



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_      (	__a     :				Optional[Any] ,		__a     :				List[str]		):

				"""simple docstring"""
				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(SCREAMING_SNAKE_CASE_		)

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

				# 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(SCREAMING_SNAKE_CASE_ ,		"""w"""		) as p, open(SCREAMING_SNAKE_CASE_ ,		"""w"""		) as t:
												# mapping function to write output
												def write_to_file(__a     :				int ,		__a     :				Optional[Any]		):
																p.write(F'''{i}''' + """\n"""		)
																p.write(batch["""prediction"""] + """\n"""		)
																t.write(F'''{i}''' + """\n"""		)
																t.write(batch["""target"""] + """\n"""		)

												result.map(SCREAMING_SNAKE_CASE_ ,		with_indices=SCREAMING_SNAKE_CASE_		)

def 			A_      (	__a     :				Optional[int]		):

				"""simple docstring"""
				a__				      =		"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				a__				      =		re.sub(SCREAMING_SNAKE_CASE_ ,		"""""" ,		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(SCREAMING_SNAKE_CASE_		)		)

				return text

def 			A_      (	__a     :				List[str]		):

				"""simple docstring"""
				a__				      =		load_dataset(args.dataset ,		args.config ,		split=args.split ,		use_auth_token=SCREAMING_SNAKE_CASE_		)

				# 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=SCREAMING_SNAKE_CASE_		)		)

				# 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(__a     :				Optional[int]		):
								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(SCREAMING_SNAKE_CASE_ ,		remove_columns=dataset.column_names		)

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


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)







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							 =					None

UpperCAmelCase							 =					logging.get_logger(__name__)
UpperCAmelCase							 =					{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase							 =					{
    """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							 =					{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

UpperCAmelCase							 =					"""▁"""

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Tuple         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Union[str, Any]         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[str]         =					AlbertTokenizer


							def __init__(							self  ,  a_=None  ,  a_=None  ,  a_=True  ,  a_=True  ,  a_=False  ,  a_="[CLS]"  ,  a_="[SEP]"  ,  a_="<unk>"  ,  a_="[SEP]"  ,  a_="<pad>"  ,  a_="[CLS]"  ,  a_="[MASK]"  ,  **a_  ,  ):
											# 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.
											a__				      =		(
											    AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_  ,  normalized=a_		)
											    if isinstance(a_  ,  a_		)
											    else mask_token
											)

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

											a__				      =		do_lower_case
											a__				      =		remove_space
											a__				      =		keep_accents
											a__				      =		vocab_file
											a__				      =		False if not self.vocab_file else True


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[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 _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[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 _a      (							self  ,  a_  ,  a_ = 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(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    a_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

											return (out_vocab_file,)

