
'''simple docstring'''



import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  '''vision-encoder-decoder'''
						_lowercase						: Optional[Any]										=  True




						def __init__(   self       ,  **_lowercase ):

												"""simple docstring"""



												super().__init__(**_lowercase )
												if "encoder" not in kwargs or "decoder" not in kwargs:
																		raise ValueError(
																		    F'A configuraton of type {self.model_type} cannot be instantiated because '
																		    F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}' )

												_lowerCAmelCase    		=					kwargs.pop("""encoder""" )
												_lowerCAmelCase    		=					encoder_config.pop("""model_type""" )
												_lowerCAmelCase    		=					kwargs.pop("""decoder""" )
												_lowerCAmelCase    		=					decoder_config.pop("""model_type""" )

												_lowerCAmelCase    		=					AutoConfig.for_model(_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					AutoConfig.for_model(_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					True




						@classmethod
						def    _lowercase      (   cls       ,  _lowercase       ,  _lowercase       ,  **_lowercase ):

												"""simple docstring"""



												logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
												_lowerCAmelCase    		=					True
												_lowerCAmelCase    		=					True

												return cls(encoder=encoder_config.to_dict()       ,  decoder=decoder_config.to_dict()       ,  **_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					copy.deepcopy(self.__dict__ )
												_lowerCAmelCase    		=					self.encoder.to_dict()
												_lowerCAmelCase    		=					self.decoder.to_dict()
												_lowerCAmelCase    		=					self.__class__.model_type
												return output






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[Any]										=  version.parse('''1.11'''					)




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return OrderedDict(
												    [
												        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
												    ] )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 1e-4




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OrderedDict()
												_lowerCAmelCase    		=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
												_lowerCAmelCase    		=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
												_lowerCAmelCase    		=					{0: """batch""", 1: """encoder_sequence"""}

												return common_inputs




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = -1       ,  _lowercase = -1       ,  _lowercase = False       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												import torch

												_lowerCAmelCase    		=					OrderedDict()

												_lowerCAmelCase    		=					super().generate_dummy_inputs(
												    _lowercase       ,  batch_size=_lowercase       ,  seq_length=_lowercase       ,  is_pair=_lowercase       ,  framework=_lowercase )

												_lowerCAmelCase		,       _lowerCAmelCase    		=					dummy_input["""input_ids"""].shape
												_lowerCAmelCase    		=					(batch, encoder_sequence, self._config.encoder_hidden_size)
												_lowerCAmelCase    		=					dummy_input.pop("""input_ids""" )
												_lowerCAmelCase    		=					dummy_input.pop("""attention_mask""" )
												_lowerCAmelCase    		=					torch.zeros(_lowercase )

												return common_inputs






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												return VisionEncoderDecoderEncoderOnnxConfig(_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = "default" ):

												"""simple docstring"""



												_lowerCAmelCase    		=					encoder_config.hidden_size
												return VisionEncoderDecoderDecoderOnnxConfig(_lowercase       ,  _lowercase )







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_  (  snake_case__							):
					# to overwrite at feature extractactor specific tests
					_a      :						str						    =					None
					_a      :						Tuple						    =					None



					@property
					def   __a     (		self     :     str						):
							return self.feat_extract_tester.prepare_feat_extract_dict()



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

							lowerCamelCase_	:     Optional[int] 						=      BatchFeature({input_name: speech_inputs}						)

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

							lowerCamelCase_	:     Tuple 						=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase						)
							lowerCamelCase_	:     Union[str, Any] 						=      BatchFeature({input_name: speech_inputs}       ,      tensor_type='np'						)

							lowerCamelCase_	:     int 						=      processed_features[input_name]

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



					@require_torch
					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     Any 						=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     Tuple 						=      feat_extract.model_input_names[0]

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

							lowerCamelCase_	:     Optional[int] 						=      processed_features[input_name]

							if len(batch_features_input.shape						) < 3:
									lowerCamelCase_	:     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   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     int 						=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     int 						=      feat_extract.model_input_names[0]

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

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

							if len(batch_features_input.shape						) < 3:
									lowerCamelCase_	:     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   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     int=False						):
							def _inputs_have_equal_length(lowerCamelCase     :     Union[str, Any]						):
									lowerCamelCase_	:     Any 						=      len(input[0]						)
									for input_slice in input[1:]:
											if len(lowerCamelCase						) != length:
													return False
									return True

							def _inputs_are_equal(lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Dict						):
									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

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

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

							lowerCamelCase_	:     Optional[int] 						=      self.feat_extract_tester.seq_length_diff
							lowerCamelCase_	:     int 						=      self.feat_extract_tester.max_seq_length + pad_diff
							lowerCamelCase_	:     Optional[int] 						=      self.feat_extract_tester.min_seq_length
							lowerCamelCase_	:     List[Any] 						=      self.feat_extract_tester.batch_size
							lowerCamelCase_	:     int 						=      self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							lowerCamelCase_	:     int 						=      feat_extract.pad(lowerCamelCase       ,      padding=lowerCamelCase						)
							lowerCamelCase_	:     Tuple 						=      input_a[input_name]

							lowerCamelCase_	:     List[str] 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'						)
							lowerCamelCase_	:     Optional[Any] 						=      input_a[input_name]

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

							lowerCamelCase_	:     Union[str, Any] 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      return_tensors='np'						)
							lowerCamelCase_	:     int 						=      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]

							lowerCamelCase_	:     Union[str, Any] 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=lowerCamelCase       ,      return_tensors='np'						)
							lowerCamelCase_	:     Dict 						=      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
							lowerCamelCase_	:     Optional[Any] 						=      feat_extract.pad(lowerCamelCase       ,      pad_to_multiple_of=10						)
							lowerCamelCase_	:     Dict 						=      input_a[input_name]

							lowerCamelCase_	:     List[Any] 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      pad_to_multiple_of=10						)
							lowerCamelCase_	:     Union[str, Any] 						=      input_a[input_name]

							lowerCamelCase_	:     str 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      pad_to_multiple_of=10       ,      max_length=lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      input_a[input_name]

							lowerCamelCase_	:     Any 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      pad_to_multiple_of=10       ,      max_length=lowerCamelCase       ,      return_tensors='np'       ,      )
							lowerCamelCase_	:     str 						=      input_a[input_name]

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

							lowerCamelCase_	:     Dict 						=      pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							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
							lowerCamelCase_	:     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     :     Optional[Any]       ,      lowerCamelCase     :     Any=False						):
							def _inputs_have_equal_length(lowerCamelCase     :     Dict						):
									lowerCamelCase_	:     Dict 						=      len(input[0]						)
									for input_slice in input[1:]:
											if len(lowerCamelCase						) != length:
													return False
									return True

							def _inputs_are_equal(lowerCamelCase     :     int       ,      lowerCamelCase     :     List[str]						):
									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

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

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

							# truncate to smallest
							lowerCamelCase_	:     Dict 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      truncation=lowerCamelCase						)
							lowerCamelCase_	:     int 						=      input_a[input_name]

							lowerCamelCase_	:     str 						=      feat_extract.pad(lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)						)
							lowerCamelCase_	:     Union[str, Any] 						=      input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(lowerCamelCase						)						)
							self.assertFalse(_inputs_have_equal_length(lowerCamelCase						)						)

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

							lowerCamelCase_	:     int 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      return_tensors='np'						)
							lowerCamelCase_	:     Tuple 						=      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
							lowerCamelCase_	:     Tuple 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[1]						)       ,      truncation=lowerCamelCase       ,      return_tensors='np'       ,      )
							lowerCamelCase_	:     Union[str, Any] 						=      input_a[input_name]

							lowerCamelCase_	:     str 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[1]						)       ,      truncation=lowerCamelCase						)
							lowerCamelCase_	:     Any 						=      input_a[input_name]

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

							lowerCamelCase_	:     List[Any] 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      pad_to_multiple_of=lowerCamelCase       ,      )
							lowerCamelCase_	:     Dict 						=      input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							lowerCamelCase_	:     Optional[Any] 						=      len(speech_inputs[0]						)
							if expected_length % pad_to_multiple_of != 0:
									lowerCamelCase_	:     str 						=      ((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     :     Any						):
							self._check_padding(numpify=lowerCamelCase						)



					def   __a     (		self     :     List[Any]						):
							self._check_padding(numpify=lowerCamelCase						)



					def   __a     (		self     :     int						):
							self._check_truncation(numpify=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							self._check_truncation(numpify=lowerCamelCase						)



					@require_torch
					def   __a     (		self     :     str						):
							lowerCamelCase_	:     List[Any] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     str 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     Optional[Any] 						=      feat_extract.model_input_names[0]

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

							lowerCamelCase_	:     int 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      return_tensors='np'						)[input_name]
							lowerCamelCase_	:     Dict 						=      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     :     Any						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     List[str] 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     Tuple 						=      feat_extract.model_input_names[0]

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

							lowerCamelCase_	:     Dict 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      return_tensors='np'						)[input_name]
							lowerCamelCase_	:     List[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     :     str						):
							lowerCamelCase_	:     Any 						=      self.feat_extract_dict
							lowerCamelCase_	:     Optional[Any] 						=      True
							lowerCamelCase_	:     Dict 						=      self.feature_extraction_class(**lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     List[str] 						=      [len(lowerCamelCase						) for x in speech_inputs]
							lowerCamelCase_	:     List[Any] 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     List[str] 						=      BatchFeature({input_name: speech_inputs}						)

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

							lowerCamelCase_	:     Any 						=      BatchFeature({input_name: speech_inputs}						)
							lowerCamelCase_	:     Optional[Any] 						=      min(lowerCamelCase						)

							lowerCamelCase_	:     List[Any] 						=      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]						)
