
"""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(       lowerCamelCase      ):
		# to overwrite at feature extractactor specific tests
		lowercase__         = None
		lowercase__         = None



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






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



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_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(__a) == len(__a) for x, y in zip(__a							,						processed_features[input_name])))

							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a)
							_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 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a)
							_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 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a)
							_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 		UpperCAmelCase				(   self							,						__a=False)			->			Union[str, Any]:






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

							def _inputs_are_equal(__a							,						__a):
												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

							_UpperCamelCase									=    self.feature_extraction_class(**self.feat_extract_dict)
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common(numpify=__a)
							_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(__a							,						padding=__a)
							_UpperCamelCase									=    input_a[input_name]

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

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

							_UpperCamelCase									=    feat_extract.pad(__a							,						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(__a):
												feat_extract.pad(__a							,						padding='''max_length''')[input_name]

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

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

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

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

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

							_UpperCamelCase									=    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
							_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 		UpperCAmelCase				(   self							,						__a=False)			->			List[Any]:






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

							def _inputs_are_equal(__a							,						__a):
												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

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

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

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

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

							self.assertTrue(_inputs_have_equal_length(__a))
							self.assertFalse(_inputs_have_equal_length(__a))

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

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

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

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

							_UpperCamelCase									=    feat_extract.pad(
							    __a							,						padding='''max_length'''							,						max_length=len(speech_inputs[0])							,						pad_to_multiple_of=__a							,						)
							_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(__a))
							self.assertFalse(_inputs_have_equal_length(__a))



		def 		UpperCAmelCase				(   self)			->			str:






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



		def 		UpperCAmelCase				(   self)			->			List[str]:






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



		def 		UpperCAmelCase				(   self)			->			str:






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



		def 		UpperCAmelCase				(   self)			->			Any:






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



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






							'''simple docstring'''
							_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(__a							,						padding='''longest'''							,						return_tensors='''np''')[input_name]
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_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(__a							,						padding='''longest'''							,						return_tensors='''np''')[input_name]
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Any:






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

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			List[str]:






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

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

							_UpperCamelCase									=    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"""







from math import ceil, sqrt

def     UpperCAmelCase_					(				__a   :			int = 1_00_00_00						):




			'''simple docstring'''



			_lowerCamelCase					:					Tuple	       =    0

			for outer_width in range(3      ,							(limit // 4) + 2						):
						if outer_width**2 > limit:
									_lowerCamelCase					:					Optional[int]	       =    max(ceil(sqrt(outer_width**2 - limit						)						)      ,							1						)
						else:
									_lowerCamelCase					:					Any	       =    1
						if (outer_width - hole_width_lower_bound) % 2:
									hole_width_lower_bound += 1

						answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

			return answer


if __name__ == "__main__":
	print(F"{solution() = }")





