






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_				(      lowerCamelCase ):
				# to overwrite at feature extractactor specific tests
				a__					       =  None
				a__					       =  None







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return self.feat_extract_tester.prepare_feat_extract_dict()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.feature_extraction_class(**self.feat_extract_dict     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''feature_size'''     )     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''sampling_rate'''     )     )
								self.assertTrue(hasattr(__lowerCAmelCase       ,					'''padding_value'''     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.feat_extract_tester.prepare_inputs_for_common()
								__magic_name__		:Dict        =      self.feature_extraction_class(**self.feat_extract_dict     )
								__magic_name__		:int        =      feat_extract.model_input_names[0]

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

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

								__magic_name__		:Tuple        =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase     )
								__magic_name__		:str        =      BatchFeature({input_name: speech_inputs}       ,					tensor_type='''np'''     )

								__magic_name__		:str        =      processed_features[input_name]

								if len(batch_features_input.shape     ) < 3:
												__magic_name__		: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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase     )
								__magic_name__		:Optional[int]        =      self.feature_extraction_class(**self.feat_extract_dict     )
								__magic_name__		:Optional[int]        =      feat_extract.model_input_names[0]

								__magic_name__		:str        =      BatchFeature({input_name: speech_inputs}       ,					tensor_type='''pt'''     )

								__magic_name__		:List[Any]        =      processed_features[input_name]

								if len(batch_features_input.shape     ) < 3:
												__magic_name__		: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_tf
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase     )
								__magic_name__		:str        =      self.feature_extraction_class(**self.feat_extract_dict     )
								__magic_name__		:Any        =      feat_extract.model_input_names[0]

								__magic_name__		:int        =      BatchFeature({input_name: speech_inputs}       ,					tensor_type='''tf'''     )

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

								if len(batch_features_input.shape     ) < 3:
												__magic_name__		: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)     )







				def 			A	(						self       ,					__lowerCAmelCase=False     ):
								"""simple docstring"""
								def _inputs_have_equal_length(__lowerCAmelCase     ):
												__magic_name__		:int        =      len(input[0]     )
												for input_slice in input[1:]:
																if len(__lowerCAmelCase     ) != length:
																				return False
												return True

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

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

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

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

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

								# test padding for List[int] + numpy
								__magic_name__		:Dict        =      feat_extract.pad(__lowerCAmelCase       ,					padding=__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      input_a[input_name]

								__magic_name__		:Optional[Any]        =      feat_extract.pad(__lowerCAmelCase       ,					padding='''longest'''     )
								__magic_name__		:str        =      input_a[input_name]

								__magic_name__		:List[Any]        =      feat_extract.pad(__lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[-1]     )     )
								__magic_name__		:List[str]        =      input_a[input_name]

								__magic_name__		:int        =      feat_extract.pad(__lowerCAmelCase       ,					padding='''longest'''       ,					return_tensors='''np'''     )
								__magic_name__		:List[Any]        =      input_a[input_name]

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

								__magic_name__		:Optional[Any]        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=__lowerCAmelCase       ,					return_tensors='''np'''     )
								__magic_name__		:Optional[Any]        =      input_a[input_name]

								self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase     )     )
								self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase     )     )
								self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase     )     )
								self.assertTrue(_inputs_are_equal(__lowerCAmelCase       ,					__lowerCAmelCase     )     )
								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
								__magic_name__		:Dict        =      feat_extract.pad(__lowerCAmelCase       ,					pad_to_multiple_of=1_0     )
								__magic_name__		:Dict        =      input_a[input_name]

								__magic_name__		:Any        =      feat_extract.pad(__lowerCAmelCase       ,					padding='''longest'''       ,					pad_to_multiple_of=1_0     )
								__magic_name__		:Optional[int]        =      input_a[input_name]

								__magic_name__		:int        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					pad_to_multiple_of=1_0       ,					max_length=__lowerCAmelCase     )
								__magic_name__		:Any        =      input_a[input_name]

								__magic_name__		:int        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					pad_to_multiple_of=1_0       ,					max_length=__lowerCAmelCase       ,					return_tensors='''np'''       ,					)
								__magic_name__		:str        =      input_a[input_name]

								self.assertTrue(all(len(__lowerCAmelCase     ) % 1_0 == 0 for x in input_a     )     )
								self.assertTrue(_inputs_are_equal(__lowerCAmelCase       ,					__lowerCAmelCase     )     )

								__magic_name__		:Optional[int]        =      pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
								self.assertTrue(all(len(__lowerCAmelCase     ) == 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
								__magic_name__		:Dict        =      (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 			A	(						self       ,					__lowerCAmelCase=False     ):
								"""simple docstring"""
								def _inputs_have_equal_length(__lowerCAmelCase     ):
												__magic_name__		:int        =      len(input[0]     )
												for input_slice in input[1:]:
																if len(__lowerCAmelCase     ) != length:
																				return False
												return True

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

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

								__magic_name__		:Optional[int]        =      self.feature_extraction_class(**self.feat_extract_dict     )
								__magic_name__		:Union[str, Any]        =      self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase     )
								__magic_name__		:List[str]        =      feat_extract.model_input_names[0]

								__magic_name__		:Any        =      BatchFeature({input_name: speech_inputs}     )

								# truncate to smallest
								__magic_name__		:Optional[Any]        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[0]     )       ,					truncation=__lowerCAmelCase     )
								__magic_name__		:Dict        =      input_a[input_name]

								__magic_name__		:List[Any]        =      feat_extract.pad(__lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[0]     )     )
								__magic_name__		:int        =      input_a[input_name]

								self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase     )     )
								self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase     )     )

								# truncate to smallest with np
								__magic_name__		:Tuple        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[0]     )       ,					return_tensors='''np'''       ,					truncation=__lowerCAmelCase       ,					)
								__magic_name__		:int        =      input_a[input_name]

								__magic_name__		:int        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[0]     )       ,					return_tensors='''np'''     )
								__magic_name__		:str        =      input_a[input_name]

								self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase     )     )
								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(__lowerCAmelCase     )     )

								# truncate to middle
								__magic_name__		:Dict        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[1]     )       ,					truncation=__lowerCAmelCase       ,					return_tensors='''np'''       ,					)
								__magic_name__		:Tuple        =      input_a[input_name]

								__magic_name__		:Tuple        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[1]     )       ,					truncation=__lowerCAmelCase     )
								__magic_name__		:List[str]        =      input_a[input_name]

								__magic_name__		:int        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[1]     )       ,					return_tensors='''np'''     )
								__magic_name__		:Union[str, Any]        =      input_a[input_name]

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

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

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

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

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

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

								# test truncation for `pad_to_multiple_of` for List[int] + numpy
								__magic_name__		:Union[str, Any]        =      1_2
								__magic_name__		:List[Any]        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[0]     )       ,					pad_to_multiple_of=__lowerCAmelCase       ,					truncation=__lowerCAmelCase       ,					)
								__magic_name__		:Dict        =      input_a[input_name]

								__magic_name__		:str        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=len(speech_inputs[0]     )       ,					pad_to_multiple_of=__lowerCAmelCase       ,					)
								__magic_name__		:str        =      input_a[input_name]

								# retrieve expected_length as multiple of pad_to_multiple_of
								__magic_name__		:int        =      len(speech_inputs[0]     )
								if expected_length % pad_to_multiple_of != 0:
												__magic_name__		: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(__lowerCAmelCase     )     )
								self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								self._check_padding(numpify=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								self._check_padding(numpify=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								self._check_truncation(numpify=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								self._check_truncation(numpify=__lowerCAmelCase     )







				@require_torch
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.feature_extraction_class(**self.feat_extract_dict     )
								__magic_name__		:List[Any]        =      self.feat_extract_tester.prepare_inputs_for_common()
								__magic_name__		:Dict        =      feat_extract.model_input_names[0]

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

								__magic_name__		:List[Any]        =      feat_extract.pad(__lowerCAmelCase       ,					padding='''longest'''       ,					return_tensors='''np'''     )[input_name]
								__magic_name__		:int        =      feat_extract.pad(__lowerCAmelCase       ,					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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.feature_extraction_class(**self.feat_extract_dict     )
								__magic_name__		:Optional[int]        =      self.feat_extract_tester.prepare_inputs_for_common()
								__magic_name__		:List[str]        =      feat_extract.model_input_names[0]

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

								__magic_name__		:List[str]        =      feat_extract.pad(__lowerCAmelCase       ,					padding='''longest'''       ,					return_tensors='''np'''     )[input_name]
								__magic_name__		:str        =      feat_extract.pad(__lowerCAmelCase       ,					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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.feat_extract_dict
								__magic_name__		:Any        =      True
								__magic_name__		:Tuple        =      self.feature_extraction_class(**__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      self.feat_extract_tester.prepare_inputs_for_common()
								__magic_name__		:List[str]        =      [len(__lowerCAmelCase     ) for x in speech_inputs]
								__magic_name__		:Any        =      feat_extract.model_input_names[0]

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

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.feat_extract_dict
								__magic_name__		:int        =      True
								__magic_name__		:int        =      self.feature_extraction_class(**__lowerCAmelCase     )
								__magic_name__		:str        =      self.feat_extract_tester.prepare_inputs_for_common()
								__magic_name__		:Optional[Any]        =      [len(__lowerCAmelCase     ) for x in speech_inputs]
								__magic_name__		:Optional[Any]        =      feat_extract.model_input_names[0]

								__magic_name__		:List[Any]        =      BatchFeature({input_name: speech_inputs}     )
								__magic_name__		:int        =      min(__lowerCAmelCase     )

								__magic_name__		:Dict        =      feat_extract.pad(
								    __lowerCAmelCase       ,					padding='''max_length'''       ,					max_length=__lowerCAmelCase       ,					truncation=__lowerCAmelCase       ,					return_tensors='''np'''     )
								self.assertIn('''attention_mask'''       ,					__lowerCAmelCase     )
								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 typing import List

from .keymap import KEYMAP, get_character

def      __lowercase					( snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:int        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += [key]
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator

def      __lowercase					( *snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:Union[str, Any]        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += keys
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __new__(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      super().__new__(cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								if not hasattr(__lowerCAmelCase       ,					'''key_handler'''     ):
												setattr(__lowerCAmelCase       ,					'''key_handler'''       ,					{}     )
								setattr(__lowerCAmelCase       ,					'''handle_input'''       ,					KeyHandler.handle_input     )

								for value in attrs.values():
												__magic_name__		:Dict        =      getattr(__lowerCAmelCase       ,					'''handle_key'''       ,					[]     )
												for key in handled_keys:
																__magic_name__		:List[Any]        =      value
								return new_cls







				@staticmethod
				def 			A	(						cls     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      get_character()
								if char != KEYMAP["undefined"]:
												__magic_name__		:Dict        =      ord(__lowerCAmelCase     )
								__magic_name__		:str        =      cls.key_handler.get(__lowerCAmelCase     )
								if handler:
												__magic_name__		:Optional[Any]        =      char
												return handler(cls     )
								else:
												return None







def      __lowercase					( cls ):







				"""simple docstring"""






				return KeyHandler(cls.__name__,  cls.__bases__,  cls.__dict__.copy() )






