

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


__A	:       Optional[int]        =						get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')





@require_torch
class      __UpperCamelCase					(  unittest.TestCase		):


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   tempfile.mkdtemp()

							A     =   SpeechTaTokenizer(__SCREAMING_SNAKE_CASE)
							tokenizer.save_pretrained(self.tmpdirname)

							A     =   {
							    "feature_size": 1,
							    "padding_value": 0.0,
							    "sampling_rate": 1_6_0_0_0,
							    "do_normalize": False,
							    "num_mel_bins": 8_0,
							    "hop_length": 1_6,
							    "win_length": 6_4,
							    "win_function": "hann_window",
							    "fmin": 8_0,
							    "fmax": 7_6_0_0,
							    "mel_floor": 1E-10,
							    "reduction_factor": 2,
							    "return_attention_mask": True,
							}

							A     =   os.path.join(self.tmpdirname	,	__SCREAMING_SNAKE_CASE)
							with open(self.feature_extraction_file	,	"w"	,	encoding="utf-8") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + "\n")


		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	**__SCREAMING_SNAKE_CASE					:						int):
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]):
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							shutil.rmtree(self.tmpdirname)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizer()
							A     =   self.get_feature_extractor()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							processor.save_pretrained(self.tmpdirname)
							A     =   SpeechTaProcessor.from_pretrained(self.tmpdirname)

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   SpeechTaProcessor(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=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

							A     =   SpeechTaProcessor.from_pretrained(
							    self.tmpdirname	,	bos_token="(BOS)"	,	eos_token="(EOS)"	,	do_normalize=__SCREAMING_SNAKE_CASE	,	padding_value=1.0)

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

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


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio=__SCREAMING_SNAKE_CASE	,	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					:						Tuple):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   floats_list((3, 1_0_0_0))

							A     =   feature_extractor(audio_target=__SCREAMING_SNAKE_CASE	,	return_tensors="np")
							A     =   processor(audio_target=__SCREAMING_SNAKE_CASE	,	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					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

							A     =   "This is a test string"

							A     =   processor(text_target=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer(__SCREAMING_SNAKE_CASE)

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


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

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

							A     =   processor.batch_decode(__SCREAMING_SNAKE_CASE)
							A     =   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.get_feature_extractor()
							A     =   self.get_tokenizer()

							A     =   SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,	feature_extractor=__SCREAMING_SNAKE_CASE)

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












from itertools import compress, repeat
from math import ceil, sqrt




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       UpperCamelCase_ 							=	bytearray(b"\x01"	) * ((num >> 1) - 1)

       for i in range(3       ,		int(sqrt(UpperCamelCase_	)	) + 1       ,		2	):
              if sieve[(i >> 1) - 1]:
                     UpperCamelCase_ 							=	i**2
                     UpperCamelCase_ 							=	repeat(
                         0       ,		ceil((num - i_squared) / (i << 1)	)	)

       return [2] + list(compress(range(3       ,		UpperCamelCase_       ,		2	)       ,		UpperCamelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

