"""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        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				None
					snake_case__       					=				None







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									return self.feat_extract_tester.prepare_feat_extract_dict()







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			Dict    ):
									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(lowerCamelCase__    ) == len(lowerCamelCase__    ) for x, y in zip(lowerCamelCase__					,processed_features[input_name]    )    )    )

									UpperCAmelCase__    =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase__    )
									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 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase__    )
									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 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase__    )
									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 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Union[str, Any]=False    ):
									def _inputs_have_equal_length(lowerCamelCase__     :			Tuple    ):
													UpperCAmelCase__    =							len(input[0]    )
													for input_slice in input[1:]:
																	if len(lowerCamelCase__    ) != length:
																					return False
													return True

									def _inputs_are_equal(lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Tuple    ):
													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

									UpperCAmelCase__    =							self.feature_extraction_class(**self.feat_extract_dict    )
									UpperCAmelCase__    =							self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase__    )
									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(lowerCamelCase__					,padding=lowerCamelCase__    )
									UpperCAmelCase__    =							input_a[input_name]

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

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

									UpperCAmelCase__    =							feat_extract.pad(lowerCamelCase__					,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(lowerCamelCase__    ):
													feat_extract.pad(lowerCamelCase__					,padding='max_length'    )[input_name]

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

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

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

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

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

									UpperCAmelCase__    =							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
									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 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			Dict=False    ):
									def _inputs_have_equal_length(lowerCamelCase__     :			Optional[int]    ):
													UpperCAmelCase__    =							len(input[0]    )
													for input_slice in input[1:]:
																	if len(lowerCamelCase__    ) != length:
																					return False
													return True

									def _inputs_are_equal(lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Tuple    ):
													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

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

									UpperCAmelCase__    =							BatchFeature({input_name: speech_inputs}    )

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

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

									self.assertTrue(_inputs_have_equal_length(lowerCamelCase__    )    )
									self.assertFalse(_inputs_have_equal_length(lowerCamelCase__    )    )

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

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

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

									UpperCAmelCase__    =							feat_extract.pad(
									    lowerCamelCase__					,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(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
									UpperCAmelCase__    =							12
									UpperCAmelCase__    =							feat_extract.pad(
									    lowerCamelCase__					,padding='max_length'					,max_length=len(speech_inputs[0]    )					,pad_to_multiple_of=lowerCamelCase__					,truncation=lowerCamelCase__					,)
									UpperCAmelCase__    =							input_a[input_name]

									UpperCAmelCase__    =							feat_extract.pad(
									    lowerCamelCase__					,padding='max_length'					,max_length=len(speech_inputs[0]    )					,pad_to_multiple_of=lowerCamelCase__					,)
									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(lowerCamelCase__    )    )
									self.assertFalse(_inputs_have_equal_length(lowerCamelCase__    )    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									self._check_padding(numpify=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									self._check_padding(numpify=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									self._check_truncation(numpify=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									self._check_truncation(numpify=lowerCamelCase__    )







					@require_torch
					def 	__lowerCAmelCase		(       self     :			List[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(lowerCamelCase__					,padding='longest'					,return_tensors='np'    )[input_name]
									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			List[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(lowerCamelCase__					,padding='longest'					,return_tensors='np'    )[input_name]
									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.feat_extract_dict
									UpperCAmelCase__    =							True
									UpperCAmelCase__    =							self.feature_extraction_class(**lowerCamelCase__    )
									UpperCAmelCase__    =							self.feat_extract_tester.prepare_inputs_for_common()
									UpperCAmelCase__    =							[len(lowerCamelCase__    ) for x in speech_inputs]
									UpperCAmelCase__    =							feat_extract.model_input_names[0]

									UpperCAmelCase__    =							BatchFeature({input_name: speech_inputs}    )

									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							self.feat_extract_dict
									UpperCAmelCase__    =							True
									UpperCAmelCase__    =							self.feature_extraction_class(**lowerCamelCase__    )
									UpperCAmelCase__    =							self.feat_extract_tester.prepare_inputs_for_common()
									UpperCAmelCase__    =							[len(lowerCamelCase__    ) for x in speech_inputs]
									UpperCAmelCase__    =							feat_extract.model_input_names[0]

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

									UpperCAmelCase__    =							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"""






import re



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



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							split_input(str_ )
				return "".join(
				    [''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				try:
								UpperCAmelCase__    =							split_input(lowerCamelCase )
								if upper:
												UpperCAmelCase__    =							''.join(
												    [
												        separator.join([char.upper() for char in sub_str] )
												        for sub_str in string_split
												    ] )
								else:
												UpperCAmelCase__    =							''.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 						a_		(      lowerCamelCase ):
				return to_simple_case(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				try:
								UpperCAmelCase__    =							to_simple_case(lowerCamelCase )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return to_complex_case(lowerCamelCase      ,    lowerCamelCase      ,    '_' )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return to_complex_case(lowerCamelCase      ,    lowerCamelCase      ,    '-' )


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



