


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCamelCase					:       Tuple			    =     random.Random()

if is_torch_available():
    import torch



def UpperCamelCase_				(				__a			,				__a=1.0			,				__a=None			,				__a=None      )	->   Dict:
      if rng is None:
            a__	:  int     =   global_rng

      a__	:  Union[str, Any]     =   []
      for batch_idx in range(shape[0]      ):
            values.append([]      )
            for _ in range(shape[1]      ):
                  values[-1].append(rng.random() * scale      )

      return values



class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def __init__( self			:  List[Any]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Tuple=7				,				lowerCamelCase__			:  Tuple=400				,				lowerCamelCase__			:  List[str]=2_000				,				lowerCamelCase__			:  Union[str, Any]=1				,				lowerCamelCase__			:  Optional[Any]=0.0				,				lowerCamelCase__			:  Any=16_000				,				lowerCamelCase__			:  str=True				,				lowerCamelCase__			:  List[str]=True				,				):
        a__	:  List[Any]     =   parent
        a__	:  Dict     =   batch_size
        a__	:  List[Any]     =   min_seq_length
        a__	:  Tuple     =   max_seq_length
        a__	:  Tuple     =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__	:  Dict     =   feature_size
        a__	:  Any     =   padding_value
        a__	:  List[Any]     =   sampling_rate
        a__	:  Tuple     =   return_attention_mask
        a__	:  int     =   do_normalize

  def    _UpperCamelCase( self			:  Tuple       ):
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  str=False				,				lowerCamelCase__			:  Optional[Any]=False       ):
        def _flatten(lowerCamelCase__			:  Dict       ):
              return list(itertools.chain(*lowerCamelCase__       )       )

        if equal_length:
              a__	:  List[str]     =   floats_list((self.batch_size, self.max_seq_length)       )
        else:
              # make sure that inputs increase in size
              a__	:  Tuple     =   [
                  _flatten(floats_list((x, self.feature_size)       )       )
                  for x in range(self.min_seq_length				,				self.max_seq_length				,				self.seq_length_diff       )
              ]

        if numpify:
              a__	:  Any     =   [np.asarray(lowerCamelCase__       ) for x in speech_inputs]

        return speech_inputs




@require_torch
@require_torchaudio
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		ASTFeatureExtractor

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  List[Any]     =   ASTFeatureExtractionTester(self       )

  def    _UpperCamelCase( self			:  str       ):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        a__	:  Optional[Any]     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        # create three inputs of length 800, 1000, and 1200
        a__	:  List[Any]     =   [floats_list((1, x)       )[0] for x in range(800				,				1_400				,				200       )]
        a__	:  str     =   [np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

        # Test not batched input
        a__	:  Tuple     =   feat_extract(speech_inputs[0]				,				return_tensors="np"       ).input_values
        a__	:  Union[str, Any]     =   feat_extract(np_speech_inputs[0]				,				return_tensors="np"       ).input_values
        self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-3       )       )

        # Test batched
        a__	:  List[str]     =   feat_extract(lowerCamelCase__				,				padding=lowerCamelCase__				,				return_tensors="np"       ).input_values
        a__	:  str     =   feat_extract(lowerCamelCase__				,				padding=lowerCamelCase__				,				return_tensors="np"       ).input_values
        for enc_seq_a, enc_seq_a in zip(lowerCamelCase__				,				lowerCamelCase__       ):
              self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-3       )       )

        # Test 2-D numpy arrays are batched.
        a__	:  str     =   [floats_list((1, x)       )[0] for x in (800, 800, 800)]
        a__	:  List[Any]     =   np.asarray(lowerCamelCase__       )
        a__	:  int     =   feat_extract(lowerCamelCase__				,				return_tensors="np"       ).input_values
        a__	:  str     =   feat_extract(lowerCamelCase__				,				return_tensors="np"       ).input_values
        for enc_seq_a, enc_seq_a in zip(lowerCamelCase__				,				lowerCamelCase__       ):
              self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-3       )       )

  @require_torch
  def    _UpperCamelCase( self			:  str       ):
        import torch

        a__	:  str     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        a__	:  int     =   np.random.rand(100       ).astype(np.floataa       )
        a__	:  List[str]     =   np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
              a__	:  Union[str, Any]     =   feature_extractor.pad([{"input_values": inputs}]				,				return_tensors="np"       )
              self.assertTrue(np_processed.input_values.dtype == np.floataa       )
              a__	:  str     =   feature_extractor.pad([{"input_values": inputs}]				,				return_tensors="pt"       )
              self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  List[str]       ):
        from datasets import load_dataset

        a__	:  Optional[Any]     =   load_dataset("hf-internal-testing/librispeech_asr_dummy"				,				"clean"				,				split="validation"       )
        # automatic decoding with librispeech
        a__	:  Union[str, Any]     =   ds.sort("id"       ).select(range(lowerCamelCase__       )       )[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]

  @require_torch
  def    _UpperCamelCase( self			:  str       ):
        # fmt: off
        a__	:  List[Any]     =   torch.tensor(
            [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
             -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
             -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
             -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]       )
        # fmt: on

        a__	:  Union[str, Any]     =   self._load_datasamples(1       )
        a__	:  Dict     =   ASTFeatureExtractor()
        a__	:  List[str]     =   feature_extractor(lowerCamelCase__				,				return_tensors="pt"       ).input_values
        self.assertEquals(input_values.shape				,				(1, 1_024, 128)       )
        self.assertTrue(torch.allclose(input_values[0, 0, :30]				,				lowerCamelCase__				,				atol=1E-4       )       )



from collections.abc import Callable






def   lowerCamelCase__							(							__lowerCAmelCase    :    Callable[[float], float]       ,				__lowerCAmelCase    :    float       ,				__lowerCAmelCase    :    float							):




			"""simple docstring"""


			lowerCAmelCase_         =      a
			lowerCAmelCase_         =      b
			if function(__lowerCAmelCase							) == 0:  # one of the a or b is a root for the function
						return a
			elif function(__lowerCAmelCase							) == 0:
						return b
			elif (
			    function(__lowerCAmelCase							) * function(__lowerCAmelCase							) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("could not find root in given interval."							)
			else:
						lowerCAmelCase_         =      start + (end - start) / 2.0
						while abs(start - mid							) > 10**-7:  # until precisely equals to 10^-7
									if function(__lowerCAmelCase							) == 0:
												return mid
									elif function(__lowerCAmelCase							) * function(__lowerCAmelCase							) < 0:
												lowerCAmelCase_         =      mid
									else:
												lowerCAmelCase_         =      mid
									lowerCAmelCase_         =      start + (end - start) / 2.0
						return mid






def   lowerCamelCase__							(							__lowerCAmelCase    :    float							):




			"""simple docstring"""


			return x**3 - 2 * x - 5


if __name__ == "__main__":
			print(bisection(f, 1, 10_00))

			import doctest

			doctest.testmod()



