
'''simple docstring'''







import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
					from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


lowercase					:							Optional[int]   =      get_tests_dir("fixtures/test_sentencepiece.model")

lowercase					:							List[str]   =      {"target_lang": "fi", "source_lang": "en"}
lowercase					:							str   =      ">>zh<<"
lowercase					:							Optional[Any]   =      "Helsinki-NLP/"

if is_torch_available():
					lowercase					:							Optional[int]   =      "pt"
elif is_tf_available():
					lowercase					:							int   =      "tf"
else:
					lowercase					:							Optional[int]   =      "jax"


@require_sentencepiece
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= MarianTokenizer
							__lowercase   	= False
							__lowercase   	= True


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								super().setUp()
								_snake_case     		=	['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
								_snake_case     		=	dict(zip(lowerCAmelCase_					,     range(len(lowerCAmelCase_						)						)						)						)
								_snake_case     		=	Path(self.tmpdirname						)
								save_json(lowerCAmelCase_					,     save_dir / VOCAB_FILES_NAMES['vocab']						)
								save_json(lowerCAmelCase_					,     save_dir / VOCAB_FILES_NAMES['tokenizer_config_file']						)
								if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
									copyfile(lowerCAmelCase_					,     save_dir / VOCAB_FILES_NAMES['source_spm']						)
									copyfile(lowerCAmelCase_					,     save_dir / VOCAB_FILES_NAMES['target_spm']						)

								_snake_case     		=	MarianTokenizer.from_pretrained(self.tmpdirname						)
								tokenizer.save_pretrained(self.tmpdirname						)


							def 				lowerCamelCase       ( self					,     **lowerCAmelCase_						):





								"""simple docstring"""






								return MarianTokenizer.from_pretrained(self.tmpdirname					,     **lowerCAmelCase_						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								return (
								    "This is a test",
								    "This is a test",
								)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'</s>'
								_snake_case     		=	0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_						)					,     lowerCAmelCase_						)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_						)					,     lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	list(self.get_tokenizer().get_vocab().keys()						)

								self.assertEqual(vocab_keys[0]					,     '</s>'						)
								self.assertEqual(vocab_keys[1]					,     '<unk>'						)
								self.assertEqual(vocab_keys[-1]					,     '<pad>'						)
								self.assertEqual(len(lowerCAmelCase_						)					,     9						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.assertEqual(self.get_tokenizer().vocab_size					,     9						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	MarianTokenizer.from_pretrained(F'{ORG_NAME}opus-mt-en-de'						)
								_snake_case     		=	en_de_tokenizer(['I am a small frog']					,     return_tensors=lowerCAmelCase_						)
								self.assertIsInstance(lowerCAmelCase_					,     lowerCAmelCase_						)
								_snake_case     		=	[38, 1_21, 14, 6_97, 3_88_48, 0]
								self.assertListEqual(lowerCAmelCase_					,     batch.input_ids[0]						)

								_snake_case     		=	tempfile.mkdtemp()
								en_de_tokenizer.save_pretrained(lowerCAmelCase_						)
								_snake_case     		=	[x.name for x in Path(lowerCAmelCase_						).glob('*'						)]
								self.assertIn('source.spm'					,     lowerCAmelCase_						)
								MarianTokenizer.from_pretrained(lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_tokenizer()

								_snake_case     		=	tok(
								    ['I am a small frog' * 10_00, 'I am a small frog']					,     padding=lowerCAmelCase_					,     truncation=lowerCAmelCase_					,     return_tensors=lowerCAmelCase_						)
								self.assertIsInstance(lowerCAmelCase_					,     lowerCAmelCase_						)
								self.assertEqual(batch.input_ids.shape					,     (2, 5_12)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_tokenizer()
								_snake_case     		=	tok(['I am a tiny frog', 'I am a small frog']					,     padding=lowerCAmelCase_					,     return_tensors=lowerCAmelCase_						)
								self.assertIsInstance(lowerCAmelCase_					,     lowerCAmelCase_						)
								self.assertEqual(batch_smaller.input_ids.shape					,     (2, 10)						)


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	{'input_ids': [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=lowerCAmelCase_					,     model_name='Helsinki-NLP/opus-mt-en-de'					,     revision='1a8c2263da11e68e50938f97e10cd57820bd504c'					,     decode_kwargs={'use_source_tokenizer': True}					,     )




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'						)

								_snake_case     		=	'Tämä on testi'
								_snake_case     		=	'This is a test'

								_snake_case     		=	[76, 7, 20_47, 2]
								_snake_case     		=	[69, 12, 11, 9_40, 2]

								_snake_case     		=	tokenizer(lowerCAmelCase_						).input_ids
								self.assertListEqual(lowerCAmelCase_					,     lowerCAmelCase_						)

								_snake_case     		=	tokenizer(text_target=lowerCAmelCase_						).input_ids
								self.assertListEqual(lowerCAmelCase_					,     lowerCAmelCase_						)

								_snake_case     		=	tokenizer.decode(lowerCAmelCase_					,     skip_special_tokens=lowerCAmelCase_						)
								self.assertEqual(lowerCAmelCase_					,     lowerCAmelCase_						)



'''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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			int:
	_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        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_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        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[int]:
	# 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					:							str   =      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					:							Optional[int]   =      parser.parse_args()

					main(args)

