




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  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None






						@property
						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							return self.feat_extract_tester.prepare_feat_extract_dict()






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	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 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							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(__lowercase		) == len(__lowercase		) for x, y in zip(__lowercase			,      processed_features[input_name]		)		)		)

							snake_case_						       =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase		)
							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 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase		)
							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 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase		)
							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 			snake_case__		(		self				:	str			,      __lowercase				:	Dict=False		):






							"""simple docstring"""






							def _inputs_have_equal_length(__lowercase				:	Dict		):
								snake_case_						       =	len(input[0]		)
								for input_slice in input[1:]:
									if len(__lowercase		) != length:
										return False
								return True

							def _inputs_are_equal(__lowercase				:	Dict			,      __lowercase				:	Optional[int]		):
								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

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

							snake_case_						       =	feat_extract.pad(__lowercase			,      padding="longest"		)
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(__lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[-1]		)		)
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(__lowercase			,      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(__lowercase		):
								feat_extract.pad(__lowercase			,      padding="max_length"		)[input_name]

							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      max_length=__lowercase			,      return_tensors="np"		)
							snake_case_						       =	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
							snake_case_						       =	feat_extract.pad(__lowercase			,      pad_to_multiple_of=10		)
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(__lowercase			,      padding="longest"			,      pad_to_multiple_of=10		)
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      pad_to_multiple_of=10			,      max_length=__lowercase		)
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      pad_to_multiple_of=10			,      max_length=__lowercase			,      return_tensors="np"			,      )
							snake_case_						       =	input_a[input_name]

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

							snake_case_						       =	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							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
							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 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	Union[str, Any]=False		):






							"""simple docstring"""






							def _inputs_have_equal_length(__lowercase				:	Union[str, Any]		):
								snake_case_						       =	len(input[0]		)
								for input_slice in input[1:]:
									if len(__lowercase		) != length:
										return False
								return True

							def _inputs_are_equal(__lowercase				:	str			,      __lowercase				:	str		):
								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

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

							snake_case_						       =	BatchFeature({input_name: speech_inputs}		)

							# truncate to smallest
							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[0]		)			,      truncation=__lowercase		)
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(__lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[0]		)		)
							snake_case_						       =	input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(__lowercase		)		)
							self.assertFalse(_inputs_have_equal_length(__lowercase		)		)

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

							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[0]		)			,      return_tensors="np"		)
							snake_case_						       =	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
							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[1]		)			,      truncation=__lowercase			,      return_tensors="np"			,      )
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[1]		)			,      truncation=__lowercase		)
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      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(__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
							snake_case_						       =	12
							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[0]		)			,      pad_to_multiple_of=__lowercase			,      truncation=__lowercase			,      )
							snake_case_						       =	input_a[input_name]

							snake_case_						       =	feat_extract.pad(
							    __lowercase			,      padding="max_length"			,      max_length=len(speech_inputs[0]		)			,      pad_to_multiple_of=__lowercase			,      )
							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(__lowercase		)		)
							self.assertFalse(_inputs_have_equal_length(__lowercase		)		)






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							self._check_padding(numpify=__lowercase		)






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							self._check_padding(numpify=__lowercase		)






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							self._check_truncation(numpify=__lowercase		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self._check_truncation(numpify=__lowercase		)






						@require_torch
						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							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(__lowercase			,      padding="longest"			,      return_tensors="np"		)[input_name]
							snake_case_						       =	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 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							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(__lowercase			,      padding="longest"			,      return_tensors="np"		)[input_name]
							snake_case_						       =	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 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.feat_extract_dict
							snake_case_						       =	True
							snake_case_						       =	self.feature_extraction_class(**__lowercase		)
							snake_case_						       =	self.feat_extract_tester.prepare_inputs_for_common()
							snake_case_						       =	[len(__lowercase		) 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(__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 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.feat_extract_dict
							snake_case_						       =	True
							snake_case_						       =	self.feature_extraction_class(**__lowercase		)
							snake_case_						       =	self.feat_extract_tester.prepare_inputs_for_common()
							snake_case_						       =	[len(__lowercase		) for x in speech_inputs]
							snake_case_						       =	feat_extract.model_input_names[0]

							snake_case_						       =	BatchFeature({input_name: speech_inputs}		)
							snake_case_						       =	min(__lowercase		)

							snake_case_						       =	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]		)






def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(a - b				) for a, b in zip(_A	,      _A				)				)				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if point:
		if isinstance(_A	,      _A				):
			for item in point:
				if not isinstance(_A	,      (int, float)				):
					snake_case_						       =	(
					    "Expected a list of numbers as input, found "
					    f"{type(_A				).__name__}"
					)
					raise TypeError(_A				)
		else:
			snake_case_						       =	f"Expected a list of numbers as input, found {type(_A				).__name__}"
			raise TypeError(_A				)
	else:
		raise ValueError("Missing an input"				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(x - y				) for x, y in zip(_A	,      _A				)				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
