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  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


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

         A						:     str         ={
             '''unk_token''': '''<unk>''',
             '''bos_token''': '''<s>''',
             '''eos_token''': '''</s>''',
         }
         A						:     Dict         ={
             '''feature_size''': 1,
             '''padding_value''': 0.0,
             '''sampling_rate''': 1_60_00,
             '''return_attention_mask''': False,
             '''do_normalize''': True,
         }

         A						:     Optional[int]         =tempfile.mkdtemp()
         A						:     Optional[Any]         =os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES['vocab_file']     )
         A						:     str         =os.path.join(self.tmpdirname      ,	UpperCamelCase__     )
         with open(self.vocab_file      ,	'w'      ,	encoding='utf-8'     ) as fp:
                fp.write(json.dumps(UpperCamelCase__     ) + '\n'     )

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

         # load decoder from hub
         A						:     Any         ='''hf-internal-testing/ngram-beam-search-decoder'''







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	**SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Optional[Any]:
         A						:     int         =self.add_kwargs_tokens_map.copy()
         kwargs.update(UpperCamelCase__     )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,	**UpperCamelCase__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Optional[int]:
         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,	**UpperCamelCase__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				List[str]:
         return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,	**UpperCamelCase__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Union[str, Any]:
         shutil.rmtree(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				List[Any]:
         A						:     Any         =self.get_tokenizer()
         A						:     Any         =self.get_feature_extractor()
         A						:     List[str]         =self.get_decoder()

         A						:     Dict         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

         processor.save_pretrained(self.tmpdirname     )
         A						:     Union[str, Any]         =WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname     )

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

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

         # 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      ,	UpperCamelCase__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				str:
         A						:     str         =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
         A						:     Tuple         =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 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				str:
         A						:     List[str]         =self.get_tokenizer()
         # add token to trigger raise
         tokenizer.add_tokens(['xx']     )
         with self.assertRaisesRegex(UpperCamelCase__      ,	'include'     ):
                WavaVecaProcessorWithLM(
                    tokenizer=UpperCamelCase__      ,	feature_extractor=self.get_feature_extractor()      ,	decoder=self.get_decoder()     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[str]:
         A						:     str         =self.get_feature_extractor()
         A						:     Dict         =self.get_tokenizer()
         A						:     str         =self.get_decoder()

         A						:     Optional[Any]         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

         A						:     Tuple         =floats_list((3, 10_00)     )

         A						:     List[Any]         =feature_extractor(UpperCamelCase__      ,	return_tensors='np'     )
         A						:     int         =processor(UpperCamelCase__      ,	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 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         A						:     List[Any]         =self.get_feature_extractor()
         A						:     Optional[Any]         =self.get_tokenizer()
         A						:     List[Any]         =self.get_decoder()

         A						:     Dict         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

         A						:     Union[str, Any]         ='''This is a test string'''

         A						:     Optional[Any]         =processor(text=UpperCamelCase__     )

         A						:     str         =tokenizer(UpperCamelCase__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]=(2, 10, 16)      ,	SCREAMING_SNAKE_CASE__    :					int=77     )    ->				Tuple:
         np.random.seed(UpperCamelCase__     )
         return np.random.rand(*UpperCamelCase__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				List[str]:
         A						:     Optional[int]         =self.get_feature_extractor()
         A						:     Dict         =self.get_tokenizer()
         A						:     Optional[Any]         =self.get_decoder()

         A						:     int         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

         A						:     Any         =self._get_dummy_logits(shape=(10, 16)      ,	seed=13     )

         A						:     str         =processor.decode(UpperCamelCase__     )

         A						:     List[Any]         =decoder.decode_beams(UpperCamelCase__     )[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 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				str:
         A						:     int         =self.get_feature_extractor()
         A						:     int         =self.get_tokenizer()
         A						:     int         =self.get_decoder()

         A						:     str         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

         A						:     Optional[Any]         =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:
                A						:     Tuple         =processor.batch_decode(UpperCamelCase__     )
         else:
                with get_context(UpperCamelCase__     ).Pool() as pool:
                       A						:     List[Any]         =processor.batch_decode(UpperCamelCase__      ,	UpperCamelCase__     )

         A						:     Tuple         =list(UpperCamelCase__     )

         with get_context('fork'     ).Pool() as p:
                A						:     str         =decoder.decode_beams_batch(UpperCamelCase__      ,	UpperCamelCase__     )

         A						:     int         =[], [], []
         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(UpperCamelCase__      ,	decoded_processor.text     )
         self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']      ,	decoded_processor.text     )
         self.assertListEqual(UpperCamelCase__      ,	decoded_processor.logit_score     )
         self.assertListEqual(UpperCamelCase__      ,	decoded_processor.lm_score     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Optional[int]:
         A						:     List[Any]         =self.get_feature_extractor()
         A						:     List[str]         =self.get_tokenizer()
         A						:     Dict         =self.get_decoder()

         A						:     Optional[Any]         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

         A						:     Optional[int]         =self._get_dummy_logits()

         A						:     Dict         =15
         A						:     Union[str, Any]         =-20.0
         A						:     List[Any]         =-4.0

         A						:     List[Any]         =processor.batch_decode(
             UpperCamelCase__      ,	beam_width=UpperCamelCase__      ,	beam_prune_logp=UpperCamelCase__      ,	token_min_logp=UpperCamelCase__      ,	)
         A						:     Optional[Any]         =decoded_processor_out.text

         A						:     str         =list(UpperCamelCase__     )

         with get_context('fork'     ).Pool() as pool:
                A						:     Dict         =decoder.decode_beams_batch(
                    UpperCamelCase__      ,	UpperCamelCase__      ,	beam_width=UpperCamelCase__      ,	beam_prune_logp=UpperCamelCase__      ,	token_min_logp=UpperCamelCase__      ,	)

         A						:     List[str]         =[d[0][0] for d in decoded_decoder_out]
         A						:     Any         =[d[0][2] for d in decoded_decoder_out]
         A						:     List[Any]         =[d[0][3] for d in decoded_decoder_out]

         self.assertListEqual(UpperCamelCase__      ,	UpperCamelCase__     )
         self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']      ,	UpperCamelCase__     )

         self.assertTrue(np.array_equal(UpperCamelCase__      ,	decoded_processor_out.logit_score     )     )
         self.assertTrue(np.allclose([-20.0_54, -18.4_47]      ,	UpperCamelCase__      ,	atol=1e-3     )     )

         self.assertTrue(np.array_equal(UpperCamelCase__      ,	decoded_processor_out.lm_score     )     )
         self.assertTrue(np.allclose([-15.5_54, -13.94_74]      ,	UpperCamelCase__      ,	atol=1e-3     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Optional[Any]:
         A						:     Optional[int]         =self.get_feature_extractor()
         A						:     Tuple         =self.get_tokenizer()
         A						:     Optional[int]         =self.get_decoder()

         A						:     List[Any]         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

         A						:     int         =self._get_dummy_logits()

         A						:     Optional[Any]         =2.0
         A						:     Union[str, Any]         =5.0
         A						:     Tuple         =-20.0
         A						:     Tuple         =True

         A						:     int         =processor.batch_decode(
             UpperCamelCase__      ,	alpha=UpperCamelCase__      ,	beta=UpperCamelCase__      ,	unk_score_offset=UpperCamelCase__      ,	lm_score_boundary=UpperCamelCase__      ,	)
         A						:     Any         =decoded_processor_out.text

         A						:     int         =list(UpperCamelCase__     )
         decoder.reset_params(
             alpha=UpperCamelCase__      ,	beta=UpperCamelCase__      ,	unk_score_offset=UpperCamelCase__      ,	lm_score_boundary=UpperCamelCase__      ,	)

         with get_context('fork'     ).Pool() as pool:
                A						:     Optional[int]         =decoder.decode_beams_batch(
                    UpperCamelCase__      ,	UpperCamelCase__      ,	)

         A						:     List[str]         =[d[0][0] for d in decoded_decoder_out]

         self.assertListEqual(UpperCamelCase__      ,	UpperCamelCase__     )
         self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']      ,	UpperCamelCase__     )
         A						:     Optional[int]         =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      ,	UpperCamelCase__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Union[str, Any]:
         A						:     str         =WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )

         A						:     Any         =processor.decoder.model_container[processor.decoder._model_key]
         A						:     Optional[Any]         =Path(language_model._kenlm_model.path.decode('utf-8'     )     ).parent.parent.absolute()

         A						:     Optional[Any]         =os.listdir(UpperCamelCase__     )
         A						:     Tuple         =['''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(UpperCamelCase__      ,	UpperCamelCase__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Optional[Any]         =snapshot_download('hf-internal-testing/processor_with_lm'     )

         A						:     Dict         =WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase__     )

         A						:     List[str]         =processor.decoder.model_container[processor.decoder._model_key]
         A						:     int         =Path(language_model._kenlm_model.path.decode('utf-8'     )     ).parent.parent.absolute()

         A						:     Tuple         =os.listdir(UpperCamelCase__     )
         A						:     int         =os.listdir(UpperCamelCase__     )

         local_decoder_files.sort()
         expected_decoder_files.sort()

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Dict:
         A						:     Dict         =WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )
         A						:     Any         =AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'     )

         A						:     Dict         =floats_list((3, 10_00)     )

         A						:     List[Any]         =processor_wavaveca(UpperCamelCase__      ,	return_tensors='np'     )
         A						:     Tuple         =processor_auto(UpperCamelCase__      ,	return_tensors='np'     )

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

         A						:     int         =self._get_dummy_logits()

         A						:     str         =processor_wavaveca.batch_decode(UpperCamelCase__     )
         A						:     Union[str, Any]         =processor_auto.batch_decode(UpperCamelCase__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[Any]:
         A						:     Any         =self.get_feature_extractor()
         A						:     List[str]         =self.get_tokenizer()
         A						:     int         =self.get_decoder()

         A						:     List[Any]         =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__      ,	feature_extractor=UpperCamelCase__      ,	decoder=UpperCamelCase__     )

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







  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Union[str, Any]:
         A						:     Tuple         =[d[key] for d in offsets]
         return retrieved_list







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				str:
         A						:     str         =WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )
         A						:     str         =self._get_dummy_logits()[0]

         A						:     str         =processor.decode(UpperCamelCase__      ,	output_word_offsets=UpperCamelCase__     )
         # 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(UpperCamelCase__      ,	UpperCamelCase__     )     )

         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 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Union[str, Any]:
         A						:     Union[str, Any]         =WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'     )
         A						:     Optional[int]         =self._get_dummy_logits()

         A						:     Any         =processor.batch_decode(UpperCamelCase__      ,	output_word_offsets=UpperCamelCase__     )

         # 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(UpperCamelCase__      ,	UpperCamelCase__     )     )

         self.assertListEqual(
             [' '.join(self.get_from_offsets(UpperCamelCase__      ,	'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 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				List[Any]:
         import torch

         A						:     Any         =load_dataset('common_voice'      ,	'en'      ,	split='train'      ,	streaming=UpperCamelCase__     )
         A						:     str         =ds.cast_column('audio'      ,	datasets.Audio(sampling_rate=1_60_00     )     )
         A						:     int         =iter(UpperCamelCase__     )
         A						:     Dict         =next(UpperCamelCase__     )

         A						:     str         =AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'     )
         A						:     Optional[Any]         =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
         A						:     Union[str, Any]         =processor(sample['audio']['array']      ,	return_tensors='pt'     ).input_values

         with torch.no_grad():
                A						:     Union[str, Any]         =model(UpperCamelCase__     ).logits.cpu().numpy()

         A						:     Tuple         =processor.decode(logits[0]      ,	output_word_offsets=UpperCamelCase__     )

         A						:     str         =model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
         A						:     List[Any]         =[
             {
                 '''start_time''': d['''start_offset'''] * time_offset,
                 '''end_time''': d['''end_offset'''] * time_offset,
                 '''word''': d['''word'''],
             }
             for d in output['''word_offsets''']
         ]

         A						:     Union[str, Any]         ='''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(UpperCamelCase__      ,	'word'     )     )      ,	UpperCamelCase__     )
         self.assertEqual(' '.join(self.get_from_offsets(UpperCamelCase__      ,	'word'     )     )      ,	output.text     )

         # output times
         A						:     List[str]         =torch.tensor(self.get_from_offsets(UpperCamelCase__      ,	'start_time'     )     )
         A						:     Tuple         =torch.tensor(self.get_from_offsets(UpperCamelCase__      ,	'end_time'     )     )

         # fmt: off
         A						:     str         =torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]     )
         A						:     List[str]         =torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]     )
         # fmt: on

         self.assertTrue(torch.allclose(UpperCamelCase__      ,	UpperCamelCase__      ,	atol=0.0_1     )     )
         self.assertTrue(torch.allclose(UpperCamelCase__      ,	UpperCamelCase__      ,	atol=0.0_1     )     )







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






