
"""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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      # to overwrite at feature extractactor specific tests
      __SCREAMING_SNAKE_CASE :				Any				   =						None
      __SCREAMING_SNAKE_CASE :				Any				   =						None




      @property
      def      a_   (				self				)    ->       str:
          return self.feat_extract_tester.prepare_feat_extract_dict()




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          self.assertTrue(hasattr(lowercase_ ,							'feature_size'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'sampling_rate'				)				)
          self.assertTrue(hasattr(lowercase_ ,							'padding_value'				)				)




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          UpperCAmelCase          =       feat_extract.model_input_names[0]

          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs}				)

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

          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_				)
          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs} ,							tensor_type='np'				)

          UpperCAmelCase          =       processed_features[input_name]

          if len(batch_features_input.shape				) < 3:
              UpperCAmelCase          =       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      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_				)
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          UpperCAmelCase          =       feat_extract.model_input_names[0]

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

          UpperCAmelCase          =       processed_features[input_name]

          if len(batch_features_input.shape				) < 3:
              UpperCAmelCase          =       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      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_				)
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          UpperCAmelCase          =       feat_extract.model_input_names[0]

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

          UpperCAmelCase          =       processed_features[input_name]

          if len(batch_features_input.shape				) < 3:
              UpperCAmelCase          =       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      a_   (				self ,							lowercase_=False				)    ->       Dict:
          def _inputs_have_equal_length(lowercase_				):
              UpperCAmelCase          =       len(input[0]				)
              for input_slice in input[1:]:
                  if len(lowercase_				) != length:
                      return False
              return True

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

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

          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_				)
          UpperCAmelCase          =       feat_extract.model_input_names[0]

          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs}				)

          UpperCAmelCase          =       self.feat_extract_tester.seq_length_diff
          UpperCAmelCase          =       self.feat_extract_tester.max_seq_length + pad_diff
          UpperCAmelCase          =       self.feat_extract_tester.min_seq_length
          UpperCAmelCase          =       self.feat_extract_tester.batch_size
          UpperCAmelCase          =       self.feat_extract_tester.feature_size

          # test padding for List[int] + numpy
          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding=lowercase_				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding='longest'				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[-1]				)				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding='longest' ,							return_tensors='np'				)
          UpperCAmelCase          =       input_a[input_name]

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

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=lowercase_ ,							return_tensors='np'				)
          UpperCAmelCase          =       input_a[input_name]

          self.assertFalse(_inputs_have_equal_length(lowercase_				)				)
          self.assertTrue(_inputs_have_equal_length(lowercase_				)				)
          self.assertTrue(_inputs_have_equal_length(lowercase_				)				)
          self.assertTrue(_inputs_are_equal(lowercase_ ,							lowercase_				)				)
          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
          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							pad_to_multiple_of=1_0				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding='longest' ,							pad_to_multiple_of=1_0				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							pad_to_multiple_of=1_0 ,							max_length=lowercase_				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							pad_to_multiple_of=1_0 ,							max_length=lowercase_ ,							return_tensors='np' ,							)
          UpperCAmelCase          =       input_a[input_name]

          self.assertTrue(all(len(lowercase_				) % 1_0 == 0 for x in input_a				)				)
          self.assertTrue(_inputs_are_equal(lowercase_ ,							lowercase_				)				)

          UpperCAmelCase          =       pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
          self.assertTrue(all(len(lowercase_				) == 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
          UpperCAmelCase          =       (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      a_   (				self ,							lowercase_=False				)    ->       str:
          def _inputs_have_equal_length(lowercase_				):
              UpperCAmelCase          =       len(input[0]				)
              for input_slice in input[1:]:
                  if len(lowercase_				) != length:
                      return False
              return True

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

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

          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_				)
          UpperCAmelCase          =       feat_extract.model_input_names[0]

          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs}				)

          # truncate to smallest
          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[0]				) ,							truncation=lowercase_				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[0]				)				)
          UpperCAmelCase          =       input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(lowercase_				)				)
          self.assertFalse(_inputs_have_equal_length(lowercase_				)				)

          # truncate to smallest with np
          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[0]				) ,							return_tensors='np' ,							truncation=lowercase_ ,							)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[0]				) ,							return_tensors='np'				)
          UpperCAmelCase          =       input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(lowercase_				)				)
          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(lowercase_				)				)

          # truncate to middle
          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[1]				) ,							truncation=lowercase_ ,							return_tensors='np' ,							)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[1]				) ,							truncation=lowercase_				)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[1]				) ,							return_tensors='np'				)
          UpperCAmelCase          =       input_a[input_name]

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

          # 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(lowercase_				)				)
          self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

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

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

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

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

          # test truncation for `pad_to_multiple_of` for List[int] + numpy
          UpperCAmelCase          =       1_2
          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[0]				) ,							pad_to_multiple_of=lowercase_ ,							truncation=lowercase_ ,							)
          UpperCAmelCase          =       input_a[input_name]

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=len(speech_inputs[0]				) ,							pad_to_multiple_of=lowercase_ ,							)
          UpperCAmelCase          =       input_a[input_name]

          # retrieve expected_length as multiple of pad_to_multiple_of
          UpperCAmelCase          =       len(speech_inputs[0]				)
          if expected_length % pad_to_multiple_of != 0:
              UpperCAmelCase          =       ((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(lowercase_				)				)
          self.assertFalse(_inputs_have_equal_length(lowercase_				)				)




      def      a_   (				self				)    ->       Optional[int]:
          self._check_padding(numpify=lowercase_				)




      def      a_   (				self				)    ->       Optional[int]:
          self._check_padding(numpify=lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          self._check_truncation(numpify=lowercase_				)




      def      a_   (				self				)    ->       Optional[int]:
          self._check_truncation(numpify=lowercase_				)




      @require_torch
      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase          =       feat_extract.model_input_names[0]

          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs}				)

          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding='longest' ,							return_tensors='np'				)[input_name]
          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							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      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.feature_extraction_class(**self.feat_extract_dict				)
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase          =       feat_extract.model_input_names[0]

          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs}				)

          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							padding='longest' ,							return_tensors='np'				)[input_name]
          UpperCAmelCase          =       feat_extract.pad(lowercase_ ,							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      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       self.feat_extract_dict
          UpperCAmelCase          =       True
          UpperCAmelCase          =       self.feature_extraction_class(**lowercase_				)
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase          =       [len(lowercase_				) for x in speech_inputs]
          UpperCAmelCase          =       feat_extract.model_input_names[0]

          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs}				)

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




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.feat_extract_dict
          UpperCAmelCase          =       True
          UpperCAmelCase          =       self.feature_extraction_class(**lowercase_				)
          UpperCAmelCase          =       self.feat_extract_tester.prepare_inputs_for_common()
          UpperCAmelCase          =       [len(lowercase_				) for x in speech_inputs]
          UpperCAmelCase          =       feat_extract.model_input_names[0]

          UpperCAmelCase          =       BatchFeature({input_name: speech_inputs}				)
          UpperCAmelCase          =       min(lowercase_				)

          UpperCAmelCase          =       feat_extract.pad(
              lowercase_ ,							padding='max_length' ,							max_length=lowercase_ ,							truncation=lowercase_ ,							return_tensors='np'				)
          self.assertIn('attention_mask' ,							lowercase_				)
          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"""

from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



