





'''simple docstring'''




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							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
          __A						:			int								=	dict(zip(_UpperCAmelCase						,     range(len(_UpperCAmelCase))))

          __A						:			int								=	{
              'unk_token': '<unk>',
              'bos_token': '<s>',
              'eos_token': '</s>',
          }
          __A						:			Dict								=	{
              'feature_size': 1,
              'padding_value': 0.0,
              'sampling_rate': 1_6000,
              'return_attention_mask': False,
              'do_normalize': True,
          }

          __A						:			List[str]								=	tempfile.mkdtemp()
          __A						:			List[str]								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['vocab_file'])
          __A						:			int								=	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						:			str								=	'hf-internal-testing/ngram-beam-search-decoder'


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	self.add_kwargs_tokens_map.copy()
          kwargs.update(_UpperCAmelCase)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	self.get_tokenizer()
          __A						:			Optional[Any]								=	self.get_feature_extractor()
          __A						:			Union[str, Any]								=	self.get_decoder()

          __A						:			List[str]								=	WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     decoder=_UpperCAmelCase)

          processor.save_pretrained(self.tmpdirname)
          __A						:			List[str]								=	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):

          '''simple docstring'''






          __A						:			Any								=	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						:			str								=	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):

          '''simple docstring'''






          __A						:			Any								=	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):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.get_feature_extractor()
          __A						:			Optional[int]								=	self.get_tokenizer()
          __A						:			str								=	self.get_decoder()

          __A						:			Dict								=	WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     decoder=_UpperCAmelCase)

          __A						:			Tuple								=	floats_list((3, 1000))

          __A						:			List[Any]								=	feature_extractor(_UpperCAmelCase						,     return_tensors='np')
          __A						:			str								=	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):

          '''simple docstring'''






          __A						:			Dict								=	self.get_feature_extractor()
          __A						:			Tuple								=	self.get_tokenizer()
          __A						:			str								=	self.get_decoder()

          __A						:			str								=	WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     decoder=_UpperCAmelCase)

          __A						:			Optional[Any]								=	'This is a test string'

          __A						:			Optional[Any]								=	processor(text=_UpperCAmelCase)

          __A						:			Dict								=	tokenizer(_UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=(2, 10, 16)						,     _UpperCAmelCase=77):

          '''simple docstring'''






          np.random.seed(_UpperCAmelCase)
          return np.random.rand(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	self.get_feature_extractor()
          __A						:			List[str]								=	self.get_tokenizer()
          __A						:			Union[str, Any]								=	self.get_decoder()

          __A						:			str								=	WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     decoder=_UpperCAmelCase)

          __A						:			Union[str, Any]								=	self._get_dummy_logits(shape=(10, 16)						,     seed=13)

          __A						:			Optional[int]								=	processor.decode(_UpperCAmelCase)

          __A						:			str								=	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						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.get_feature_extractor()
          __A						:			Tuple								=	self.get_tokenizer()
          __A						:			int								=	self.get_decoder()

          __A						:			Tuple								=	WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     decoder=_UpperCAmelCase)

          __A						:			List[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						:			List[str]								=	processor.batch_decode(_UpperCAmelCase)
          else:
               with get_context(_UpperCAmelCase).Pool() as pool:
                    __A						:			Optional[int]								=	processor.batch_decode(_UpperCAmelCase						,     _UpperCAmelCase)

          __A						:			Union[str, Any]								=	list(_UpperCAmelCase)

          with get_context('fork').Pool() as p:
               __A						:			Optional[int]								=	decoder.decode_beams_batch(_UpperCAmelCase						,     _UpperCAmelCase)

          __A			,__A			,__A						:			List[Any]								=	[], [], []
          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):

          '''simple docstring'''






          __A						:			Dict								=	self.get_feature_extractor()
          __A						:			List[str]								=	self.get_tokenizer()
          __A						:			List[Any]								=	self.get_decoder()

          __A						:			Tuple								=	WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     decoder=_UpperCAmelCase)

          __A						:			List[str]								=	self._get_dummy_logits()

          __A						:			Tuple								=	15
          __A						:			Any								=	-20.0
          __A						:			Dict								=	-4.0

          __A						:			Dict								=	processor.batch_decode(
              _UpperCAmelCase						,     beam_width=_UpperCAmelCase						,     beam_prune_logp=_UpperCAmelCase						,     token_min_logp=_UpperCAmelCase						,     )
          __A						:			Tuple								=	decoded_processor_out.text

          __A						:			Optional[Any]								=	list(_UpperCAmelCase)

          with get_context('fork').Pool() as pool:
               __A						:			int								=	decoder.decode_beams_batch(
                   _UpperCAmelCase						,     _UpperCAmelCase						,     beam_width=_UpperCAmelCase						,     beam_prune_logp=_UpperCAmelCase						,     token_min_logp=_UpperCAmelCase						,     )

          __A						:			Tuple								=	[d[0][0] for d in decoded_decoder_out]
          __A						:			Tuple								=	[d[0][2] for d in decoded_decoder_out]
          __A						:			Dict								=	[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([-20.054, -18.447]						,     _UpperCAmelCase						,     atol=1e-3))

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.get_feature_extractor()
          __A						:			List[str]								=	self.get_tokenizer()
          __A						:			Union[str, Any]								=	self.get_decoder()

          __A						:			Union[str, Any]								=	WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase						,     feature_extractor=_UpperCAmelCase						,     decoder=_UpperCAmelCase)

          __A						:			Any								=	self._get_dummy_logits()

          __A						:			str								=	2.0
          __A						:			str								=	5.0
          __A						:			Optional[int]								=	-20.0
          __A						:			Optional[int]								=	True

          __A						:			Dict								=	processor.batch_decode(
              _UpperCAmelCase						,     alpha=_UpperCAmelCase						,     beta=_UpperCAmelCase						,     unk_score_offset=_UpperCAmelCase						,     lm_score_boundary=_UpperCAmelCase						,     )
          __A						:			Optional[int]								=	decoded_processor_out.text

          __A						:			Optional[Any]								=	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						:			Tuple								=	decoder.decode_beams_batch(
                   _UpperCAmelCase						,     _UpperCAmelCase						,     )

          __A						:			Tuple								=	[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						:			Dict								=	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):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')

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

          __A						:			str								=	os.listdir(_UpperCAmelCase)
          __A						:			Dict								=	['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):

          '''simple docstring'''






          __A						:			List[str]								=	snapshot_download('hf-internal-testing/processor_with_lm')

          __A						:			Tuple								=	WavaVecaProcessorWithLM.from_pretrained(_UpperCAmelCase)

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

          __A						:			str								=	os.listdir(_UpperCAmelCase)
          __A						:			Optional[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):

          '''simple docstring'''






          __A						:			str								=	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
          __A						:			str								=	AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm')

          __A						:			List[Any]								=	floats_list((3, 1000))

          __A						:			Optional[Any]								=	processor_wavaveca(_UpperCAmelCase						,     return_tensors='np')
          __A						:			Any								=	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						:			Dict								=	self._get_dummy_logits()

          __A						:			Dict								=	processor_wavaveca.batch_decode(_UpperCAmelCase)
          __A						:			List[Any]								=	processor_auto.batch_decode(_UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	self.get_feature_extractor()
          __A						:			Optional[int]								=	self.get_tokenizer()
          __A						:			Dict								=	self.get_decoder()

          __A						:			Union[str, 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  (						_UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Tuple								=	[d[key] for d in offsets]
          return retrieved_list


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
          __A						:			Dict								=	self._get_dummy_logits()[0]

          __A						:			Union[str, Any]								=	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):

          '''simple docstring'''






          __A						:			int								=	WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
          __A						:			Optional[int]								=	self._get_dummy_logits()

          __A						:			Optional[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):

          '''simple docstring'''






          import torch

          __A						:			Union[str, Any]								=	load_dataset('common_voice'						,     'en'						,     split='train'						,     streaming=_UpperCAmelCase)
          __A						:			int								=	ds.cast_column('audio'						,     datasets.Audio(sampling_rate=1_6000))
          __A						:			List[Any]								=	iter(_UpperCAmelCase)
          __A						:			int								=	next(_UpperCAmelCase)

          __A						:			List[str]								=	AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm')
          __A						:			int								=	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						:			Dict								=	processor(sample['audio']['array']						,     return_tensors='pt').input_values

          with torch.no_grad():
               __A						:			str								=	model(_UpperCAmelCase).logits.cpu().numpy()

          __A						:			Optional[int]								=	processor.decode(logits[0]						,     output_word_offsets=_UpperCAmelCase)

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

          __A						:			List[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						:			int								=	torch.tensor(self.get_from_offsets(_UpperCAmelCase						,     'start_time'))
          __A						:			Dict								=	torch.tensor(self.get_from_offsets(_UpperCAmelCase						,     'end_time'))

          # fmt: off
          __A						:			List[Any]								=	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])
          __A						:			List[str]								=	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(_UpperCAmelCase						,     _UpperCAmelCase						,     atol=0.01))
          self.assertTrue(torch.allclose(_UpperCAmelCase						,     _UpperCAmelCase						,     atol=0.01))






'''simple docstring'''




import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					int:

     if not isinstance(__snake_case			,							__snake_case      ):
          __A						:			List[Any]								=	f'Input value of [number={number}] must be an integer'
          raise TypeError(__snake_case      )

     if number < 1:
          __A						:			Union[str, Any]								=	f'Input value of [number={number}] must be > 0'
          raise ValueError(__snake_case      )
     elif number == 1:
          return 3
     elif number == 2:
          return 5
     else:
          __A						:			Optional[Any]								=	int(math.log(number // 3			,							2      )      ) + 2

          __A						:			Union[str, Any]								=	[3, 5]
          __A						:			List[Any]								=	2
          __A						:			Optional[Any]								=	3
          for block in range(1			,							__snake_case      ):
               for _ in range(__snake_case      ):
                    proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]      )
                    proth_index += 1
               increment *= 2

     return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    lowercase__   :				str      =   0
    try:
      lowercase__   :				List[str]      =   proth(number)
    except ValueError:
      print(f"""ValueError: there is no {number}th Proth number""")
      continue

    print(f"""The {number}th Proth number: {value}""")