


'''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


lowercase__					  =		get_tests_dir("fixtures/test_sentencepiece.model")





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






     def 			a_       (      self		)							->     Optional[int]:
          _a            =		tempfile.mkdtemp()

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

          _a            =		SpeechaTextTokenizer.from_pretrained(self.tmpdirname		)
          tokenizer.save_pretrained(self.tmpdirname		)

          _a            =		{
              "feature_size": 24,
              "num_mel_bins": 24,
              "padding_value": 0.0,
              "sampling_rate": 16_000,
              "return_attention_mask": False,
              "do_normalize": True,
          }
          save_json(__UpperCamelCase  ,				save_dir / FEATURE_EXTRACTOR_NAME		)






     def 			a_       (      self  ,				**__UpperCamelCase		)							->     Optional[int]:
          return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,				**__UpperCamelCase		)






     def 			a_       (      self  ,				**__UpperCamelCase		)							->     Union[str, Any]:
          return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,				**__UpperCamelCase		)






     def 			a_       (      self		)							->     Tuple:
          shutil.rmtree(self.tmpdirname		)






     def 			a_       (      self		)							->     List[Any]:
          _a            =		self.get_tokenizer()
          _a            =		self.get_feature_extractor()

          _a            =		SpeechaTextProcessor(tokenizer=__UpperCamelCase  ,				feature_extractor=__UpperCamelCase		)

          processor.save_pretrained(self.tmpdirname		)
          _a            =		SpeechaTextProcessor.from_pretrained(self.tmpdirname		)

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

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






     def 			a_       (      self		)							->     Tuple:
          _a            =		SpeechaTextProcessor(
              tokenizer=self.get_tokenizer()  ,				feature_extractor=self.get_feature_extractor()		)
          processor.save_pretrained(self.tmpdirname		)

          _a            =		self.get_tokenizer(bos_token="(BOS)"  ,				eos_token="(EOS)"		)
          _a            =		self.get_feature_extractor(do_normalize=__UpperCamelCase  ,				padding_value=1.0		)

          _a            =		SpeechaTextProcessor.from_pretrained(
              self.tmpdirname  ,				bos_token="(BOS)"  ,				eos_token="(EOS)"  ,				do_normalize=__UpperCamelCase  ,				padding_value=1.0		)

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

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






     def 			a_       (      self		)							->     List[Any]:
          _a            =		self.get_feature_extractor()
          _a            =		self.get_tokenizer()

          _a            =		SpeechaTextProcessor(tokenizer=__UpperCamelCase  ,				feature_extractor=__UpperCamelCase		)

          _a            =		floats_list((3, 1_000)		)

          _a            =		feature_extractor(__UpperCamelCase  ,				return_tensors="np"		)
          _a            =		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 			a_       (      self		)							->     Any:
          _a            =		self.get_feature_extractor()
          _a            =		self.get_tokenizer()

          _a            =		SpeechaTextProcessor(tokenizer=__UpperCamelCase  ,				feature_extractor=__UpperCamelCase		)

          _a            =		"This is a test string"

          _a            =		processor(text=__UpperCamelCase		)

          _a            =		tokenizer(__UpperCamelCase		)

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






     def 			a_       (      self		)							->     int:
          _a            =		self.get_feature_extractor()
          _a            =		self.get_tokenizer()

          _a            =		SpeechaTextProcessor(tokenizer=__UpperCamelCase  ,				feature_extractor=__UpperCamelCase		)

          _a            =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _a            =		processor.batch_decode(__UpperCamelCase		)
          _a            =		tokenizer.batch_decode(__UpperCamelCase		)

          self.assertListEqual(__UpperCamelCase  ,				__UpperCamelCase		)






     def 			a_       (      self		)							->     str:
          _a            =		self.get_feature_extractor()
          _a            =		self.get_tokenizer()

          _a            =		SpeechaTextProcessor(tokenizer=__UpperCamelCase  ,				feature_extractor=__UpperCamelCase		)

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





'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			float   ,	__lowerCamelCase		:			float   ,	__lowerCamelCase		:			float					)   ->  float:







     '''simple docstring'''






     if mass < 0:
          raise ValueError("The mass of the body cannot be negative"					)
     if radius <= 0:
          raise ValueError("The radius is always a positive non zero integer"					)
     return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

