




'''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 							__SCREAMING_SNAKE_CASE       (							A_     ):
      # to overwrite at feature extractactor specific tests
      __a  					=None
      __a  					=None




      @property
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         return self.feat_extract_tester.prepare_feat_extract_dict()




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.feature_extraction_class(**self.feat_extract_dict      )
         self.assertTrue(hasattr(lowerCamelCase ,   'feature_size'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'sampling_rate'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'padding_value'      )      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.feat_extract_tester.prepare_inputs_for_common()
         __a				     =    self.feature_extraction_class(**self.feat_extract_dict      )
         __a				     =    feat_extract.model_input_names[0]

         __a				     =    BatchFeature({input_name: speech_inputs}      )

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

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

         __a				     =    processed_features[input_name]

         if len(batch_features_input.shape      ) < 3:
            __a				     =    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      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
         __a				     =    self.feature_extraction_class(**self.feat_extract_dict      )
         __a				     =    feat_extract.model_input_names[0]

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

         __a				     =    processed_features[input_name]

         if len(batch_features_input.shape      ) < 3:
            __a				     =    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      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
         __a				     =    self.feature_extraction_class(**self.feat_extract_dict      )
         __a				     =    feat_extract.model_input_names[0]

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

         __a				     =    processed_features[input_name]

         if len(batch_features_input.shape      ) < 3:
            __a				     =    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 ,   lowerCamelCase=False      )							->Dict:



         '''simple docstring'''
         def _inputs_have_equal_length(lowerCamelCase      ):
            __a				     =    len(input[0]      )
            for input_slice in input[1:]:
               if len(lowerCamelCase      ) != length:
                  return False
            return True

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

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

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

         __a				     =    BatchFeature({input_name: speech_inputs}      )

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

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

         __a				     =    feat_extract.pad(lowerCamelCase ,   padding='longest'      )
         __a				     =    input_a[input_name]

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

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

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

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

         self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
         self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
         self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
         self.assertTrue(_inputs_are_equal(lowerCamelCase ,   lowerCamelCase      )      )
         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
         __a				     =    feat_extract.pad(lowerCamelCase ,   pad_to_multiple_of=10      )
         __a				     =    input_a[input_name]

         __a				     =    feat_extract.pad(lowerCamelCase ,   padding='longest' ,   pad_to_multiple_of=10      )
         __a				     =    input_a[input_name]

         __a				     =    feat_extract.pad(
             lowerCamelCase ,   padding='max_length' ,   pad_to_multiple_of=10 ,   max_length=lowerCamelCase      )
         __a				     =    input_a[input_name]

         __a				     =    feat_extract.pad(
             lowerCamelCase ,   padding='max_length' ,   pad_to_multiple_of=10 ,   max_length=lowerCamelCase ,   return_tensors='np' ,   )
         __a				     =    input_a[input_name]

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

         __a				     =    pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(lowerCamelCase      ) == 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
         __a				     =    (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 ,   lowerCamelCase=False      )							->Any:



         '''simple docstring'''
         def _inputs_have_equal_length(lowerCamelCase      ):
            __a				     =    len(input[0]      )
            for input_slice in input[1:]:
               if len(lowerCamelCase      ) != length:
                  return False
            return True

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

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

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

         __a				     =    BatchFeature({input_name: speech_inputs}      )

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

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

         self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
         self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

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

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

         self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
         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(lowerCamelCase      )      )

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

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

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

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

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

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

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

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

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

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

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

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




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         self._check_padding(numpify=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         self._check_padding(numpify=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         self._check_truncation(numpify=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         self._check_truncation(numpify=lowerCamelCase      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    self.feature_extraction_class(**self.feat_extract_dict      )
         __a				     =    self.feat_extract_tester.prepare_inputs_for_common()
         __a				     =    feat_extract.model_input_names[0]

         __a				     =    BatchFeature({input_name: speech_inputs}      )

         __a				     =    feat_extract.pad(lowerCamelCase ,   padding='longest' ,   return_tensors='np'      )[input_name]
         __a				     =    feat_extract.pad(lowerCamelCase ,   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      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.feature_extraction_class(**self.feat_extract_dict      )
         __a				     =    self.feat_extract_tester.prepare_inputs_for_common()
         __a				     =    feat_extract.model_input_names[0]

         __a				     =    BatchFeature({input_name: speech_inputs}      )

         __a				     =    feat_extract.pad(lowerCamelCase ,   padding='longest' ,   return_tensors='np'      )[input_name]
         __a				     =    feat_extract.pad(lowerCamelCase ,   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      )							->int:



         '''simple docstring'''
         __a				     =    self.feat_extract_dict
         __a				     =    True
         __a				     =    self.feature_extraction_class(**lowerCamelCase      )
         __a				     =    self.feat_extract_tester.prepare_inputs_for_common()
         __a				     =    [len(lowerCamelCase      ) for x in speech_inputs]
         __a				     =    feat_extract.model_input_names[0]

         __a				     =    BatchFeature({input_name: speech_inputs}      )

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






      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    self.feat_extract_dict
         __a				     =    True
         __a				     =    self.feature_extraction_class(**lowerCamelCase      )
         __a				     =    self.feat_extract_tester.prepare_inputs_for_common()
         __a				     =    [len(lowerCamelCase      ) for x in speech_inputs]
         __a				     =    feat_extract.model_input_names[0]

         __a				     =    BatchFeature({input_name: speech_inputs}      )
         __a				     =    min(lowerCamelCase      )

         __a				     =    feat_extract.pad(
             lowerCamelCase ,   padding='max_length' ,   max_length=lowerCamelCase ,   truncation=lowerCamelCase ,   return_tensors='np'      )
         self.assertIn('attention_mask' ,   lowerCamelCase      )
         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'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			bool:

   """simple docstring"""
   return number & 1 == 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()