





from __future__ import annotations

from dataclasses import dataclass





@dataclass
class 					A_					:







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			float
       SCREAMING_SNAKE_CASE_   :			TreeNode | None  			=	None
       SCREAMING_SNAKE_CASE_   :			TreeNode | None  			=	None

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:TreeNode | None  )   ->    bool:

  # Validation
  def is_valid_tree(snake_case__	:TreeNode | None  ) -> bool:
    if node is None:
      return True

    if not isinstance(snake_case__      ,				snake_case__  ):
      return False

    try:
      float(node.data  )
    except (TypeError, ValueError):
      return False

    return is_valid_tree(node.left  ) and is_valid_tree(node.right  )

  if not is_valid_tree(snake_case__  ):
    raise ValueError(
        'Each node should be type of TreeNode and data should be float.'  )

  def is_binary_search_tree_recursive_check(
      snake_case__	:TreeNode | None      ,				snake_case__	:float      ,				snake_case__	:float  ) -> bool:

    if node is None:
      return True

    return (
        left_bound < node.data < right_bound
        and is_binary_search_tree_recursive_check(node.left      ,				snake_case__      ,				node.data  )
        and is_binary_search_tree_recursive_check(
            node.right      ,				node.data      ,				snake_case__  )
    )

  return is_binary_search_tree_recursive_check(snake_case__      ,				-float('inf'  )      ,				float('inf'  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
'''simple docstring'''






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

_UpperCAmelCase    :     List[Any]         =       random.Random()





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			Dict=1.0   ,		__snake_case							:			List[Any]=None   ,		__snake_case							:			List[str]=None				):
     if rng is None:
          _A    		=  global_rng

     _A    		=  []
     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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def __init__(      self      :			Tuple, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any]=7, UpperCamelCase__      :			List[str]=4_00, UpperCamelCase__      :			Any=20_00, UpperCamelCase__      :			int=10, UpperCamelCase__      :			Tuple=1_60, UpperCamelCase__      :			Tuple=8, UpperCamelCase__      :			List[str]=0.0, UpperCamelCase__      :			Optional[Any]=40_00, UpperCamelCase__      :			List[str]=False, UpperCamelCase__      :			List[Any]=True, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  min_seq_length
        _A    		=  max_seq_length
        _A    		=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _A    		=  padding_value
        _A    		=  sampling_rate
        _A    		=  return_attention_mask
        _A    		=  do_normalize
        _A    		=  feature_size
        _A    		=  chunk_length
        _A    		=  hop_length
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[Any]:
        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 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			List[Any]=False, UpperCamelCase__      :			str=False     )  ->			Any:
        def _flatten(UpperCamelCase__      :			List[str]     ):
             return list(itertools.chain(*UpperCamelCase__     )     )

        if equal_length:
             _A    		=  [floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
        else:
             # make sure that inputs increase in size
             _A    		=  [
                 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    		=  [np.asarray(UpperCamelCase__     ) for x in speech_inputs]
        return speech_inputs







@require_torch
@require_torchaudio
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   WhisperFeatureExtractor if is_speech_available() else None
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        _A    		=  WhisperFeatureExtractionTester(self     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  feat_extract_first.save_pretrained(UpperCamelCase__     )[0]
             check_json_file_has_correct_format(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_pretrained(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  os.path.join(UpperCamelCase__, 'feat_extract.json'     )
             feat_extract_first.to_json_file(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_json_file(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        # create three inputs of length 800, 1000, and 1200
        _A    		=  [floats_list((1, x)     )[0] for x in range(8_00, 14_00, 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        # Test feature size
        _A    		=  feature_extractor(UpperCamelCase__, 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    		=  feature_extractor(speech_inputs[0], return_tensors='np'     ).input_features
        _A    		=  feature_extractor(np_speech_inputs[0], return_tensors='np'     ).input_features
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test batched
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test 2-D numpy arrays are batched.
        _A    		=  [floats_list((1, x)     )[0] for x in (8_00, 8_00, 8_00)]
        _A    		=  np.asarray(UpperCamelCase__     )
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test truncation required
        _A    		=  [floats_list((1, x)     )[0] for x in range(2_00, (feature_extractor.n_samples + 5_00), 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        _A    		=  [x[: feature_extractor.n_samples] for x in speech_inputs]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs_truncated]

        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        import torch

        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  np.random.rand(1_00, 32     ).astype(np.floataa     )
        _A    		=  np_speech_inputs.tolist()

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

        return [x["array"] for x in speech_samples]
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        # fmt: off
        _A    		=  torch.tensor(
            [
                0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
                0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
                0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
                -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
            ]     )
        # fmt: on

        _A    		=  self._load_datasamples(1     )
        _A    		=  WhisperFeatureExtractor()
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='pt'     ).input_features
        self.assertEqual(input_features.shape, (1, 80, 30_00)     )
        self.assertTrue(torch.allclose(input_features[0, 0, :30], UpperCamelCase__, atol=1e-4     )     )

   def 	__UpperCAmelCase   (      self      :			int     )  ->			Any:
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  self._load_datasamples(1     )[0]
        _A    		=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
        _A    		=  feat_extract.zero_mean_unit_var_norm([audio], attention_mask=UpperCamelCase__     )[0]

        self.assertTrue(np.all(np.mean(UpperCamelCase__     ) < 1e-3     )     )
        self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__     ) - 1     ) < 1e-3     )     )



