






"""simple docstring"""


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

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase							:       str       					=		get_tests_dir('fixtures/test_sentencepiece.model')






@require_torch
@require_torchaudio
@require_sentencepiece
class 		snake_case    (    unittest.TestCase			):





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      a	:     str							=  tempfile.mkdtemp()

      a	:     Union[str, Any]							=  ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
      a	:     Optional[Any]							=  dict(zip(_lowercase							,						range(len(_lowercase     )     )     )     )
      a	:     str							=  Path(self.tmpdirname     )
      save_json(_lowercase							,						save_dir / VOCAB_FILES_NAMES['vocab_file']     )
      if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
        copyfile(_lowercase							,						save_dir / VOCAB_FILES_NAMES['spm_file']     )

      a	:     Dict							=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
      tokenizer.save_pretrained(self.tmpdirname     )

      a	:     Tuple							=  {
          'feature_size': 2_4,
          'num_mel_bins': 2_4,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }
      save_json(_lowercase							,						save_dir / FEATURE_EXTRACTOR_NAME     )





    def 							lowerCamelCase__ (				self      :   Dict							,						**A      :   List[Any]     ):


      '''simple docstring'''




      return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							,						**_lowercase     )





    def 							lowerCamelCase__ (				self      :   Any							,						**A      :   List[Any]     ):


      '''simple docstring'''




      return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							,						**_lowercase     )





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      shutil.rmtree(self.tmpdirname     )





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      a	:     Tuple							=  self.get_tokenizer()
      a	:     List[str]							=  self.get_feature_extractor()

      a	:     Dict							=  SpeechaTextProcessor(tokenizer=_lowercase							,						feature_extractor=_lowercase     )

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

      self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer.get_vocab()     )
      self.assertIsInstance(processor.tokenizer							,						_lowercase     )

      self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor.to_json_string()     )
      self.assertIsInstance(processor.feature_extractor							,						_lowercase     )





    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      a	:     Any							=  SpeechaTextProcessor(
          tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()     )
      processor.save_pretrained(self.tmpdirname     )

      a	:     Tuple							=  self.get_tokenizer(bos_token='(BOS)'							,						eos_token='(EOS)'     )
      a	:     int							=  self.get_feature_extractor(do_normalize=_lowercase							,						padding_value=1.0     )

      a	:     int							=  SpeechaTextProcessor.from_pretrained(
          self.tmpdirname							,						bos_token='(BOS)'							,						eos_token='(EOS)'							,						do_normalize=_lowercase							,						padding_value=1.0     )

      self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()     )
      self.assertIsInstance(processor.tokenizer							,						_lowercase     )

      self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor_add_kwargs.to_json_string()     )
      self.assertIsInstance(processor.feature_extractor							,						_lowercase     )





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      a	:     int							=  self.get_feature_extractor()
      a	:     Tuple							=  self.get_tokenizer()

      a	:     List[Any]							=  SpeechaTextProcessor(tokenizer=_lowercase							,						feature_extractor=_lowercase     )

      a	:     Union[str, Any]							=  floats_list((3, 1_0_0_0)     )

      a	:     str							=  feature_extractor(_lowercase							,						return_tensors='np'     )
      a	:     Dict							=  processor(_lowercase							,						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 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      a	:     int							=  self.get_feature_extractor()
      a	:     Union[str, Any]							=  self.get_tokenizer()

      a	:     Any							=  SpeechaTextProcessor(tokenizer=_lowercase							,						feature_extractor=_lowercase     )

      a	:     str							=  'This is a test string'

      a	:     List[str]							=  processor(text=_lowercase     )

      a	:     Optional[int]							=  tokenizer(_lowercase     )

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





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      a	:     Tuple							=  self.get_feature_extractor()
      a	:     Dict							=  self.get_tokenizer()

      a	:     Union[str, Any]							=  SpeechaTextProcessor(tokenizer=_lowercase							,						feature_extractor=_lowercase     )

      a	:     Optional[Any]							=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      a	:     Optional[Any]							=  processor.batch_decode(_lowercase     )
      a	:     Tuple							=  tokenizer.batch_decode(_lowercase     )

      self.assertListEqual(_lowercase							,						_lowercase     )





    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      a	:     List[Any]							=  self.get_feature_extractor()
      a	:     Optional[Any]							=  self.get_tokenizer()

      a	:     List[str]							=  SpeechaTextProcessor(tokenizer=_lowercase							,						feature_extractor=_lowercase     )

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









import random







class 				a_    :

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :float  ,		_lowercase      :float)						->   None:
												UpperCAmelCase_					=	x
												UpperCAmelCase_					=	y

						def       __a			(       self      :str)						->   bool:
												return (self.x**2 + self.y**2) <= 1




						@classmethod
						def       __a			(       cls      :Optional[Any])						->   str:
												return cls(x=random.random()  ,		y=random.random())







def 							A     (						__UpperCAmelCase      )			->			float:



						'''simple docstring'''






						if number_of_simulations < 1:
												raise ValueError('''At least one simulation is necessary to estimate PI.'''      )

						UpperCAmelCase_					=	0
						for _ in range(__UpperCAmelCase      ):
												UpperCAmelCase_					=	Point.random_unit_square()

												if random_point.is_in_unit_circle():
																		number_in_unit_circle += 1

						return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							UpperCamelCase_      			=  "Please enter the desired number of Monte Carlo simulations: "
							UpperCamelCase_      			=  estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")





