
import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
      from transformers import WhisperFeatureExtractor

if is_torch_available():
      import torch

lowercase__          =random.Random()




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Optional[int]=1.0  ,       lowerCAmelCase__							:    Dict=None  ,       lowerCAmelCase__							:    Optional[int]=None  ):
 if rng is None:
  __a			:  List[Any]		    =  global_rng

 __a			:  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_torchaudio
class 		UpperCamelCase__  (	unittest.TestCase   ):
   def __init__(self			: str ,    snake_case_			: str ,    snake_case_			: List[str]=7 ,    snake_case_			: str=4_0_0 ,    snake_case_			: Union[str, Any]=2_0_0_0 ,    snake_case_			: Optional[Any]=1_0 ,    snake_case_			: int=1_6_0 ,    snake_case_			: Union[str, Any]=8 ,    snake_case_			: Tuple=0.0 ,    snake_case_			: Optional[int]=4_0_0_0 ,    snake_case_			: int=False ,    snake_case_			: Any=True ,    ):
    __a			:  Optional[int]		    =  parent
    __a			:  List[Any]		    =  batch_size
    __a			:  Optional[Any]		    =  min_seq_length
    __a			:  str		    =  max_seq_length
    __a			:  Union[str, Any]		    =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    __a			:  Tuple		    =  padding_value
    __a			:  Any		    =  sampling_rate
    __a			:  Union[str, Any]		    =  return_attention_mask
    __a			:  Any		    =  do_normalize
    __a			:  Optional[int]		    =  feature_size
    __a			:  Tuple		    =  chunk_length
    __a			:  Optional[int]		    =  hop_length
   def 				lowerCAmelCase      (self			: int       ):
    return {
        "feature_size": self.feature_size,
        "hop_length": self.hop_length,
        "chunk_length": self.chunk_length,
        "padding_value": self.padding_value,
        "sampling_rate": self.sampling_rate,
        "return_attention_mask": self.return_attention_mask,
        "do_normalize": self.do_normalize,
    }







   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Tuple=False ,    snake_case_			: List[str]=False       ):
    def _flatten(snake_case_			: Tuple       ):
     return list(itertools.chain(*snake_case_       )       )

    if equal_length:
     __a			:  Optional[int]		    =  [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
    else:
     # make sure that inputs increase in size
     __a			:  Dict		    =  [
         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(snake_case_       ) for x in speech_inputs]
    return speech_inputs





@require_torch
@require_torchaudio
class 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				WhisperFeatureExtractor if is_speech_available() else None
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  List[str]		    =  WhisperFeatureExtractionTester(self       )
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    __a			:  int		    =  self.feature_extraction_class(**self.feat_extract_dict       )

    with tempfile.TemporaryDirectory() as tmpdirname:
     __a			:  Optional[int]		    =  feat_extract_first.save_pretrained(snake_case_       )[0]
     check_json_file_has_correct_format(snake_case_       )
     __a			:  Optional[Any]		    =  self.feature_extraction_class.from_pretrained(snake_case_       )

    __a			:  List[Any]		    =  feat_extract_first.to_dict()
    __a			:  Any		    =  feat_extract_second.to_dict()
    __a			:  int		    =  feat_extract_first.mel_filters
    __a			:  int		    =  feat_extract_second.mel_filters
    self.assertTrue(np.allclose(snake_case_ ,    snake_case_       )       )
    self.assertEqual(snake_case_ ,    snake_case_       )
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    __a			:  int		    =  self.feature_extraction_class(**self.feat_extract_dict       )

    with tempfile.TemporaryDirectory() as tmpdirname:
     __a			:  Any		    =  os.path.join(snake_case_ ,    '''feat_extract.json'''       )
     feat_extract_first.to_json_file(snake_case_       )
     __a			:  str		    =  self.feature_extraction_class.from_json_file(snake_case_       )

    __a			:  Tuple		    =  feat_extract_first.to_dict()
    __a			:  int		    =  feat_extract_second.to_dict()
    __a			:  str		    =  feat_extract_first.mel_filters
    __a			:  Dict		    =  feat_extract_second.mel_filters
    self.assertTrue(np.allclose(snake_case_ ,    snake_case_       )       )
    self.assertEqual(snake_case_ ,    snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    # Tests that all call wrap to encode_plus and batch_encode_plus
    __a			:  List[Any]		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    # create three inputs of length 800, 1000, and 1200
    __a			:  Optional[Any]		    =  [floats_list((1, x)       )[0] for x in range(8_0_0 ,    1_4_0_0 ,    2_0_0       )]
    __a			:  Tuple		    =  [np.asarray(snake_case_       ) for speech_input in speech_inputs]

    # Test feature size
    __a			:  List[str]		    =  feature_extractor(snake_case_ ,    padding='''max_length''' ,    return_tensors='''np'''       ).input_features
    self.assertTrue(input_features.ndim == 3       )
    self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames       )
    self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size       )

    # Test not batched input
    __a			:  Any		    =  feature_extractor(speech_inputs[0] ,    return_tensors='''np'''       ).input_features
    __a			:  Union[str, Any]		    =  feature_extractor(np_speech_inputs[0] ,    return_tensors='''np'''       ).input_features
    self.assertTrue(np.allclose(snake_case_ ,    snake_case_ ,    atol=1E-3       )       )

    # Test batched
    __a			:  int		    =  feature_extractor(snake_case_ ,    return_tensors='''np'''       ).input_features
    __a			:  Optional[int]		    =  feature_extractor(snake_case_ ,    return_tensors='''np'''       ).input_features
    for enc_seq_a, enc_seq_a in zip(snake_case_ ,    snake_case_       ):
     self.assertTrue(np.allclose(snake_case_ ,    snake_case_ ,    atol=1E-3       )       )

    # Test 2-D numpy arrays are batched.
    __a			:  Dict		    =  [floats_list((1, x)       )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
    __a			:  Union[str, Any]		    =  np.asarray(snake_case_       )
    __a			:  Any		    =  feature_extractor(snake_case_ ,    return_tensors='''np'''       ).input_features
    __a			:  Optional[Any]		    =  feature_extractor(snake_case_ ,    return_tensors='''np'''       ).input_features
    for enc_seq_a, enc_seq_a in zip(snake_case_ ,    snake_case_       ):
     self.assertTrue(np.allclose(snake_case_ ,    snake_case_ ,    atol=1E-3       )       )

    # Test truncation required
    __a			:  Tuple		    =  [floats_list((1, x)       )[0] for x in range(2_0_0 ,    (feature_extractor.n_samples + 5_0_0) ,    2_0_0       )]
    __a			:  List[Any]		    =  [np.asarray(snake_case_       ) for speech_input in speech_inputs]

    __a			:  Dict		    =  [x[: feature_extractor.n_samples] for x in speech_inputs]
    __a			:  Union[str, Any]		    =  [np.asarray(snake_case_       ) for speech_input in speech_inputs_truncated]

    __a			:  List[Any]		    =  feature_extractor(snake_case_ ,    return_tensors='''np'''       ).input_features
    __a			:  Optional[Any]		    =  feature_extractor(snake_case_ ,    return_tensors='''np'''       ).input_features
    for enc_seq_a, enc_seq_a in zip(snake_case_ ,    snake_case_       ):
     self.assertTrue(np.allclose(snake_case_ ,    snake_case_ ,    atol=1E-3       )       )
   def 				lowerCAmelCase      (self			: List[str]       ):
    import torch

    __a			:  List[str]		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    __a			:  List[str]		    =  np.random.rand(1_0_0 ,    3_2       ).astype(np.floataa       )
    __a			:  Any		    =  np_speech_inputs.tolist()

    for inputs in [py_speech_inputs, np_speech_inputs]:
     __a			:  Optional[Any]		    =  feature_extractor.pad([{'''input_features''': inputs}] ,    return_tensors='''np'''       )
     self.assertTrue(np_processed.input_features.dtype == np.floataa       )
     __a			:  Optional[Any]		    =  feature_extractor.pad([{'''input_features''': inputs}] ,    return_tensors='''pt'''       )
     self.assertTrue(pt_processed.input_features.dtype == torch.floataa       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Any       ):
    __a			:  int		    =  load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,    '''clean''' ,    split='''validation'''       )
    # automatic decoding with librispeech
    __a			:  List[str]		    =  ds.sort('''id'''       ).select(range(snake_case_       )       )[:num_samples]['''audio''']

    return [x["array"] for x in speech_samples]
   def 				lowerCAmelCase      (self			: int       ):
    # fmt: off
    __a			:  Optional[int]		    =  torch.tensor(
        [
            0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
            0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
            0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
            -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
        ]       )
    # fmt: on

    __a			:  List[str]		    =  self._load_datasamples(1       )
    __a			:  Dict		    =  WhisperFeatureExtractor()
    __a			:  List[Any]		    =  feature_extractor(snake_case_ ,    return_tensors='''pt'''       ).input_features
    self.assertEqual(input_features.shape ,    (1, 8_0, 3_0_0_0)       )
    self.assertTrue(torch.allclose(input_features[0, 0, :3_0] ,    snake_case_ ,    atol=1E-4       )       )







   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Dict		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
    __a			:  Optional[Any]		    =  self._load_datasamples(1       )[0]
    __a			:  int		    =  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
    __a			:  List[Any]		    =  feat_extract.zero_mean_unit_var_norm([audio] ,    attention_mask=snake_case_       )[0]

    self.assertTrue(np.all(np.mean(snake_case_       ) < 1E-3       )       )
    self.assertTrue(np.all(np.abs(np.var(snake_case_       ) - 1       ) < 1E-3       )       )


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase__          =10




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  if array[i] == target:
   return i
 return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 __a			:  List[Any]		    =  0
 __a			:  Union[str, Any]		    =  len(lowerCAmelCase__  )
 while left <= right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

  __a			:  str		    =  (left + right) // 3 + 1
  __a			:  List[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   __a			:  List[str]		    =  one_third - 1
  elif array[two_third] < target:
   __a			:  List[str]		    =  two_third + 1

  else:
   __a			:  Dict		    =  one_third + 1
   __a			:  Any		    =  two_third - 1
 else:
  return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 if left < right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Union[str, Any]		    =  (left + right) // 3 + 1
  __a			:  Optional[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   return rec_ternary_search(lowerCAmelCase__  ,       one_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  else:
   return rec_ternary_search(one_third + 1  ,       two_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 else:
  return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase__          =input('Enter numbers separated by comma:\n').strip()
      lowercase__          =[int(item.strip()) for item in user_input.split(',')]
      assert collection == sorted(collection), F"List must be ordered.\n{collection}."
      lowercase__          =int(input('Enter the number to be found in the list:\n').strip())
      lowercase__          =ite_ternary_search(collection, target)
      lowercase__          =rec_ternary_search(0, len(collection) - 1, collection, target)
      if resulta != -1:
            print(F"""Iterative search: {target} found at positions: {resulta}""")
            print(F"""Recursive search: {target} found at positions: {resulta}""")
      else:
            print('Not found')
