

"""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	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[Any]							=					None
    UpperCamelCase_	:					Optional[Any]							=					None
    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         return self.feat_extract_tester.prepare_feat_extract_dict()
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         __snake_case				=							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  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

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

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

         __snake_case				=							processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              __snake_case				=							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  lowercase					(		self							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_    )
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							feat_extract.model_input_names[0]

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

         __snake_case				=							processed_features[input_name]

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

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

         __snake_case				=							processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              __snake_case				=							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  lowercase					(		self							:     int	,      A_							:     Union[str, Any]=False    )    ->       Optional[Any]:
         def _inputs_have_equal_length(A_							:     int    ):
              __snake_case				=							len(input[0]    )
              for input_slice in input[1:]:
                   if len(A_    ) != length:
                        return False
              return True

         def _inputs_are_equal(A_							:     int	,      A_							:     Optional[int]    ):
              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

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

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

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

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

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''    )
         __snake_case				=							input_a[input_name]

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

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''np'''    )
         __snake_case				=							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]

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

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

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

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

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

         __snake_case				=							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
         __snake_case				=							(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  lowercase					(		self							:     List[Any]	,      A_							:     Optional[int]=False    )    ->       Dict:
         def _inputs_have_equal_length(A_							:     Any    ):
              __snake_case				=							len(input[0]    )
              for input_slice in input[1:]:
                   if len(A_    ) != length:
                        return False
              return True

         def _inputs_are_equal(A_							:     str	,      A_							:     str    ):
              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

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

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

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

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

         self.assertTrue(_inputs_have_equal_length(A_    )    )
         self.assertFalse(_inputs_have_equal_length(A_    )    )

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

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )	,      return_tensors='''np'''    )
         __snake_case				=							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
         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[1]    )	,      truncation=A_	,      return_tensors='''np'''	,      )
         __snake_case				=							input_a[input_name]

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

         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[1]    )	,      return_tensors='''np'''    )
         __snake_case				=							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
         __snake_case				=							12
         __snake_case				=							feat_extract.pad(
             A_	,      padding='''max_length'''	,      max_length=len(speech_inputs[0]    )	,      pad_to_multiple_of=A_	,      truncation=A_	,      )
         __snake_case				=							input_a[input_name]

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

         # retrieve expected_length as multiple of pad_to_multiple_of
         __snake_case				=							len(speech_inputs[0]    )
         if expected_length % pad_to_multiple_of != 0:
              __snake_case				=							((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  lowercase					(		self							:     int    )    ->       str:
         self._check_padding(numpify=A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         self._check_padding(numpify=A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Dict:
         self._check_truncation(numpify=A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         self._check_truncation(numpify=A_    )
    @require_torch
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

         __snake_case				=							feat_extract.pad(A_	,      padding='''longest'''	,      return_tensors='''np'''    )[input_name]
         __snake_case				=							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  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							self.feature_extraction_class(**self.feat_extract_dict    )
         __snake_case				=							self.feat_extract_tester.prepare_inputs_for_common()
         __snake_case				=							feat_extract.model_input_names[0]

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

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

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )

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

         __snake_case				=							BatchFeature({input_name: speech_inputs}    )
         __snake_case				=							min(A_    )

         __snake_case				=							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"""



import re





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''',      str_)]





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							split_input(str_)
     return "".join(
         [''''''.join([char.capitalize() for char in sub_str]) for sub_str in string_split])





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     try:
          __snake_case				=							split_input(snake_case)
          if upper:
               __snake_case				=							''''''.join(
                   [
                       separator.join([char.upper() for char in sub_str])
                       for sub_str in string_split
                   ])
          else:
               __snake_case				=							''''''.join(
                   [
                       separator.join([char.lower() for char in sub_str])
                       for sub_str in string_split
                   ])
          return res_str
     except IndexError:
          return "not valid string"





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return to_simple_case(snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     try:
          __snake_case				=							to_simple_case(snake_case)
          return res_str[0].lower() + res_str[1:]
     except IndexError:
          return "not valid string"





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return to_complex_case(snake_case,      snake_case,      '''_''')





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return to_complex_case(snake_case,      snake_case,      '''-''')


if __name__ == "__main__":
       __import__("doctest").testmod()