




'''simple docstring'''



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Union[str, Any]        =						None
       A						:				int        =						None







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.feat_extract_tester.prepare_feat_extract_dict()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             self.assertTrue(hasattr(A,							'feature_size'  )  )
             self.assertTrue(hasattr(A,							'sampling_rate'  )  )
             self.assertTrue(hasattr(A,							'padding_value'  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.feat_extract_tester.prepare_inputs_for_common()
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						BatchFeature({input_name: speech_inputs}  )

             self.assertTrue(all(len(A  ) == len(A  ) for x, y in zip(A,							processed_features[input_name]  )  )  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.feat_extract_tester.prepare_inputs_for_common(equal_length=A  )
             SCREAMING_SNAKE_CASE   :      int    					=						BatchFeature({input_name: speech_inputs},							tensor_type='np'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						processed_features[input_name]

             if len(batch_features_input.shape  ) < 3:
                   SCREAMING_SNAKE_CASE   :      int    					=						batch_features_input[:, :, None]

             self.assertTrue(
                 batch_features_input.shape
                 == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.feature_size)  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.feat_extract_tester.prepare_inputs_for_common(equal_length=A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             SCREAMING_SNAKE_CASE   :      str    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      Dict    					=						BatchFeature({input_name: speech_inputs},							tensor_type='pt'  )

             SCREAMING_SNAKE_CASE   :      int    					=						processed_features[input_name]

             if len(batch_features_input.shape  ) < 3:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						batch_features_input[:, :, None]

             self.assertTrue(
                 batch_features_input.shape
                 == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.feature_size)  )







       @require_tf
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.feat_extract_tester.prepare_inputs_for_common(equal_length=A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      Tuple    					=						BatchFeature({input_name: speech_inputs},							tensor_type='tf'  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						processed_features[input_name]

             if len(batch_features_input.shape  ) < 3:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						batch_features_input[:, :, None]

             self.assertTrue(
                 batch_features_input.shape
                 == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.feature_size)  )







       def        UpperCamelCase_							(      self,							A=False  ):







             '''simple docstring'''







             def _inputs_have_equal_length(A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						len(input[0]  )
                   for input_slice in input[1:]:
                         if len(A  ) != length:
                               return False
                   return True

             def _inputs_are_equal(A,							A  ):
                   if len(A  ) != len(A  ):
                         return False

                   for input_slice_a, input_slice_a in zip(A,							A  ):
                         if not np.allclose(np.asarray(A  ),							np.asarray(A  ),							atol=1E-3  ):
                               return False
                   return True

             SCREAMING_SNAKE_CASE   :      Dict    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.feat_extract_tester.prepare_inputs_for_common(numpify=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      int    					=						BatchFeature({input_name: speech_inputs}  )

             SCREAMING_SNAKE_CASE   :      int    					=						self.feat_extract_tester.seq_length_diff
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.feat_extract_tester.max_seq_length + pad_diff
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.feat_extract_tester.min_seq_length
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.feat_extract_tester.batch_size
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.feat_extract_tester.feature_size

             # test padding for List[int] + numpy
             SCREAMING_SNAKE_CASE   :      Tuple    					=						feat_extract.pad(A,							padding=A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Tuple    					=						feat_extract.pad(A,							padding='longest'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.pad(A,							padding='max_length',							max_length=len(speech_inputs[-1]  )  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.pad(A,							padding='longest',							return_tensors='np'  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						input_a[input_name]

             # max_length parameter has to be provided when setting `padding="max_length"`
             with self.assertRaises(A  ):
                   feat_extract.pad(A,							padding='max_length'  )[input_name]

             SCREAMING_SNAKE_CASE   :      List[str]    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=A,							return_tensors='np'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						input_a[input_name]

             self.assertFalse(_inputs_have_equal_length(A  )  )
             self.assertTrue(_inputs_have_equal_length(A  )  )
             self.assertTrue(_inputs_have_equal_length(A  )  )
             self.assertTrue(_inputs_are_equal(A,							A  )  )
             self.assertTrue(len(input_a[0]  ) == pad_min_length  )
             self.assertTrue(len(input_a[1]  ) == pad_min_length + pad_diff  )
             self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]  ))  )
             self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)  )

             if feature_size > 1:
                   self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size  )

             # test padding for `pad_to_multiple_of` for List[int] + numpy
             SCREAMING_SNAKE_CASE   :      Any    					=						feat_extract.pad(A,							pad_to_multiple_of=10  )
             SCREAMING_SNAKE_CASE   :      int    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						feat_extract.pad(A,							padding='longest',							pad_to_multiple_of=10  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						feat_extract.pad(
                 A,							padding='max_length',							pad_to_multiple_of=10,							max_length=A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      str    					=						feat_extract.pad(
                 A,							padding='max_length',							pad_to_multiple_of=10,							max_length=A,							return_tensors='np',							)
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						input_a[input_name]

             self.assertTrue(all(len(A  ) % 10 == 0 for x in input_a  )  )
             self.assertTrue(_inputs_are_equal(A,							A  )  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
             self.assertTrue(all(len(A  ) == expected_mult_pad_length for x in input_a  )  )
             self.assertEqual(input_a.shape[:2],							(batch_size, expected_mult_pad_length)  )

             if feature_size > 1:
                   self.assertTrue(input_a.shape[2] == feature_size  )

             # Check padding value is correct
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						(np.ones(self.feat_extract_tester.feature_size  ) * feat_extract.padding_value).sum()
             self.assertTrue(
                 abs(np.asarray(input_a[0]  )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)  )
                 < 1E-3  )
             self.assertTrue(
                 abs(
                     np.asarray(input_a[1]  )[pad_min_length + pad_diff :].sum()
                     - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)  )
                 < 1E-3  )
             self.assertTrue(
                 abs(
                     np.asarray(input_a[2]  )[pad_min_length + 2 * pad_diff :].sum()
                     - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)  )
                 < 1E-3  )
             self.assertTrue(
                 abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)  ) < 1E-3  )
             self.assertTrue(
                 abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)  )
                 < 1E-3  )







       def        UpperCamelCase_							(      self,							A=False  ):







             '''simple docstring'''







             def _inputs_have_equal_length(A  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						len(input[0]  )
                   for input_slice in input[1:]:
                         if len(A  ) != length:
                               return False
                   return True

             def _inputs_are_equal(A,							A  ):
                   if len(A  ) != len(A  ):
                         return False

                   for input_slice_a, input_slice_a in zip(A,							A  ):
                         if not np.allclose(np.asarray(A  ),							np.asarray(A  ),							atol=1E-3  ):
                               return False
                   return True

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.feat_extract_tester.prepare_inputs_for_common(numpify=A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						BatchFeature({input_name: speech_inputs}  )

             # truncate to smallest
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[0]  ),							truncation=A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feat_extract.pad(A,							padding='max_length',							max_length=len(speech_inputs[0]  )  )
             SCREAMING_SNAKE_CASE   :      int    					=						input_a[input_name]

             self.assertTrue(_inputs_have_equal_length(A  )  )
             self.assertFalse(_inputs_have_equal_length(A  )  )

             # truncate to smallest with np
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[0]  ),							return_tensors='np',							truncation=A,							)
             SCREAMING_SNAKE_CASE   :      Any    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      str    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[0]  ),							return_tensors='np'  )
             SCREAMING_SNAKE_CASE   :      str    					=						input_a[input_name]

             self.assertTrue(_inputs_have_equal_length(A  )  )
             self.assertTrue(input_a.shape[1] == len(speech_inputs[0]  )  )

             # since truncation forces padding to be smaller than longest input
             # function can't return `np.ndarray`, but has to return list
             self.assertFalse(_inputs_have_equal_length(A  )  )

             # truncate to middle
             SCREAMING_SNAKE_CASE   :      Any    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[1]  ),							truncation=A,							return_tensors='np',							)
             SCREAMING_SNAKE_CASE   :      Tuple    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Any    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[1]  ),							truncation=A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[1]  ),							return_tensors='np'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						input_a[input_name]

             self.assertTrue(input_a.shape[1] == len(speech_inputs[1]  )  )
             self.assertTrue(_inputs_have_equal_length(A  )  )
             self.assertTrue(_inputs_have_equal_length(A  )  )
             self.assertTrue(_inputs_are_equal(A,							A  )  )

             # since truncation forces padding to be smaller than longest input
             # function can't return `np.ndarray`, but has to return list
             self.assertFalse(_inputs_have_equal_length(A  )  )
             self.assertTrue(len(input_a[-1]  ) == len(speech_inputs[-1]  )  )

             # padding has to be max_length when setting `truncation=True`
             with self.assertRaises(A  ):
                   feat_extract.pad(A,							truncation=A  )[input_name]

             # padding has to be max_length when setting `truncation=True`
             with self.assertRaises(A  ):
                   feat_extract.pad(A,							padding='longest',							truncation=A  )[input_name]

             # padding has to be max_length when setting `truncation=True`
             with self.assertRaises(A  ):
                   feat_extract.pad(A,							padding='longest',							truncation=A  )[input_name]

             # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
             with self.assertRaises(A  ):
                   feat_extract.pad(A,							padding='max_length',							truncation=A  )[input_name]

             # test truncation for `pad_to_multiple_of` for List[int] + numpy
             SCREAMING_SNAKE_CASE   :      Tuple    					=						12
             SCREAMING_SNAKE_CASE   :      Tuple    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[0]  ),							pad_to_multiple_of=A,							truncation=A,							)
             SCREAMING_SNAKE_CASE   :      Tuple    					=						input_a[input_name]

             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=len(speech_inputs[0]  ),							pad_to_multiple_of=A,							)
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						input_a[input_name]

             # retrieve expected_length as multiple of pad_to_multiple_of
             SCREAMING_SNAKE_CASE   :      Dict    					=						len(speech_inputs[0]  )
             if expected_length % pad_to_multiple_of != 0:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						((len(speech_inputs[0]  ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

             self.assertTrue(len(input_a[0]  ) == expected_length  )
             self.assertTrue(_inputs_have_equal_length(A  )  )
             self.assertFalse(_inputs_have_equal_length(A  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self._check_padding(numpify=A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self._check_padding(numpify=A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self._check_truncation(numpify=A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self._check_truncation(numpify=A  )







       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.feat_extract_tester.prepare_inputs_for_common()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						BatchFeature({input_name: speech_inputs}  )

             SCREAMING_SNAKE_CASE   :      Any    					=						feat_extract.pad(A,							padding='longest',							return_tensors='np'  )[input_name]
             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.pad(A,							padding='longest',							return_tensors='pt'  )[input_name]

             self.assertTrue(abs(input_np.astype(np.floataa  ).sum() - input_pt.numpy().astype(np.floataa  ).sum()  ) < 1E-2  )







       @require_tf
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.feature_extraction_class(**self.feat_extract_dict  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.feat_extract_tester.prepare_inputs_for_common()
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						BatchFeature({input_name: speech_inputs}  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.pad(A,							padding='longest',							return_tensors='np'  )[input_name]
             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.pad(A,							padding='longest',							return_tensors='tf'  )[input_name]

             self.assertTrue(abs(input_np.astype(np.floataa  ).sum() - input_tf.numpy().astype(np.floataa  ).sum()  ) < 1E-2  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.feat_extract_dict
             SCREAMING_SNAKE_CASE   :      Dict    					=						True
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.feature_extraction_class(**A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.feat_extract_tester.prepare_inputs_for_common()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[len(A  ) for x in speech_inputs]
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						BatchFeature({input_name: speech_inputs}  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						feat_extract.pad(A,							padding='longest',							return_tensors='np'  )
             self.assertIn('attention_mask',							A  )
             self.assertListEqual(list(processed.attention_mask.shape  ),							list(processed[input_name].shape[:2]  )  )
             self.assertListEqual(processed.attention_mask.sum(-1  ).tolist(),							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.feat_extract_dict
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						True
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.feature_extraction_class(**A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.feat_extract_tester.prepare_inputs_for_common()
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[len(A  ) for x in speech_inputs]
             SCREAMING_SNAKE_CASE   :      int    					=						feat_extract.model_input_names[0]

             SCREAMING_SNAKE_CASE   :      List[str]    					=						BatchFeature({input_name: speech_inputs}  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						min(A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feat_extract.pad(
                 A,							padding='max_length',							max_length=A,							truncation=A,							return_tensors='np'  )
             self.assertIn('attention_mask',							A  )
             self.assertListEqual(
                 list(processed_pad.attention_mask.shape  ),							[processed_pad[input_name].shape[0], max_length]  )
             self.assertListEqual(
                 processed_pad.attention_mask[:, :max_length].sum(-1  ).tolist(),							[max_length for x in speech_inputs]  )








'''simple docstring'''



class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						val
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						None







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.val:
                   if val < self.val:
                         if self.left is None:
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(A  )
                         else:
                               self.left.insert(A  )
                   elif val > self.val:
                         if self.right is None:
                               SCREAMING_SNAKE_CASE   :      int    					=						Node(A  )
                         else:
                               self.right.insert(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						val




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if root:
            inorder(root.left      ,__UpperCamelCase    )
            res.append(root.val    )
            inorder(root.right      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      if len(__UpperCamelCase    ) == 0:
            return arr
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(arr[0]    )
      for i in range(1      ,len(__UpperCamelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      SCREAMING_SNAKE_CASE   :      Dict    					=						[]
      inorder(__UpperCamelCase      ,__UpperCamelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))


