




'''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__   (				a    ,	a							):
						__snake_case						 =   args.log_outputs
						__snake_case						 =   '_'.join(args.dataset.split('/'							) + [args.config, args.split]							)

						# load metric
						__snake_case						 =   load_metric('wer'							)
						__snake_case						 =   load_metric('cer'							)

						# compute metrics
						__snake_case						 =   wer.compute(references=result['target']    ,	predictions=result['prediction']							)
						__snake_case						 =   cer.compute(references=result['target']    ,	predictions=result['prediction']							)

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

						__snake_case						 =   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!
						__snake_case						 =   ['\n\n', '\n', '   ', '  ']

						for t in token_sequences_to_ignore:
												__snake_case						 =   ' '.join(text.split(a							)							)

						return text
def 						lowerCamelCase__   (				a							):
						# load dataset
						__snake_case						 =   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
						__snake_case						 =   AutoFeatureExtractor.from_pretrained(args.model_id							)
						__snake_case						 =   feature_extractor.sampling_rate

						# resample audio
						__snake_case						 =   dataset.cast_column('audio'    ,	Audio(sampling_rate=a							)							)

						# load eval pipeline
						if args.device is None:
												__snake_case						 =   0 if torch.cuda.is_available() else -1
						__snake_case						 =   pipeline('automatic-speech-recognition'    ,	model=args.model_id    ,	device=args.device							)

						# map function to decode audio
						def map_to_pred(a							):
												__snake_case						 =   asr(
												    batch['audio']['array']    ,	chunk_length_s=args.chunk_length_s    ,	stride_length_s=args.stride_length_s							)

												__snake_case						 =   prediction['text']
												__snake_case						 =   normalize_text(batch['sentence']							)
												return batch

						# run inference on all examples
						__snake_case						 =   dataset.map(a    ,	remove_columns=dataset.column_names							)

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


if __name__ == "__main__":
	_lowercase  =			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.""",
	)
	_lowercase  =			parser.parse_args()

	main(args)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase  =			{
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GroupViTModel""",
	    """GroupViTPreTrainedModel""",
	    """GroupViTTextModel""",
	    """GroupViTVisionModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGroupViTModel""",
	    """TFGroupViTPreTrainedModel""",
	    """TFGroupViTTextModel""",
	    """TFGroupViTVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_groupvit import (
	    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    GroupViTConfig,
	    GroupViTOnnxConfig,
	    GroupViTTextConfig,
	    GroupViTVisionConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_groupvit import (
		    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GroupViTModel,
		    GroupViTPreTrainedModel,
		    GroupViTTextModel,
		    GroupViTVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_groupvit import (
		    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGroupViTModel,
		    TFGroupViTPreTrainedModel,
		    TFGroupViTTextModel,
		    TFGroupViTVisionModel,
		)

else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

