"""simple docstring"""



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(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			args.log_outputs
							A__       :     Union[str, Any]								=			'''_'''.join(args.dataset.split("""/"""							) + [args.config, args.split]							)

							# load metric
							A__       :     str								=			load_metric("""wer"""							)
							A__       :     str								=			load_metric("""cer"""							)

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

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

							A__       :     List[Any]								=			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!
							A__       :     Tuple								=			['''\n\n''', '''\n''', '''   ''', '''  ''']

							for t in token_sequences_to_ignore:
														A__       :     Dict								=			''' '''.join(text.split(A_							)							)

							return text

def  _A(		lowerCAmelCase							):
							# load dataset
							A__       :     Union[str, 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
							A__       :     int								=			AutoFeatureExtractor.from_pretrained(args.model_id							)
							A__       :     Union[str, Any]								=			feature_extractor.sampling_rate

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

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

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

														A__       :     List[str]								=			prediction['''text''']
														A__       :     List[str]								=			normalize_text(batch["""sentence"""]							)
														return batch

							# run inference on all examples
							A__       :     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"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_albert import AlbertTokenizer
else:
							__UpperCamelCase    : Any				 =						None

__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)
__UpperCamelCase    : List[Any]				 =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : str				 =						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__UpperCamelCase    : Any				 =						{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__UpperCamelCase    : Union[str, Any]				 =						'''▁'''







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			AlbertTokenizer







					def __init__(			self     :							Union[str, Any]  ,lowercase_     :							List[str]=None  ,lowercase_     :							Union[str, Any]=None  ,lowercase_     :							str=True  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=False  ,lowercase_     :							Tuple="[CLS]"  ,lowercase_     :							Optional[int]="[SEP]"  ,lowercase_     :							Optional[Any]="<unk>"  ,lowercase_     :							List[Any]="[SEP]"  ,lowercase_     :							Optional[int]="<pad>"  ,lowercase_     :							List[Any]="[CLS]"  ,lowercase_     :							Optional[int]="[MASK]"  ,**lowercase_     :							Optional[int]  ,):
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						lowerCAmelCase__      :		Optional[Any]    							=      (
						    AddedToken(lowercase_  ,lstrip=lowercase_  ,rstrip=lowercase_  ,normalized=lowercase_						)
						    if isinstance(lowercase_  ,lowercase_						)
						    else mask_token
						)

						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,remove_space=lowercase_  ,keep_accents=lowercase_  ,bos_token=lowercase_  ,eos_token=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		List[Any]    							=      do_lower_case
						lowerCAmelCase__      :		str    							=      remove_space
						lowerCAmelCase__      :		Optional[int]    							=      keep_accents
						lowerCAmelCase__      :		Dict    							=      vocab_file
						lowerCAmelCase__      :		Optional[int]    							=      False if not self.vocab_file else True







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[Any]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [self.cls_token_id]
						if token_ids_a is None:
							return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[str]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		List[Any]    							=      [self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''						)

						if not os.path.isdir(lowercase_						):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
							return
						lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(
						    lowercase_  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase_						):
							copyfile(self.vocab_file  ,lowercase_						)

						return (out_vocab_file,)




