




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       _A				(		UpperCamelCase__     ):
		# to overwrite at feature extractactor specific tests
		__a					    =       None
		__a					    =       None






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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""feature_size"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""sampling_rate"""			)			)
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,		"""padding_value"""			)			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common()
								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}			)

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

								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}						,		tensor_type="""np"""			)

								_UpperCAmelCase      =			processed_features[input_name]

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

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






		@require_torch
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

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

								_UpperCAmelCase      =			processed_features[input_name]

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

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






		@require_tf
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

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

								_UpperCAmelCase      =			processed_features[input_name]

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

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=False			):
								def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			len(input[0]			)
														for input_slice in input[1:]:
																				if len(_SCREAMING_SNAKE_CASE			) != length:
																										return False
														return True

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

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

								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}			)

								_UpperCAmelCase      =			self.feat_extract_tester.seq_length_diff
								_UpperCAmelCase      =			self.feat_extract_tester.max_seq_length + pad_diff
								_UpperCAmelCase      =			self.feat_extract_tester.min_seq_length
								_UpperCAmelCase      =			self.feat_extract_tester.batch_size
								_UpperCAmelCase      =			self.feat_extract_tester.feature_size

								# test padding for List[int] + numpy
								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""longest"""			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[-1]			)			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""longest"""						,		return_tensors="""np"""			)
								_UpperCAmelCase      =			input_a[input_name]

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

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=_SCREAMING_SNAKE_CASE						,		return_tensors="""np"""			)
								_UpperCAmelCase      =			input_a[input_name]

								self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE			)			)
								self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE			)			)
								self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE			)			)
								self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)			)
								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
								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		pad_to_multiple_of=10			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""longest"""						,		pad_to_multiple_of=10			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		pad_to_multiple_of=10						,		max_length=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		pad_to_multiple_of=10						,		max_length=_SCREAMING_SNAKE_CASE						,		return_tensors="""np"""						,		)
								_UpperCAmelCase      =			input_a[input_name]

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

								_UpperCAmelCase      =			pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
								self.assertTrue(all(len(_SCREAMING_SNAKE_CASE			) == 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
								_UpperCAmelCase      =			(np.ones(self.feat_extract_tester.feature_size			) * feat_extract.padding_value).sum()
								self.assertTrue(
								    abs(np.asarray(input_a[0]			)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			)
								    < 1e-3			)
								self.assertTrue(
								    abs(
								        np.asarray(input_a[1]			)[pad_min_length + pad_diff :].sum()
								        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)			)
								    < 1e-3			)
								self.assertTrue(
								    abs(
								        np.asarray(input_a[2]			)[pad_min_length + 2 * pad_diff :].sum()
								        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)			)
								    < 1e-3			)
								self.assertTrue(
								    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)			) < 1e-3			)
								self.assertTrue(
								    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)			)
								    < 1e-3			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=False			):
								def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			len(input[0]			)
														for input_slice in input[1:]:
																				if len(_SCREAMING_SNAKE_CASE			) != length:
																										return False
														return True

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

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

								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}			)

								# truncate to smallest
								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[0]			)						,		truncation=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[0]			)			)
								_UpperCAmelCase      =			input_a[input_name]

								self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE			)			)
								self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE			)			)

								# truncate to smallest with np
								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[0]			)						,		return_tensors="""np"""						,		truncation=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[0]			)						,		return_tensors="""np"""			)
								_UpperCAmelCase      =			input_a[input_name]

								self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE			)			)
								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(_SCREAMING_SNAKE_CASE			)			)

								# truncate to middle
								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[1]			)						,		truncation=_SCREAMING_SNAKE_CASE						,		return_tensors="""np"""						,		)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[1]			)						,		truncation=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[1]			)						,		return_tensors="""np"""			)
								_UpperCAmelCase      =			input_a[input_name]

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

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

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

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

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

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

								# test truncation for `pad_to_multiple_of` for List[int] + numpy
								_UpperCAmelCase      =			12
								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[0]			)						,		pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,		truncation=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			input_a[input_name]

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=len(speech_inputs[0]			)						,		pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			input_a[input_name]

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






		def 				UpperCAmelCase (    self			):
								self._check_padding(numpify=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								self._check_padding(numpify=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								self._check_truncation(numpify=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								self._check_truncation(numpify=_SCREAMING_SNAKE_CASE			)






		@require_torch
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common()
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}			)

								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""longest"""						,		return_tensors="""np"""			)[input_name]
								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""longest"""						,		return_tensors="""pt"""			)[input_name]

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






		@require_tf
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feature_extraction_class(**self.feat_extract_dict			)
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common()
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}			)

								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""longest"""						,		return_tensors="""np"""			)[input_name]
								_UpperCAmelCase      =			feat_extract.pad(_SCREAMING_SNAKE_CASE						,		padding="""longest"""						,		return_tensors="""tf"""			)[input_name]

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feat_extract_dict
								_UpperCAmelCase      =			True
								_UpperCAmelCase      =			self.feature_extraction_class(**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common()
								_UpperCAmelCase      =			[len(_SCREAMING_SNAKE_CASE			) for x in speech_inputs]
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}			)

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.feat_extract_dict
								_UpperCAmelCase      =			True
								_UpperCAmelCase      =			self.feature_extraction_class(**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.feat_extract_tester.prepare_inputs_for_common()
								_UpperCAmelCase      =			[len(_SCREAMING_SNAKE_CASE			) for x in speech_inputs]
								_UpperCAmelCase      =			feat_extract.model_input_names[0]

								_UpperCAmelCase      =			BatchFeature({input_name: speech_inputs}			)
								_UpperCAmelCase      =			min(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			feat_extract.pad(
								    _SCREAMING_SNAKE_CASE						,		padding="""max_length"""						,		max_length=_SCREAMING_SNAKE_CASE						,		truncation=_SCREAMING_SNAKE_CASE						,		return_tensors="""np"""			)
								self.assertIn("""attention_mask"""						,		_SCREAMING_SNAKE_CASE			)
								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 Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)