



from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__       =      TypeVar('''KEY''')
SCREAMING_SNAKE_CASE__       =      TypeVar('''VAL''')


@dataclass(frozen=UpperCAmelCase_							,     slots=UpperCAmelCase_      )
class      __lowerCAmelCase     (		Generic[KEY, VAL]      ):
					"""simple docstring"""






					A__				:		KEY
					A__				:		VAL







class      __lowerCAmelCase     (		_Item      ):
					"""simple docstring"""






					def __init__(     self					:       int					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __bool__(     self					:       List[str]					):






								"""simple docstring"""


								return False







SCREAMING_SNAKE_CASE__       =      _DeletedItem()


class      __lowerCAmelCase     (		MutableMapping[KEY, VAL]      ):
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       int = 8				,					_snake_case					:       float = 0.75					):






								"""simple docstring"""


								A__       =    initial_block_size
								A__       =    [None] * initial_block_size
								assert 0.0 < capacity_factor < 1.0
								A__       =    capacity_factor
								A__       =    0



					def     _a							(     self					:       Dict				,					_snake_case					:       KEY					):






								"""simple docstring"""


								return hash(_snake_case					) % len(self._buckets					)



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       int					):






								"""simple docstring"""


								return (ind + 1) % len(self._buckets					)



					def     _a							(     self					:       Any				,					_snake_case					:       int				,					_snake_case					:       KEY				,					_snake_case					:       VAL					):






								"""simple docstring"""


								A__       =    self._buckets[ind]
								if not stored:
											A__       =    _Item(_snake_case				,					_snake_case					)
											self._len += 1
											return True
								elif stored.key == key:
											A__       =    _Item(_snake_case				,					_snake_case					)
											return True
								else:
											return False



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    len(self._buckets					) * self._capacity_factor
								return len(self					) >= int(_snake_case					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								if len(self._buckets					) <= self._initial_block_size:
											return False
								A__       =    len(self._buckets					) * self._capacity_factor / 2
								return len(self					) < limit



					def     _a							(     self					:       List[Any]				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    self._buckets
								A__       =    [None] * new_size
								A__       =    0
								for item in old_buckets:
											if item:
														self._add_item(item.key				,					item.val					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								self._resize(len(self._buckets					) * 2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._resize(len(self._buckets					) // 2					)



					def     _a							(     self					:       str				,					_snake_case					:       KEY					):






								"""simple docstring"""


								A__       =    self._get_bucket_index(_snake_case					)
								for _ in range(len(self._buckets					)					):
											yield ind
											A__       =    self._get_next_ind(_snake_case					)



					def     _a							(     self					:       Any				,					_snake_case					:       KEY				,					_snake_case					:       VAL					):






								"""simple docstring"""


								for ind in self._iterate_buckets(_snake_case					):
											if self._try_set(_snake_case				,					_snake_case				,					_snake_case					):
														break



					def __setitem__(     self					:       Union[str, Any]				,					_snake_case					:       KEY				,					_snake_case					:       VAL					):






								"""simple docstring"""


								if self._is_full():
											self._size_up()

								self._add_item(_snake_case				,					_snake_case					)



					def __delitem__(     self					:       List[str]				,					_snake_case					:       KEY					):






								"""simple docstring"""


								for ind in self._iterate_buckets(_snake_case					):
											A__       =    self._buckets[ind]
											if item is None:
														raise KeyError(_snake_case					)
											if item is _deleted:
														continue
											if item.key == key:
														A__       =    _deleted
														self._len -= 1
														break
								if self._is_sparse():
											self._size_down()



					def __getitem__(     self					:       List[Any]				,					_snake_case					:       KEY					):






								"""simple docstring"""


								for ind in self._iterate_buckets(_snake_case					):
											A__       =    self._buckets[ind]
											if item is None:
														break
											if item is _deleted:
														continue
											if item.key == key:
														return item.val
								raise KeyError(_snake_case					)



					def __len__(     self					:       Any					):






								"""simple docstring"""


								return self._len



					def __iter__(     self					:       List[str]					):






								"""simple docstring"""


								yield from (item.key for item in self._buckets if item)



					def __repr__(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ' ,'.join(
								    F'''{item.key}: {item.val}''' for item in self._buckets if item					)
								return F'''HashMap({val_string})'''




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 A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			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 A						(       __UpperCamelCase					)						->      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 A						(       __UpperCamelCase					)						->      Union[str, Any]:
			# 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__":
							SCREAMING_SNAKE_CASE__       =      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.''',
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()

							main(args)




