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        lowercase_   (					__snake_case		):
							# to overwrite at feature extractactor specific tests
							_lowerCamelCase						=		None
							_lowerCamelCase						=		None



							@property
							def 	UpperCamelCase	(						self    ):
													return self.feat_extract_tester.prepare_feat_extract_dict()



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

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

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

													_snake_case						:					Optional[Any]    =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_    )
													_snake_case						:					Optional[int]    =   BatchFeature({input_name: speech_inputs}				,    tensor_type="np"    )

													_snake_case						:					Optional[int]    =   processed_features[input_name]

													if len(batch_features_input.shape    ) < 3:
																			_snake_case						:					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_torch
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_    )
													_snake_case						:					Any    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					Optional[int]    =   feat_extract.model_input_names[0]

													_snake_case						:					Dict    =   BatchFeature({input_name: speech_inputs}				,    tensor_type="pt"    )

													_snake_case						:					Dict    =   processed_features[input_name]

													if len(batch_features_input.shape    ) < 3:
																			_snake_case						:					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_tf
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowercase_    )
													_snake_case						:					List[Any]    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					Any    =   feat_extract.model_input_names[0]

													_snake_case						:					str    =   BatchFeature({input_name: speech_inputs}				,    tensor_type="tf"    )

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

													if len(batch_features_input.shape    ) < 3:
																			_snake_case						:					str    =   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				,    lowercase_=False    ):
													def _inputs_have_equal_length(lowercase_    ):
																			_snake_case						:					List[str]    =   len(input[0]    )
																			for input_slice in input[1:]:
																									if len(lowercase_    ) != length:
																															return False
																			return True

													def _inputs_are_equal(lowercase_				,    lowercase_    ):
																			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						:					Any    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					Dict    =   self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_    )
													_snake_case						:					Tuple    =   feat_extract.model_input_names[0]

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

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

													# test padding for List[int] + numpy
													_snake_case						:					Optional[Any]    =   feat_extract.pad(lowercase_				,    padding=lowercase_    )
													_snake_case						:					Union[str, Any]    =   input_a[input_name]

													_snake_case						:					int    =   feat_extract.pad(lowercase_				,    padding="longest"    )
													_snake_case						:					Any    =   input_a[input_name]

													_snake_case						:					int    =   feat_extract.pad(lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[-1]    )    )
													_snake_case						:					List[str]    =   input_a[input_name]

													_snake_case						:					Dict    =   feat_extract.pad(lowercase_				,    padding="longest"				,    return_tensors="np"    )
													_snake_case						:					Optional[Any]    =   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						:					Union[str, Any]    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=lowercase_				,    return_tensors="np"    )
													_snake_case						:					List[Any]    =   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						:					Tuple    =   feat_extract.pad(lowercase_				,    pad_to_multiple_of=10    )
													_snake_case						:					Union[str, Any]    =   input_a[input_name]

													_snake_case						:					str    =   feat_extract.pad(lowercase_				,    padding="longest"				,    pad_to_multiple_of=10    )
													_snake_case						:					str    =   input_a[input_name]

													_snake_case						:					Optional[Any]    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    pad_to_multiple_of=10				,    max_length=lowercase_    )
													_snake_case						:					List[Any]    =   input_a[input_name]

													_snake_case						:					int    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    pad_to_multiple_of=10				,    max_length=lowercase_				,    return_tensors="np"				,    )
													_snake_case						:					List[str]    =   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						:					List[Any]    =   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						:					List[str]    =   (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				,    lowercase_=False    ):
													def _inputs_have_equal_length(lowercase_    ):
																			_snake_case						:					Optional[int]    =   len(input[0]    )
																			for input_slice in input[1:]:
																									if len(lowercase_    ) != length:
																															return False
																			return True

													def _inputs_are_equal(lowercase_				,    lowercase_    ):
																			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						:					Any    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					Any    =   self.feat_extract_tester.prepare_inputs_for_common(numpify=lowercase_    )
													_snake_case						:					Optional[Any]    =   feat_extract.model_input_names[0]

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

													# truncate to smallest
													_snake_case						:					Optional[int]    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[0]    )				,    truncation=lowercase_    )
													_snake_case						:					Any    =   input_a[input_name]

													_snake_case						:					Optional[int]    =   feat_extract.pad(lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[0]    )    )
													_snake_case						:					Union[str, Any]    =   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						:					int    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[0]    )				,    return_tensors="np"				,    truncation=lowercase_				,    )
													_snake_case						:					str    =   input_a[input_name]

													_snake_case						:					Dict    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[0]    )				,    return_tensors="np"    )
													_snake_case						:					Tuple    =   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						:					Union[str, Any]    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[1]    )				,    truncation=lowercase_				,    return_tensors="np"				,    )
													_snake_case						:					Union[str, Any]    =   input_a[input_name]

													_snake_case						:					Any    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[1]    )				,    truncation=lowercase_    )
													_snake_case						:					Optional[Any]    =   input_a[input_name]

													_snake_case						:					List[str]    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[1]    )				,    return_tensors="np"    )
													_snake_case						:					Union[str, Any]    =   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						:					str    =   12
													_snake_case						:					Any    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[0]    )				,    pad_to_multiple_of=lowercase_				,    truncation=lowercase_				,    )
													_snake_case						:					Optional[Any]    =   input_a[input_name]

													_snake_case						:					str    =   feat_extract.pad(
													    lowercase_				,    padding="max_length"				,    max_length=len(speech_inputs[0]    )				,    pad_to_multiple_of=lowercase_				,    )
													_snake_case						:					Optional[Any]    =   input_a[input_name]

													# retrieve expected_length as multiple of pad_to_multiple_of
													_snake_case						:					List[Any]    =   len(speech_inputs[0]    )
													if expected_length % pad_to_multiple_of != 0:
																			_snake_case						:					List[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(lowercase_    )    )
													self.assertFalse(_inputs_have_equal_length(lowercase_    )    )



							def 	UpperCamelCase	(						self    ):
													self._check_padding(numpify=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self._check_padding(numpify=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self._check_truncation(numpify=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self._check_truncation(numpify=lowercase_    )



							@require_torch
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					int    =   self.feat_extract_tester.prepare_inputs_for_common()
													_snake_case						:					Tuple    =   feat_extract.model_input_names[0]

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

													_snake_case						:					Dict    =   feat_extract.pad(lowercase_				,    padding="longest"				,    return_tensors="np"    )[input_name]
													_snake_case						:					int    =   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 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.feature_extraction_class(**self.feat_extract_dict    )
													_snake_case						:					Any    =   self.feat_extract_tester.prepare_inputs_for_common()
													_snake_case						:					Optional[Any]    =   feat_extract.model_input_names[0]

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

													_snake_case						:					Union[str, Any]    =   feat_extract.pad(lowercase_				,    padding="longest"				,    return_tensors="np"    )[input_name]
													_snake_case						:					List[Any]    =   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 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   self.feat_extract_dict
													_snake_case						:					int    =   True
													_snake_case						:					Dict    =   self.feature_extraction_class(**lowercase_    )
													_snake_case						:					Dict    =   self.feat_extract_tester.prepare_inputs_for_common()
													_snake_case						:					str    =   [len(lowercase_    ) for x in speech_inputs]
													_snake_case						:					List[str]    =   feat_extract.model_input_names[0]

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

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

													_snake_case						:					Dict    =   BatchFeature({input_name: speech_inputs}    )
													_snake_case						:					Optional[Any]    =   min(lowercase_    )

													_snake_case						:					Optional[Any]    =   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  snake_case						(__lowercase						)	->	list:

						'''simple docstring'''



						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__lowercase						)						)
						    if txt[a].isalpha()
						]


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