

'''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(lowercase__			, lowercase__	):





	'''simple docstring'''
	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(lowercase__			, lowercase__	):
				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(lowercase__	):





	'''simple docstring'''
	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 a(lowercase__	):





	'''simple docstring'''
	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(lowercase__	):
		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__":
		A											=     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											=     parser.parse_args()

		main(args)



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


A											=     '3'

print('Python version:', sys.version)

print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())

try:
		import torch

		print('Torch version:', torch.__version__)
		print('Cuda available:', torch.cuda.is_available())
		print('Cuda version:', torch.version.cuda)
		print('CuDNN version:', torch.backends.cudnn.version())
		print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
		print('Torch version:', None)

try:
		import transformers

		print('transformers version:', transformers.__version__)
except ImportError:
		print('transformers version:', None)
