



'''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       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		int							     =		args.log_outputs
							UpperCAmelCase     :		List[Any]							     =		"""_""".join(args.dataset.split("""/"""		) + [args.config, args.split]		)

							# load metric
							UpperCAmelCase     :		Dict							     =		load_metric("""wer"""		)
							UpperCAmelCase     :		Optional[Any]							     =		load_metric("""cer"""		)

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

							# print & log results
							UpperCAmelCase     :		int							     =		F'''WER: {wer_result}\nCER: {cer_result}'''
							print(_lowercase		)

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

							# log all results in text file. Possibly interesting for analysis
							if log_outputs is not None:
														UpperCAmelCase     :		Dict							     =		F'''log_{dataset_id}_predictions.txt'''
														UpperCAmelCase     :		Tuple							     =		F'''log_{dataset_id}_targets.txt'''

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

																					result.map(_lowercase	,				with_indices=_lowercase		)



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

							UpperCAmelCase     :		List[str]							     =		re.sub(_lowercase	,				""""""	,				text.lower()		)

							# In addition, we can normalize the target text, e.g. removing new lines characters etc...
							# note that order is important here!
							UpperCAmelCase     :		int							     =		["""\n\n""", """\n""", """   """, """  """]

							for t in token_sequences_to_ignore:
														UpperCAmelCase     :		List[Any]							     =		""" """.join(text.split(_lowercase		)		)

							return text



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							# load dataset
							UpperCAmelCase     :		int							     =		load_dataset(args.dataset	,				args.config	,				split=args.split	,				use_auth_token=_lowercase		)

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

							# load processor
							UpperCAmelCase     :		List[Any]							     =		AutoFeatureExtractor.from_pretrained(args.model_id		)
							UpperCAmelCase     :		List[Any]							     =		feature_extractor.sampling_rate

							# resample audio
							UpperCAmelCase     :		Tuple							     =		dataset.cast_column("""audio"""	,				Audio(sampling_rate=_lowercase		)		)

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

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

														UpperCAmelCase     :		Optional[Any]							     =		prediction["""text"""]
														UpperCAmelCase     :		Union[str, Any]							     =		normalize_text(batch["""sentence"""]		)
														return batch

							# run inference on all examples
							UpperCAmelCase     :		List[str]							     =		dataset.map(_lowercase	,				remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
							a   :			Optional[Any]					  =    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.""",
							)
							a   :			Any					  =    parser.parse_args()

							main(args)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a   :			Optional[int]					  =    {
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a   :			Tuple					  =    [
							    """GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GitForCausalLM""",
							    """GitModel""",
							    """GitPreTrainedModel""",
							    """GitVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
							from .processing_git import GitProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_git import (
														    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GitForCausalLM,
														    GitModel,
														    GitPreTrainedModel,
														    GitVisionModel,
														)

else:
							import sys

							a   :			Tuple					  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

