case       :   Tuple							=      normalize_text(batch["sentence"]    )
              return batch

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

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


if __name__ == "__main__":
   __lowerCamelCase							=       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							=       parser.parse_args()

   main(args)





