'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase          =  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''OPTForCausalLM''',
						    '''OPTModel''',
						    '''OPTPreTrainedModel''',
						    '''OPTForSequenceClassification''',
						    '''OPTForQuestionAnswering''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FlaxOPTForCausalLM''',
						    '''FlaxOPTModel''',
						    '''FlaxOPTPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_opt import (
												    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OPTForCausalLM,
												    OPTForQuestionAnswering,
												    OPTForSequenceClassification,
												    OPTModel,
												    OPTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""


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     snake_case	(				__snake_case  ):
				# to overwrite at feature extractactor specific tests
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  None

				@property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						return self.feat_extract_tester.prepare_feat_extract_dict()

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "sampling_rate"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "padding_value"))

				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="np")

						__lowerCAmelCase:       Optional[int]          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				@require_torch
				def 							lowercase_     (	self							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="pt")

						__lowerCAmelCase:       str          =			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))

				@require_tf
				def 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="tf")

						__lowerCAmelCase:       int          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]=False)->   Optional[int]:



						'''simple docstring'''


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

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

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

						__lowerCAmelCase:       List[str]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			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:       str          =			self.feat_extract_tester.max_seq_length + pad_diff
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.min_seq_length
						__lowerCAmelCase:       Union[str, Any]          =			self.feat_extract_tester.batch_size
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(UpperCamelCase__     ,  padding=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest")
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[-1]))
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

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

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

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

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__)
						__lowerCAmelCase:       str          =			input_a[input_name]

						__lowerCAmelCase:       Dict          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

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

						__lowerCAmelCase:       Optional[Any]          =			pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(UpperCamelCase__) == 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:       Any          =			(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
						self.assertTrue(
						    abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
						    < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
						    < 1e-3)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]=False)->   str:



						'''simple docstring'''


						def _inputs_have_equal_length(UpperCamelCase__							:				List[Any]):
								__lowerCAmelCase:       List[str]          =			len(input[0])
								for input_slice in input[1:]:
										if len(UpperCamelCase__) != length:
												return False
								return True

						def _inputs_are_equal(UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[Any]):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

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

						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

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

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

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0]))
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

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

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np")
						__lowerCAmelCase:       Any          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						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(UpperCamelCase__))

						# truncate to middle
						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  return_tensors="np")
						__lowerCAmelCase:       str          =			input_a[input_name]

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

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

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

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

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

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

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Tuple          =			1_2
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  )
						__lowerCAmelCase:       str          =			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:       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(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

				def 							lowercase_     (	self							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   Tuple:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any])->   Optional[Any]:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="pt")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)

				@require_tf
				def 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Any          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="tf")[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1e-2)

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



						'''simple docstring'''


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

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

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




				def 							lowercase_     (	self							:				List[str])->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_dict
						__lowerCAmelCase:       str          =			True
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       str          =			[len(UpperCamelCase__) for x in speech_inputs]
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})
						__lowerCAmelCase:       List[str]          =			min(UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						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])


