





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_		(  _lowerCamelCase					):
					# to overwrite at feature extractactor specific tests
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	None




					@property
					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										return self.feat_extract_tester.prepare_feat_extract_dict()




					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common()
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_dict  )
										_snake_case									=				feat_extract.model_input_names[0]

										_snake_case									=				BatchFeature({input_name: speech_inputs}  )

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

										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_  )
										_snake_case									=				BatchFeature({input_name: speech_inputs}				,						tensor_type='np'  )

										_snake_case									=				processed_features[input_name]

										if len(batch_features_input.shape  ) < 3:
															_snake_case									=				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 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_  )
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_dict  )
										_snake_case									=				feat_extract.model_input_names[0]

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

										_snake_case									=				processed_features[input_name]

										if len(batch_features_input.shape  ) < 3:
															_snake_case									=				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 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase_  )
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_dict  )
										_snake_case									=				feat_extract.model_input_names[0]

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

										_snake_case									=				processed_features[input_name]

										if len(batch_features_input.shape  ) < 3:
															_snake_case									=				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 	lowerCAmelCase			(		self				,						lowerCAmelCase_=False  )		->							List[Any]:
										def _inputs_have_equal_length(lowerCAmelCase_  ):
															_snake_case									=				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

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

										_snake_case									=				BatchFeature({input_name: speech_inputs}  )

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

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

										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						padding='longest'  )
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[-1]  )  )
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						padding='longest'				,						return_tensors='np'  )
										_snake_case									=				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]

										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=lowerCAmelCase_				,						return_tensors='np'  )
										_snake_case									=				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
										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						pad_to_multiple_of=10  )
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						padding='longest'				,						pad_to_multiple_of=10  )
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						pad_to_multiple_of=10				,						max_length=lowerCAmelCase_  )
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						pad_to_multiple_of=10				,						max_length=lowerCAmelCase_				,						return_tensors='np'				,						)
										_snake_case									=				input_a[input_name]

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

										_snake_case									=				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
										_snake_case									=				(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 	lowerCAmelCase			(		self				,						lowerCAmelCase_=False  )		->							Union[str, Any]:
										def _inputs_have_equal_length(lowerCAmelCase_  ):
															_snake_case									=				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

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

										_snake_case									=				BatchFeature({input_name: speech_inputs}  )

										# truncate to smallest
										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[0]  )				,						truncation=lowerCAmelCase_  )
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[0]  )  )
										_snake_case									=				input_a[input_name]

										self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_  )  )
										self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_  )  )

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

										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[0]  )				,						return_tensors='np'  )
										_snake_case									=				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
										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[1]  )				,						truncation=lowerCAmelCase_				,						return_tensors='np'				,						)
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[1]  )				,						truncation=lowerCAmelCase_  )
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[1]  )				,						return_tensors='np'  )
										_snake_case									=				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
										_snake_case									=				12
										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[0]  )				,						pad_to_multiple_of=lowerCAmelCase_				,						truncation=lowerCAmelCase_				,						)
										_snake_case									=				input_a[input_name]

										_snake_case									=				feat_extract.pad(
										    lowerCAmelCase_				,						padding='max_length'				,						max_length=len(speech_inputs[0]  )				,						pad_to_multiple_of=lowerCAmelCase_				,						)
										_snake_case									=				input_a[input_name]

										# retrieve expected_length as multiple of pad_to_multiple_of
										_snake_case									=				len(speech_inputs[0]  )
										if expected_length % pad_to_multiple_of != 0:
															_snake_case									=				((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 	lowerCAmelCase			(		self  )		->							Tuple:
										self._check_padding(numpify=lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										self._check_padding(numpify=lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										self._check_truncation(numpify=lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										self._check_truncation(numpify=lowerCAmelCase_  )




					@require_torch
					def 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_dict  )
										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common()
										_snake_case									=				feat_extract.model_input_names[0]

										_snake_case									=				BatchFeature({input_name: speech_inputs}  )

										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						padding='longest'				,						return_tensors='np'  )[input_name]
										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_dict  )
										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common()
										_snake_case									=				feat_extract.model_input_names[0]

										_snake_case									=				BatchFeature({input_name: speech_inputs}  )

										_snake_case									=				feat_extract.pad(lowerCAmelCase_				,						padding='longest'				,						return_tensors='np'  )[input_name]
										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				self.feat_extract_dict
										_snake_case									=				True
										_snake_case									=				self.feature_extraction_class(**lowerCAmelCase_  )
										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common()
										_snake_case									=				[len(lowerCAmelCase_  ) for x in speech_inputs]
										_snake_case									=				feat_extract.model_input_names[0]

										_snake_case									=				BatchFeature({input_name: speech_inputs}  )

										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				self.feat_extract_dict
										_snake_case									=				True
										_snake_case									=				self.feature_extraction_class(**lowerCAmelCase_  )
										_snake_case									=				self.feat_extract_tester.prepare_inputs_for_common()
										_snake_case									=				[len(lowerCAmelCase_  ) for x in speech_inputs]
										_snake_case									=				feat_extract.model_input_names[0]

										_snake_case									=				BatchFeature({input_name: speech_inputs}  )
										_snake_case									=				min(lowerCAmelCase_  )

										_snake_case									=				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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



