
'''simple docstring'''
import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
  pass

if is_speech_available():
  from transformers import EncodecFeatureExtractor, MusicgenProcessor


a_     =  random.Random()

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE=1.0       ,						__SCREAMING_SNAKE_CASE=None       ,						__SCREAMING_SNAKE_CASE=None    ):
    """simple docstring"""






    if rng is None:
        snake_case_	:      Union[str, Any]            =  global_rng

    snake_case_	:      Optional[int]            =  []
    for batch_idx in range(shape[0]    ):
        values.append([]    )
        for _ in range(shape[1]    ):
            values[-1].append(rng.random() * scale    )

    return values






@require_torch
@require_sentencepiece
class 				UpperCAmelCase_			(		unittest.TestCase   ):





 def     snake_case__						(      self):
     snake_case_	:      Union[str, Any]            =  "facebook/musicgen-small"
     snake_case_	:      str            =  tempfile.mkdtemp()





 def     snake_case__						(      self	,    **lowercase_):
     return TaTokenizer.from_pretrained(self.checkpoint	,    **lowerCAmelCase__)





 def     snake_case__						(      self	,    **lowercase_):
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint	,    **lowerCAmelCase__)





 def     snake_case__						(      self):
     shutil.rmtree(self.tmpdirname)





 def     snake_case__						(      self):
     snake_case_	:      Optional[int]            =  self.get_tokenizer()
     snake_case_	:      Union[str, Any]            =  self.get_feature_extractor()

     snake_case_	:      Union[str, Any]            =  MusicgenProcessor(tokenizer=lowerCAmelCase__	,    feature_extractor=lowerCAmelCase__)

     processor.save_pretrained(self.tmpdirname)
     snake_case_	:      Dict            =  MusicgenProcessor.from_pretrained(self.tmpdirname)

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

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





 def     snake_case__						(      self):
     snake_case_	:      int            =  MusicgenProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor())
     processor.save_pretrained(self.tmpdirname)

     snake_case_	:      List[Any]            =  self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)")
     snake_case_	:      List[str]            =  self.get_feature_extractor(do_normalize=lowerCAmelCase__	,    padding_value=1.0)

     snake_case_	:      Dict            =  MusicgenProcessor.from_pretrained(
         self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=lowerCAmelCase__	,    padding_value=1.0)

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

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





 def     snake_case__						(      self):
     snake_case_	:      Optional[int]            =  self.get_feature_extractor()
     snake_case_	:      int            =  self.get_tokenizer()

     snake_case_	:      Tuple            =  MusicgenProcessor(tokenizer=lowerCAmelCase__	,    feature_extractor=lowerCAmelCase__)

     snake_case_	:      Optional[Any]            =  floats_list((3, 10_00))

     snake_case_	:      List[Any]            =  feature_extractor(lowerCAmelCase__	,    return_tensors="np")
     snake_case_	:      Optional[Any]            =  processor(lowerCAmelCase__	,    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     snake_case__						(      self):
     snake_case_	:      List[Any]            =  self.get_feature_extractor()
     snake_case_	:      int            =  self.get_tokenizer()

     snake_case_	:      int            =  MusicgenProcessor(tokenizer=lowerCAmelCase__	,    feature_extractor=lowerCAmelCase__)

     snake_case_	:      str            =  "This is a test string"

     snake_case_	:      Optional[Any]            =  processor(text=lowerCAmelCase__)

     snake_case_	:      int            =  tokenizer(lowerCAmelCase__)

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





 def     snake_case__						(      self):
     snake_case_	:      List[str]            =  self.get_feature_extractor()
     snake_case_	:      str            =  self.get_tokenizer()

     snake_case_	:      Optional[Any]            =  MusicgenProcessor(tokenizer=lowerCAmelCase__	,    feature_extractor=lowerCAmelCase__)

     snake_case_	:      str            =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     snake_case_	:      Optional[int]            =  processor.batch_decode(sequences=lowerCAmelCase__)
     snake_case_	:      Any            =  tokenizer.batch_decode(lowerCAmelCase__)

     self.assertListEqual(lowerCAmelCase__	,    lowerCAmelCase__)





 def     snake_case__						(      self):
     snake_case_	:      int            =  self.get_feature_extractor()
     snake_case_	:      str            =  self.get_tokenizer()

     snake_case_	:      Optional[Any]            =  MusicgenProcessor(tokenizer=lowerCAmelCase__	,    feature_extractor=lowerCAmelCase__)

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





 def     snake_case__						(      self):
     snake_case_	:      int            =  self.get_feature_extractor(padding_side="left")
     snake_case_	:      List[Any]            =  self.get_tokenizer()

     snake_case_	:      List[str]            =  MusicgenProcessor(tokenizer=lowerCAmelCase__	,    feature_extractor=lowerCAmelCase__)

     snake_case_	:      Tuple            =  [floats_list((1, x))[0] for x in range(5	,    20	,    5)]
     snake_case_	:      Any            =  processor(lowerCAmelCase__).padding_mask

     snake_case_	:      List[Any]            =  np.asarray(floats_list((3, 20)))[:, None, :]
     snake_case_	:      List[str]            =  processor.batch_decode(lowerCAmelCase__	,    padding_mask=lowerCAmelCase__)

     self.assertIsInstance(lowerCAmelCase__	,    lowerCAmelCase__)

     for audio in decoded_audios:
         self.assertIsInstance(lowerCAmelCase__	,    np.ndarray)

     self.assertTrue(decoded_audios[0].shape == (1, 10))
     self.assertTrue(decoded_audios[1].shape == (1, 15))
     self.assertTrue(decoded_audios[2].shape == (1, 20))






'''simple docstring'''
from math import exp

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    return 8 * x - 2 * exp(-x    )

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  lower_bound
    snake_case_	:      int            =  upper_bound
    for _ in range(0,						__SCREAMING_SNAKE_CASE    ):
        snake_case_     ,	snake_case_	:      List[str]            =  xa, xa - (f(__SCREAMING_SNAKE_CASE    ) * (xa - xa)) / (f(__SCREAMING_SNAKE_CASE    ) - f(__SCREAMING_SNAKE_CASE    ))
    return xa


if __name__ == "__main__":
  print(f"""Example: {secant_method(1, 3, 2)}""")




