


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 					lowerCAmelCase_ (       _a):
						# to overwrite at feature extractactor specific tests
						lowerCamelCase_         =     None
						lowerCamelCase_         =     None




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






									"""simple docstring"""






									return self.feat_extract_tester.prepare_feat_extract_dict()




						def  _snake_case					(	self						:   Optional[Any]						)      ->Tuple:






									"""simple docstring"""






									a__ :str						  =    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  _snake_case					(	self						:   List[Any]						)      ->List[Any]:






									"""simple docstring"""






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

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

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

									a__ :List[Any]						  =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__A						)
									a__ :int						  =    BatchFeature({input_name: speech_inputs}   ,     tensor_type="np"						)

									a__ :int						  =    processed_features[input_name]

									if len(batch_features_input.shape						) < 3:
												a__ :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  _snake_case					(	self						:   List[Any]						)      ->List[str]:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__A						)
									a__ :int						  =    self.feature_extraction_class(**self.feat_extract_dict						)
									a__ :int						  =    feat_extract.model_input_names[0]

									a__ :str						  =    BatchFeature({input_name: speech_inputs}   ,     tensor_type="pt"						)

									a__ :Any						  =    processed_features[input_name]

									if len(batch_features_input.shape						) < 3:
												a__ :Optional[int]						  =    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						:   int						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Tuple						  =    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__A						)
									a__ :Dict						  =    self.feature_extraction_class(**self.feat_extract_dict						)
									a__ :List[str]						  =    feat_extract.model_input_names[0]

									a__ :Dict						  =    BatchFeature({input_name: speech_inputs}   ,     tensor_type="tf"						)

									a__ :int						  =    processed_features[input_name]

									if len(batch_features_input.shape						) < 3:
												a__ :Optional[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  _snake_case					(	self						:   int   ,     __A						:   List[Any]=False						)      ->List[Any]:






									"""simple docstring"""






									def _inputs_have_equal_length(__A						:   List[Any]						):
												a__ :int						  =    len(input[0]						)
												for input_slice in input[1:]:
															if len(__A						) != length:
																		return False
												return True

									def _inputs_are_equal(__A						:   List[str]   ,     __A						:   str						):
												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

									a__ :Dict						  =    self.feature_extraction_class(**self.feat_extract_dict						)
									a__ :Any						  =    self.feat_extract_tester.prepare_inputs_for_common(numpify=__A						)
									a__ :List[str]						  =    feat_extract.model_input_names[0]

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

									a__ :Optional[Any]						  =    self.feat_extract_tester.seq_length_diff
									a__ :str						  =    self.feat_extract_tester.max_seq_length + pad_diff
									a__ :Optional[int]						  =    self.feat_extract_tester.min_seq_length
									a__ :Tuple						  =    self.feat_extract_tester.batch_size
									a__ :Optional[Any]						  =    self.feat_extract_tester.feature_size

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

									a__ :Any						  =    feat_extract.pad(__A   ,     padding="longest"						)
									a__ :Optional[Any]						  =    input_a[input_name]

									a__ :Union[str, Any]						  =    feat_extract.pad(__A   ,     padding="max_length"   ,     max_length=len(speech_inputs[-1]						)						)
									a__ :Optional[Any]						  =    input_a[input_name]

									a__ :str						  =    feat_extract.pad(__A   ,     padding="longest"   ,     return_tensors="np"						)
									a__ :Any						  =    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]

									a__ :List[Any]						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     max_length=__A   ,     return_tensors="np"						)
									a__ :Optional[Any]						  =    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
									a__ :Any						  =    feat_extract.pad(__A   ,     pad_to_multiple_of=10						)
									a__ :List[str]						  =    input_a[input_name]

									a__ :Tuple						  =    feat_extract.pad(__A   ,     padding="longest"   ,     pad_to_multiple_of=10						)
									a__ :int						  =    input_a[input_name]

									a__ :Union[str, Any]						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     pad_to_multiple_of=10   ,     max_length=__A						)
									a__ :Any						  =    input_a[input_name]

									a__ :Optional[int]						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     pad_to_multiple_of=10   ,     max_length=__A   ,     return_tensors="np"   ,     )
									a__ :Any						  =    input_a[input_name]

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

									a__ :Any						  =    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
									a__ :int						  =    (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						:   Any   ,     __A						:   str=False						)      ->List[str]:






									"""simple docstring"""






									def _inputs_have_equal_length(__A						:   Tuple						):
												a__ :List[str]						  =    len(input[0]						)
												for input_slice in input[1:]:
															if len(__A						) != length:
																		return False
												return True

									def _inputs_are_equal(__A						:   Dict   ,     __A						:   Dict						):
												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

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

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

									# truncate to smallest
									a__ :Union[str, Any]						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     max_length=len(speech_inputs[0]						)   ,     truncation=__A						)
									a__ :Any						  =    input_a[input_name]

									a__ :Optional[Any]						  =    feat_extract.pad(__A   ,     padding="max_length"   ,     max_length=len(speech_inputs[0]						)						)
									a__ :int						  =    input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(__A						)						)
									self.assertFalse(_inputs_have_equal_length(__A						)						)

									# truncate to smallest with np
									a__ :Optional[int]						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     max_length=len(speech_inputs[0]						)   ,     return_tensors="np"   ,     truncation=__A   ,     )
									a__ :List[Any]						  =    input_a[input_name]

									a__ :Tuple						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     max_length=len(speech_inputs[0]						)   ,     return_tensors="np"						)
									a__ :Dict						  =    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
									a__ :Any						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     max_length=len(speech_inputs[1]						)   ,     truncation=__A   ,     return_tensors="np"   ,     )
									a__ :int						  =    input_a[input_name]

									a__ :Union[str, Any]						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     max_length=len(speech_inputs[1]						)   ,     truncation=__A						)
									a__ :Dict						  =    input_a[input_name]

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

									a__ :int						  =    feat_extract.pad(
									    __A   ,     padding="max_length"   ,     max_length=len(speech_inputs[0]						)   ,     pad_to_multiple_of=__A   ,     )
									a__ :Tuple						  =    input_a[input_name]

									# retrieve expected_length as multiple of pad_to_multiple_of
									a__ :str						  =    len(speech_inputs[0]						)
									if expected_length % pad_to_multiple_of != 0:
												a__ :Optional[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(__A						)						)
									self.assertFalse(_inputs_have_equal_length(__A						)						)




						def  _snake_case					(	self						:   str						)      ->Dict:






									"""simple docstring"""






									self._check_padding(numpify=__A						)




						def  _snake_case					(	self						:   Optional[int]						)      ->Dict:






									"""simple docstring"""






									self._check_padding(numpify=__A						)




						def  _snake_case					(	self						:   Any						)      ->Optional[int]:






									"""simple docstring"""






									self._check_truncation(numpify=__A						)




						def  _snake_case					(	self						:   Tuple						)      ->int:






									"""simple docstring"""






									self._check_truncation(numpify=__A						)




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






									"""simple docstring"""






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

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

									a__ :int						  =    feat_extract.pad(__A   ,     padding="longest"   ,     return_tensors="np"						)[input_name]
									a__ :List[Any]						  =    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  _snake_case					(	self						:   int						)      ->int:






									"""simple docstring"""






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

									a__ :int						  =    BatchFeature({input_name: speech_inputs}						)

									a__ :Tuple						  =    feat_extract.pad(__A   ,     padding="longest"   ,     return_tensors="np"						)[input_name]
									a__ :Optional[Any]						  =    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  _snake_case					(	self						:   Tuple						)      ->List[str]:






									"""simple docstring"""






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

									a__ :int						  =    BatchFeature({input_name: speech_inputs}						)

									a__ :List[Any]						  =    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  _snake_case					(	self						:   int						)      ->Tuple:






									"""simple docstring"""






									a__ :Dict						  =    self.feat_extract_dict
									a__ :List[str]						  =    True
									a__ :int						  =    self.feature_extraction_class(**__A						)
									a__ :str						  =    self.feat_extract_tester.prepare_inputs_for_common()
									a__ :Dict						  =    [len(__A						) for x in speech_inputs]
									a__ :List[str]						  =    feat_extract.model_input_names[0]

									a__ :str						  =    BatchFeature({input_name: speech_inputs}						)
									a__ :List[Any]						  =    min(__A						)

									a__ :Any						  =    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]						)






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *


