


'''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_      (  __lowercase		):
     # to overwrite at feature extractactor specific tests
     lowerCamelCase				:	Tuple		=     None
     lowerCamelCase				:	Any		=     None





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        return self.feat_extract_tester.prepare_feat_extract_dict()





     def 							__UpperCAmelCase      ( self       :      int    )				->  int:
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_dict    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'feature_size'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'sampling_rate'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'padding_value'    )    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			self.feat_extract_tester.prepare_inputs_for_common()
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_dict    )
        lowerCAmelCase     =			feat_extract.model_input_names[0]

        lowerCAmelCase     =			BatchFeature({input_name: speech_inputs}    )

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

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

        lowerCAmelCase     =			processed_features[input_name]

        if len(batch_features_input.shape    ) < 3:
           lowerCAmelCase     =			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       :      Dict    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase__    )
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_dict    )
        lowerCAmelCase     =			feat_extract.model_input_names[0]

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

        lowerCAmelCase     =			processed_features[input_name]

        if len(batch_features_input.shape    ) < 3:
           lowerCAmelCase     =			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       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase__    )
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_dict    )
        lowerCAmelCase     =			feat_extract.model_input_names[0]

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

        lowerCAmelCase     =			processed_features[input_name]

        if len(batch_features_input.shape    ) < 3:
           lowerCAmelCase     =			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       :      List[str]    ,      UpperCAmelCase__       :      Union[str, Any]=False    )				->  Tuple:
        def _inputs_have_equal_length(UpperCAmelCase__       :      Optional[Any]    ):
           lowerCAmelCase     =			len(input[0]    )
           for input_slice in input[1:]:
              if len(UpperCAmelCase__    ) != length:
                 return False
           return True

        def _inputs_are_equal(UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ):
           if len(UpperCAmelCase__    ) != len(UpperCAmelCase__    ):
              return False

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

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

        lowerCAmelCase     =			BatchFeature({input_name: speech_inputs}    )

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

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

        lowerCAmelCase     =			feat_extract.pad(UpperCAmelCase__    ,      padding='longest'    )
        lowerCAmelCase     =			input_a[input_name]

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

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

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

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

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

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

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

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

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

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

        def _inputs_are_equal(UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Any    ):
           if len(UpperCAmelCase__    ) != len(UpperCAmelCase__    ):
              return False

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

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

        lowerCAmelCase     =			BatchFeature({input_name: speech_inputs}    )

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

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

        self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__    )    )
        self.assertFalse(_inputs_have_equal_length(UpperCAmelCase__    )    )

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

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

        self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__    )    )
        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(UpperCAmelCase__    )    )

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

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

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

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

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

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

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

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

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

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

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

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





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[str]:
        self._check_padding(numpify=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Any:
        self._check_padding(numpify=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        self._check_truncation(numpify=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Optional[int]:
        self._check_truncation(numpify=UpperCAmelCase__    )





     @require_torch
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  str:
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_dict    )
        lowerCAmelCase     =			self.feat_extract_tester.prepare_inputs_for_common()
        lowerCAmelCase     =			feat_extract.model_input_names[0]

        lowerCAmelCase     =			BatchFeature({input_name: speech_inputs}    )

        lowerCAmelCase     =			feat_extract.pad(UpperCAmelCase__    ,      padding='longest'    ,      return_tensors='np'    )[input_name]
        lowerCAmelCase     =			feat_extract.pad(UpperCAmelCase__    ,      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       :      Dict    )				->  List[Any]:
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_dict    )
        lowerCAmelCase     =			self.feat_extract_tester.prepare_inputs_for_common()
        lowerCAmelCase     =			feat_extract.model_input_names[0]

        lowerCAmelCase     =			BatchFeature({input_name: speech_inputs}    )

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

        lowerCAmelCase     =			BatchFeature({input_name: speech_inputs}    )

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





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.feat_extract_dict
        lowerCAmelCase     =			True
        lowerCAmelCase     =			self.feature_extraction_class(**UpperCAmelCase__    )
        lowerCAmelCase     =			self.feat_extract_tester.prepare_inputs_for_common()
        lowerCAmelCase     =			[len(UpperCAmelCase__    ) for x in speech_inputs]
        lowerCAmelCase     =			feat_extract.model_input_names[0]

        lowerCAmelCase     =			BatchFeature({input_name: speech_inputs}    )
        lowerCAmelCase     =			min(UpperCAmelCase__    )

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




print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))





