




import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
		from huggingface_hub import snapshot_download
		from pyctcdecode import BeamSearchDecoderCTC

		from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
		from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
		from transformers import WavaVecaForCTC




@require_pyctcdecode
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					"""| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
										SCREAMING_SNAKE_CASE           =					dict(zip(a       ,     range(len(a			)			)			)			)

										SCREAMING_SNAKE_CASE           =					{
										    """unk_token""": """<unk>""",
										    """bos_token""": """<s>""",
										    """eos_token""": """</s>""",
										}
										SCREAMING_SNAKE_CASE           =					{
										    """feature_size""": 1,
										    """padding_value""": 0.0,
										    """sampling_rate""": 16_000,
										    """return_attention_mask""": False,
										    """do_normalize""": True,
										}

										SCREAMING_SNAKE_CASE           =					tempfile.mkdtemp()
										SCREAMING_SNAKE_CASE           =					os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["""vocab_file"""]			)
										SCREAMING_SNAKE_CASE           =					os.path.join(self.tmpdirname       ,     a			)
										with open(self.vocab_file       ,     """w"""       ,     encoding="""utf-8"""			) as fp:
													fp.write(json.dumps(a			) + """\n"""			)

										with open(self.feature_extraction_file       ,     """w"""       ,     encoding="""utf-8"""			) as fp:
													fp.write(json.dumps(a			) + """\n"""			)

										# load decoder from hub
										SCREAMING_SNAKE_CASE           =					"""hf-internal-testing/ngram-beam-search-decoder"""





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     **a       :	Optional[Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					self.add_kwargs_tokens_map.copy()
										kwargs.update(a			)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,     **a			)





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     **a       :	int			)			->					Optional[int]:
										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     **a       :	Any			)			->					Any:
										return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										shutil.rmtree(self.tmpdirname			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										processor.save_pretrained(self.tmpdirname			)
										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname			)

										# tokenizer
										self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer.get_vocab()			)
										self.assertIsInstance(processor.tokenizer       ,     a			)

										# feature extractor
										self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor.to_json_string()			)
										self.assertIsInstance(processor.feature_extractor       ,     a			)

										# decoder
										self.assertEqual(processor.decoder._alphabet.labels       ,     decoder._alphabet.labels			)
										self.assertEqual(
										    processor.decoder.model_container[decoder._model_key]._unigram_set       ,     decoder.model_container[decoder._model_key]._unigram_set       ,     )
										self.assertIsInstance(processor.decoder       ,     a			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(
										    tokenizer=self.get_tokenizer()       ,     feature_extractor=self.get_feature_extractor()       ,     decoder=self.get_decoder()			)
										processor.save_pretrained(self.tmpdirname			)

										# make sure that error is thrown when decoder alphabet doesn't match
										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM.from_pretrained(
										    self.tmpdirname       ,     alpha=5.0       ,     beta=3.0       ,     score_boundary=-7.0       ,     unk_score_offset=3			)

										# decoder
										self.assertEqual(processor.language_model.alpha       ,     5.0			)
										self.assertEqual(processor.language_model.beta       ,     3.0			)
										self.assertEqual(processor.language_model.score_boundary       ,     -7.0			)
										self.assertEqual(processor.language_model.unk_score_offset       ,     3			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										# add token to trigger raise
										tokenizer.add_tokens(["""xx"""]			)
										with self.assertRaisesRegex(a       ,     """include"""			):
													WavaVecaProcessorWithLM(
													    tokenizer=a       ,     feature_extractor=self.get_feature_extractor()       ,     decoder=self.get_decoder()			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										SCREAMING_SNAKE_CASE           =					floats_list((3, 1_000)			)

										SCREAMING_SNAKE_CASE           =					feature_extractor(a       ,     return_tensors="""np"""			)
										SCREAMING_SNAKE_CASE           =					processor(a       ,     return_tensors="""np"""			)

										for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1E-2			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					str:
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										SCREAMING_SNAKE_CASE           =					"""This is a test string"""

										SCREAMING_SNAKE_CASE           =					processor(text=a			)

										SCREAMING_SNAKE_CASE           =					tokenizer(a			)

										for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]			)





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	Dict=(2, 10, 16)       ,     a       :	int=77			)			->					str:
										np.random.seed(a			)
										return np.random.rand(*a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										SCREAMING_SNAKE_CASE           =					self._get_dummy_logits(shape=(10, 16)       ,     seed=13			)

										SCREAMING_SNAKE_CASE           =					processor.decode(a			)

										SCREAMING_SNAKE_CASE           =					decoder.decode_beams(a			)[0]

										self.assertEqual(decoded_decoder[0]       ,     decoded_processor.text			)
										self.assertEqual("""</s> <s> </s>"""       ,     decoded_processor.text			)
										self.assertEqual(decoded_decoder[-2]       ,     decoded_processor.logit_score			)
										self.assertEqual(decoded_decoder[-1]       ,     decoded_processor.lm_score			)





							@parameterized.expand([[None], ["""fork"""], ["""spawn"""]]			)
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Union[str, Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										SCREAMING_SNAKE_CASE           =					self._get_dummy_logits()

										# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
										#       otherwise, the LM won't be available to the pool's sub-processes.
										# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
										if pool_context is None:
													SCREAMING_SNAKE_CASE           =					processor.batch_decode(a			)
										else:
													with get_context(a			).Pool() as pool:
																SCREAMING_SNAKE_CASE           =					processor.batch_decode(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					list(a			)

										with get_context("""fork"""			).Pool() as p:
													SCREAMING_SNAKE_CASE           =					decoder.decode_beams_batch(a       ,     a			)

										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					[], [], []
										for beams in decoded_beams:
													texts_decoder.append(beams[0][0]			)
													logit_scores_decoder.append(beams[0][-2]			)
													lm_scores_decoder.append(beams[0][-1]			)

										self.assertListEqual(a       ,     decoded_processor.text			)
										self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]       ,     decoded_processor.text			)
										self.assertListEqual(a       ,     decoded_processor.logit_score			)
										self.assertListEqual(a       ,     decoded_processor.lm_score			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										SCREAMING_SNAKE_CASE           =					self._get_dummy_logits()

										SCREAMING_SNAKE_CASE           =					15
										SCREAMING_SNAKE_CASE           =					-20.0
										SCREAMING_SNAKE_CASE           =					-4.0

										SCREAMING_SNAKE_CASE           =					processor.batch_decode(
										    a       ,     beam_width=a       ,     beam_prune_logp=a       ,     token_min_logp=a       ,     )
										SCREAMING_SNAKE_CASE           =					decoded_processor_out.text

										SCREAMING_SNAKE_CASE           =					list(a			)

										with get_context("""fork"""			).Pool() as pool:
													SCREAMING_SNAKE_CASE           =					decoder.decode_beams_batch(
													    a       ,     a       ,     beam_width=a       ,     beam_prune_logp=a       ,     token_min_logp=a       ,     )

										SCREAMING_SNAKE_CASE           =					[d[0][0] for d in decoded_decoder_out]
										SCREAMING_SNAKE_CASE           =					[d[0][2] for d in decoded_decoder_out]
										SCREAMING_SNAKE_CASE           =					[d[0][3] for d in decoded_decoder_out]

										self.assertListEqual(a       ,     a			)
										self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]       ,     a			)

										self.assertTrue(np.array_equal(a       ,     decoded_processor_out.logit_score			)			)
										self.assertTrue(np.allclose([-20.054, -18.447]       ,     a       ,     atol=1E-3			)			)

										self.assertTrue(np.array_equal(a       ,     decoded_processor_out.lm_score			)			)
										self.assertTrue(np.allclose([-15.554, -13.9474]       ,     a       ,     atol=1E-3			)			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										SCREAMING_SNAKE_CASE           =					self._get_dummy_logits()

										SCREAMING_SNAKE_CASE           =					2.0
										SCREAMING_SNAKE_CASE           =					5.0
										SCREAMING_SNAKE_CASE           =					-20.0
										SCREAMING_SNAKE_CASE           =					True

										SCREAMING_SNAKE_CASE           =					processor.batch_decode(
										    a       ,     alpha=a       ,     beta=a       ,     unk_score_offset=a       ,     lm_score_boundary=a       ,     )
										SCREAMING_SNAKE_CASE           =					decoded_processor_out.text

										SCREAMING_SNAKE_CASE           =					list(a			)
										decoder.reset_params(
										    alpha=a       ,     beta=a       ,     unk_score_offset=a       ,     lm_score_boundary=a       ,     )

										with get_context("""fork"""			).Pool() as pool:
													SCREAMING_SNAKE_CASE           =					decoder.decode_beams_batch(
													    a       ,     a       ,     )

										SCREAMING_SNAKE_CASE           =					[d[0][0] for d in decoded_decoder_out]

										self.assertListEqual(a       ,     a			)
										self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]       ,     a			)
										SCREAMING_SNAKE_CASE           =					processor.decoder.model_container[processor.decoder._model_key]
										self.assertEqual(lm_model.alpha       ,     2.0			)
										self.assertEqual(lm_model.beta       ,     5.0			)
										self.assertEqual(lm_model.unk_score_offset       ,     -20.0			)
										self.assertEqual(lm_model.score_boundary       ,     a			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""			)

										SCREAMING_SNAKE_CASE           =					processor.decoder.model_container[processor.decoder._model_key]
										SCREAMING_SNAKE_CASE           =					Path(language_model._kenlm_model.path.decode("""utf-8"""			)			).parent.parent.absolute()

										SCREAMING_SNAKE_CASE           =					os.listdir(a			)
										SCREAMING_SNAKE_CASE           =					["""alphabet.json""", """language_model"""]

										downloaded_decoder_files.sort()
										expected_decoder_files.sort()

										# test that only decoder relevant files from
										# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
										# are downloaded and none of the rest (e.g. README.md, ...)
										self.assertListEqual(a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					snapshot_download("""hf-internal-testing/processor_with_lm"""			)

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM.from_pretrained(a			)

										SCREAMING_SNAKE_CASE           =					processor.decoder.model_container[processor.decoder._model_key]
										SCREAMING_SNAKE_CASE           =					Path(language_model._kenlm_model.path.decode("""utf-8"""			)			).parent.parent.absolute()

										SCREAMING_SNAKE_CASE           =					os.listdir(a			)
										SCREAMING_SNAKE_CASE           =					os.listdir(a			)

										local_decoder_files.sort()
										expected_decoder_files.sort()

										# test that both decoder form hub and local files in cache are the same
										self.assertListEqual(a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""			)
										SCREAMING_SNAKE_CASE           =					AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""			)

										SCREAMING_SNAKE_CASE           =					floats_list((3, 1_000)			)

										SCREAMING_SNAKE_CASE           =					processor_wavaveca(a       ,     return_tensors="""np"""			)
										SCREAMING_SNAKE_CASE           =					processor_auto(a       ,     return_tensors="""np"""			)

										for key in input_wavaveca.keys():
													self.assertAlmostEqual(input_wavaveca[key].sum()       ,     input_auto[key].sum()       ,     delta=1E-2			)

										SCREAMING_SNAKE_CASE           =					self._get_dummy_logits()

										SCREAMING_SNAKE_CASE           =					processor_wavaveca.batch_decode(a			)
										SCREAMING_SNAKE_CASE           =					processor_auto.batch_decode(a			)

										self.assertListEqual(decoded_wavaveca.text       ,     decoded_auto.text			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_decoder()

										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM(tokenizer=a       ,     feature_extractor=a       ,     decoder=a			)

										self.assertListEqual(
										    processor.model_input_names       ,     feature_extractor.model_input_names       ,     msg="""`processor` and `feature_extractor` model input names do not match"""       ,     )





							@staticmethod
							def    _UpperCAmelCase     (					a       :	str       ,     a       :	Tuple			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					[d[key] for d in offsets]
										return retrieved_list





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""			)
										SCREAMING_SNAKE_CASE           =					self._get_dummy_logits()[0]

										SCREAMING_SNAKE_CASE           =					processor.decode(a       ,     output_word_offsets=a			)
										# check Wav2Vec2CTCTokenizerOutput keys for word
										self.assertEqual(len(outputs.keys()			)       ,     4			)
										self.assertTrue("""text""" in outputs			)
										self.assertTrue("""word_offsets""" in outputs			)
										self.assertTrue(isinstance(a       ,     a			)			)

										self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]       ,     """word"""			)			)       ,     outputs.text			)
										self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]       ,     """word"""			)       ,     ["""<s>""", """<s>""", """</s>"""]			)
										self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]       ,     """start_offset"""			)       ,     [0, 2, 4]			)
										self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]       ,     """end_offset"""			)       ,     [1, 3, 5]			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""			)
										SCREAMING_SNAKE_CASE           =					self._get_dummy_logits()

										SCREAMING_SNAKE_CASE           =					processor.batch_decode(a       ,     output_word_offsets=a			)

										# check Wav2Vec2CTCTokenizerOutput keys for word
										self.assertEqual(len(outputs.keys()			)       ,     4			)
										self.assertTrue("""text""" in outputs			)
										self.assertTrue("""word_offsets""" in outputs			)
										self.assertTrue(isinstance(a       ,     a			)			)

										self.assertListEqual(
										    [""" """.join(self.get_from_offsets(a       ,     """word"""			)			) for o in outputs["""word_offsets"""]]       ,     outputs.text			)
										self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]       ,     """word"""			)       ,     ["""<s>""", """<s>""", """</s>"""]			)
										self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]       ,     """start_offset"""			)       ,     [0, 2, 4]			)
										self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]       ,     """end_offset"""			)       ,     [1, 3, 5]			)







							@slow
							@require_torch
							@require_torchaudio
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[Any]:
										import torch

										SCREAMING_SNAKE_CASE           =					load_dataset("""common_voice"""       ,     """en"""       ,     split="""train"""       ,     streaming=a			)
										SCREAMING_SNAKE_CASE           =					ds.cast_column("""audio"""       ,     datasets.Audio(sampling_rate=16_000			)			)
										SCREAMING_SNAKE_CASE           =					iter(a			)
										SCREAMING_SNAKE_CASE           =					next(a			)

										SCREAMING_SNAKE_CASE           =					AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""			)
										SCREAMING_SNAKE_CASE           =					WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""			)

										# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
										SCREAMING_SNAKE_CASE           =					processor(sample["""audio"""]["""array"""]       ,     return_tensors="""pt"""			).input_values

										with torch.no_grad():
													SCREAMING_SNAKE_CASE           =					model(a			).logits.cpu().numpy()

										SCREAMING_SNAKE_CASE           =					processor.decode(logits[0]       ,     output_word_offsets=a			)

										SCREAMING_SNAKE_CASE           =					model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
										SCREAMING_SNAKE_CASE           =					[
										    {
										        """start_time""": d["""start_offset"""] * time_offset,
										        """end_time""": d["""end_offset"""] * time_offset,
										        """word""": d["""word"""],
										    }
										    for d in output["""word_offsets"""]
										]

										SCREAMING_SNAKE_CASE           =					"""WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

										# output words
										self.assertEqual(""" """.join(self.get_from_offsets(a       ,     """word"""			)			)       ,     a			)
										self.assertEqual(""" """.join(self.get_from_offsets(a       ,     """word"""			)			)       ,     output.text			)

										# output times
										SCREAMING_SNAKE_CASE           =					torch.tensor(self.get_from_offsets(a       ,     """start_time"""			)			)
										SCREAMING_SNAKE_CASE           =					torch.tensor(self.get_from_offsets(a       ,     """end_time"""			)			)

										# fmt: off
										SCREAMING_SNAKE_CASE           =					torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]			)
										SCREAMING_SNAKE_CASE           =					torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]			)
										# fmt: on

										self.assertTrue(torch.allclose(a       ,     a       ,     atol=0.01			)			)
										self.assertTrue(torch.allclose(a       ,     a       ,     atol=0.01			)			)












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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					args.log_outputs
			SCREAMING_SNAKE_CASE           =					"""_""".join(args.dataset.split("""/"""   ) + [args.config, args.split]   )

			# load metric
			SCREAMING_SNAKE_CASE           =					load_metric("""wer"""   )
			SCREAMING_SNAKE_CASE           =					load_metric("""cer"""   )

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

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

			SCREAMING_SNAKE_CASE           =					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!
			SCREAMING_SNAKE_CASE           =					["""\n\n""", """\n""", """   """, """  """]

			for t in token_sequences_to_ignore:
						SCREAMING_SNAKE_CASE           =					""" """.join(text.split(SCREAMING_SNAKE_CASE   )   )

			return text
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# load dataset
			SCREAMING_SNAKE_CASE           =					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
			SCREAMING_SNAKE_CASE           =					AutoFeatureExtractor.from_pretrained(args.model_id   )
			SCREAMING_SNAKE_CASE           =					feature_extractor.sampling_rate

			# resample audio
			SCREAMING_SNAKE_CASE           =					dataset.cast_column("""audio"""					,	Audio(sampling_rate=SCREAMING_SNAKE_CASE   )   )

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

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

						SCREAMING_SNAKE_CASE           =					prediction["""text"""]
						SCREAMING_SNAKE_CASE           =					normalize_text(batch["""sentence"""]   )
						return batch

			# run inference on all examples
			SCREAMING_SNAKE_CASE           =					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__":
		__A						:							List[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						:							Tuple							=  parser.parse_args()

		main(args)






