


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_	(							_a):
       '''simple docstring'''
       # to overwrite at feature extractactor specific tests
       __UpperCamelCase      :     Union[str, Any]							=					None
       __UpperCamelCase      :     List[str]							=					None






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.feat_extract_tester.prepare_feat_extract_dict()






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''feature_size'''						)						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''sampling_rate'''						)						)
              self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'''padding_value'''						)						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.feat_extract_tester.prepare_inputs_for_common()
              UpperCamelCase    :	Any   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              UpperCamelCase    :	Dict   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	Union[str, Any]   	=		BatchFeature({input_name: speech_inputs}						)

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

              UpperCamelCase    :	Union[str, Any]   	=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		BatchFeature({input_name: speech_inputs} ,					tensor_type='''np'''						)

              UpperCamelCase    :	Optional[int]   	=		processed_features[input_name]

              if len(batch_features_input.shape						) < 3:
                     UpperCamelCase    :	List[Any]   	=		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						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              UpperCamelCase    :	str   	=		feat_extract.model_input_names[0]

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

              UpperCamelCase    :	Union[str, Any]   	=		processed_features[input_name]

              if len(batch_features_input.shape						) < 3:
                     UpperCamelCase    :	Tuple   	=		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						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              UpperCamelCase    :	Tuple   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	List[Any]   	=		BatchFeature({input_name: speech_inputs} ,					tensor_type='''tf'''						)

              UpperCamelCase    :	Any   	=		processed_features[input_name]

              if len(batch_features_input.shape						) < 3:
                     UpperCamelCase    :	Union[str, Any]   	=		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 ,					__SCREAMING_SNAKE_CASE=False						):




              """simple docstring"""




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

              def _inputs_are_equal(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     if len(__SCREAMING_SNAKE_CASE						) != len(__SCREAMING_SNAKE_CASE						):
                            return False

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

              UpperCamelCase    :	Dict   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              UpperCamelCase    :	Any   	=		self.feat_extract_tester.prepare_inputs_for_common(numpify=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	List[Any]   	=		BatchFeature({input_name: speech_inputs}						)

              UpperCamelCase    :	Union[str, Any]   	=		self.feat_extract_tester.seq_length_diff
              UpperCamelCase    :	str   	=		self.feat_extract_tester.max_seq_length + pad_diff
              UpperCamelCase    :	Any   	=		self.feat_extract_tester.min_seq_length
              UpperCamelCase    :	Optional[Any]   	=		self.feat_extract_tester.batch_size
              UpperCamelCase    :	List[str]   	=		self.feat_extract_tester.feature_size

              # test padding for List[int] + numpy
              UpperCamelCase    :	str   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Dict   	=		input_a[input_name]

              UpperCamelCase    :	Union[str, Any]   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding='''longest'''						)
              UpperCamelCase    :	Tuple   	=		input_a[input_name]

              UpperCamelCase    :	Tuple   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[-1]						)						)
              UpperCamelCase    :	str   	=		input_a[input_name]

              UpperCamelCase    :	List[str]   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding='''longest''' ,					return_tensors='''np'''						)
              UpperCamelCase    :	Union[str, Any]   	=		input_a[input_name]

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

              UpperCamelCase    :	int   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=__SCREAMING_SNAKE_CASE ,					return_tensors='''np'''						)
              UpperCamelCase    :	Any   	=		input_a[input_name]

              self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE						)						)
              self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE						)						)
              self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE						)						)
              self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)
              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    :	str   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=10						)
              UpperCamelCase    :	List[str]   	=		input_a[input_name]

              UpperCamelCase    :	int   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding='''longest''' ,					pad_to_multiple_of=10						)
              UpperCamelCase    :	Union[str, Any]   	=		input_a[input_name]

              UpperCamelCase    :	Optional[int]   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					pad_to_multiple_of=10 ,					max_length=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		input_a[input_name]

              UpperCamelCase    :	Union[str, Any]   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					pad_to_multiple_of=10 ,					max_length=__SCREAMING_SNAKE_CASE ,					return_tensors='''np''' ,					)
              UpperCamelCase    :	Tuple   	=		input_a[input_name]

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

              UpperCamelCase    :	str   	=		pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
              self.assertTrue(all(len(__SCREAMING_SNAKE_CASE						) == 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    :	List[Any]   	=		(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 ,					__SCREAMING_SNAKE_CASE=False						):




              """simple docstring"""




              def _inputs_have_equal_length(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Optional[int]   	=		len(input[0]						)
                     for input_slice in input[1:]:
                            if len(__SCREAMING_SNAKE_CASE						) != length:
                                   return False
                     return True

              def _inputs_are_equal(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     if len(__SCREAMING_SNAKE_CASE						) != len(__SCREAMING_SNAKE_CASE						):
                            return False

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

              UpperCamelCase    :	Optional[int]   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              UpperCamelCase    :	List[Any]   	=		self.feat_extract_tester.prepare_inputs_for_common(numpify=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Tuple   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	str   	=		BatchFeature({input_name: speech_inputs}						)

              # truncate to smallest
              UpperCamelCase    :	Tuple   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[0]						) ,					truncation=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		input_a[input_name]

              UpperCamelCase    :	Dict   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[0]						)						)
              UpperCamelCase    :	Optional[int]   	=		input_a[input_name]

              self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE						)						)
              self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE						)						)

              # truncate to smallest with np
              UpperCamelCase    :	List[str]   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[0]						) ,					return_tensors='''np''' ,					truncation=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[Any]   	=		input_a[input_name]

              UpperCamelCase    :	str   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[0]						) ,					return_tensors='''np'''						)
              UpperCamelCase    :	Union[str, Any]   	=		input_a[input_name]

              self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE						)						)
              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(__SCREAMING_SNAKE_CASE						)						)

              # truncate to middle
              UpperCamelCase    :	str   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[1]						) ,					truncation=__SCREAMING_SNAKE_CASE ,					return_tensors='''np''' ,					)
              UpperCamelCase    :	Optional[int]   	=		input_a[input_name]

              UpperCamelCase    :	Optional[int]   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[1]						) ,					truncation=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		input_a[input_name]

              UpperCamelCase    :	Any   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[1]						) ,					return_tensors='''np'''						)
              UpperCamelCase    :	Optional[int]   	=		input_a[input_name]

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

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

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

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

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

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

              # test truncation for `pad_to_multiple_of` for List[int] + numpy
              UpperCamelCase    :	List[str]   	=		12
              UpperCamelCase    :	str   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[0]						) ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					truncation=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	str   	=		input_a[input_name]

              UpperCamelCase    :	Tuple   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=len(speech_inputs[0]						) ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	int   	=		input_a[input_name]

              # retrieve expected_length as multiple of pad_to_multiple_of
              UpperCamelCase    :	Tuple   	=		len(speech_inputs[0]						)
              if expected_length % pad_to_multiple_of != 0:
                     UpperCamelCase    :	Union[str, Any]   	=		((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(__SCREAMING_SNAKE_CASE						)						)
              self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self._check_padding(numpify=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self._check_padding(numpify=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self._check_truncation(numpify=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self._check_truncation(numpify=__SCREAMING_SNAKE_CASE						)






       @require_torch
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              UpperCamelCase    :	Union[str, Any]   	=		self.feat_extract_tester.prepare_inputs_for_common()
              UpperCamelCase    :	Tuple   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	Dict   	=		BatchFeature({input_name: speech_inputs}						)

              UpperCamelCase    :	Any   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding='''longest''' ,					return_tensors='''np'''						)[input_name]
              UpperCamelCase    :	Any   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					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						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		self.feature_extraction_class(**self.feat_extract_dict						)
              UpperCamelCase    :	int   	=		self.feat_extract_tester.prepare_inputs_for_common()
              UpperCamelCase    :	List[Any]   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	Tuple   	=		BatchFeature({input_name: speech_inputs}						)

              UpperCamelCase    :	Any   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					padding='''longest''' ,					return_tensors='''np'''						)[input_name]
              UpperCamelCase    :	int   	=		feat_extract.pad(__SCREAMING_SNAKE_CASE ,					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						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		self.feat_extract_dict
              UpperCamelCase    :	Dict   	=		True
              UpperCamelCase    :	Union[str, Any]   	=		self.feature_extraction_class(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		self.feat_extract_tester.prepare_inputs_for_common()
              UpperCamelCase    :	str   	=		[len(__SCREAMING_SNAKE_CASE						) for x in speech_inputs]
              UpperCamelCase    :	Dict   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	Optional[Any]   	=		BatchFeature({input_name: speech_inputs}						)

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.feat_extract_dict
              UpperCamelCase    :	Optional[Any]   	=		True
              UpperCamelCase    :	Tuple   	=		self.feature_extraction_class(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Any   	=		self.feat_extract_tester.prepare_inputs_for_common()
              UpperCamelCase    :	List[Any]   	=		[len(__SCREAMING_SNAKE_CASE						) for x in speech_inputs]
              UpperCamelCase    :	Tuple   	=		feat_extract.model_input_names[0]

              UpperCamelCase    :	int   	=		BatchFeature({input_name: speech_inputs}						)
              UpperCamelCase    :	Dict   	=		min(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		feat_extract.pad(
                  __SCREAMING_SNAKE_CASE ,					padding='''max_length''' ,					max_length=__SCREAMING_SNAKE_CASE ,					truncation=__SCREAMING_SNAKE_CASE ,					return_tensors='''np'''						)
              self.assertIn('''attention_mask''' ,					__SCREAMING_SNAKE_CASE						)
              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]						)





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       UpperCamelCase    :	List[Any]   	=		zeros((dst_height * dst_width, k_size * k_size)       )
       UpperCamelCase    :	Tuple   	=		0
       for i, j in product(range(SCREAMING_SNAKE_CASE_       )							,			range(SCREAMING_SNAKE_CASE_       )       ):
              UpperCamelCase    :	Dict   	=		ravel(image[i : i + k_size, j : j + k_size]       )
              UpperCamelCase    :	Dict   	=		window
              row += 1

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
  # turn image in gray scale value
  __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[Any] 			=				gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow("gaussian filter with 3x3 mask", gaussianaxa)
  imshow("gaussian filter with 5x5 mask", gaussianaxa)
  waitKey()

