

'''simple docstring'''





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax

							from transformers.models.big_bird.modeling_flax_big_bird import (
							    FlaxBigBirdForCausalLM,
							    FlaxBigBirdForMaskedLM,
							    FlaxBigBirdForMultipleChoice,
							    FlaxBigBirdForPreTraining,
							    FlaxBigBirdForQuestionAnswering,
							    FlaxBigBirdForSequenceClassification,
							    FlaxBigBirdForTokenClassification,
							    FlaxBigBirdModel,
							)
class        A  (	unittest.TestCase ):





						def __init__(	self	,    snake_case_	,    snake_case_=2	,    snake_case_=5_6	,    snake_case_=True	,    snake_case_=True	,    snake_case_=True	,    snake_case_=True	,    snake_case_=9_9	,    snake_case_=3_2	,    snake_case_=2	,    snake_case_=2	,    snake_case_=7	,    snake_case_="gelu_new"	,    snake_case_=0.1	,    snake_case_=0.1	,    snake_case_=5_1_2	,    snake_case_=1_6	,    snake_case_=2	,    snake_case_=0.02	,    snake_case_=4	,    snake_case_="block_sparse"	,    snake_case_=True	,    snake_case_=False	,    snake_case_=2	,    snake_case_=3	,    )			->							str:
										_a        =   parent
										_a        =   batch_size
										_a        =   seq_length
										_a        =   is_training
										_a        =   use_attention_mask
										_a        =   use_token_type_ids
										_a        =   use_labels
										_a        =   vocab_size
										_a        =   hidden_size
										_a        =   num_hidden_layers
										_a        =   num_attention_heads
										_a        =   intermediate_size
										_a        =   hidden_act
										_a        =   hidden_dropout_prob
										_a        =   attention_probs_dropout_prob
										_a        =   max_position_embeddings
										_a        =   type_vocab_size
										_a        =   type_sequence_label_size
										_a        =   initializer_range
										_a        =   num_choices

										_a        =   rescale_embeddings
										_a        =   attention_type
										_a        =   use_bias
										_a        =   block_size
										_a        =   num_random_blocks





						def __lowerCAmelCase   (	self			)			->							int:
										_a        =   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size			)

										_a        =   None
										if self.use_attention_mask:
														_a        =   random_attention_mask([self.batch_size, self.seq_length]			)

										_a        =   None
										if self.use_token_type_ids:
														_a        =   ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size			)

										_a        =   BigBirdConfig(
										    vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=snake_case_	,    initializer_range=self.initializer_range	,    attention_type=self.attention_type	,    block_size=self.block_size	,    num_random_blocks=self.num_random_blocks	,    use_bias=self.use_bias	,    rescale_embeddings=self.rescale_embeddings	,    )

										return config, input_ids, token_type_ids, attention_mask





						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										_a        =   self.prepare_config_and_inputs()
										_a      ,     _a      ,     _a      ,     _a        =   config_and_inputs
										_a        =   {
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										    "attention_mask": attention_mask,
										}
										return config, inputs_dict



@require_flax
class        A  (	a_  ,    unittest.TestCase ):
						__UpperCAmelCase				:	Optional[int]   =   (
						    (
						        FlaxBigBirdForCausalLM,
						        FlaxBigBirdModel,
						        FlaxBigBirdForPreTraining,
						        FlaxBigBirdForMaskedLM,
						        FlaxBigBirdForMultipleChoice,
						        FlaxBigBirdForQuestionAnswering,
						        FlaxBigBirdForSequenceClassification,
						        FlaxBigBirdForTokenClassification,
						    )
						    if is_flax_available()
						    else ()
						)

						__UpperCAmelCase				:	List[str]   =   False
						__UpperCAmelCase				:	Optional[Any]   =   False





						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										_a        =   FlaxBigBirdModelTester(self			)





						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										super().test_from_pretrained_save_pretrained()





						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def __lowerCAmelCase   (	self			)			->							Any:
										super().test_from_pretrained_with_no_automatic_init()





						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def __lowerCAmelCase   (	self			)			->							List[str]:
										super().test_no_automatic_init()





						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def __lowerCAmelCase   (	self			)			->							Tuple:
										super().test_hidden_states_output()





						@slow
						def __lowerCAmelCase   (	self			)			->							Any:







										for model_class_name in self.all_model_classes:
														_a        =   model_class_name.from_pretrained("google/bigbird-roberta-base"			)
														self.assertIsNotNone(snake_case_			)





						def __lowerCAmelCase   (	self			)			->							int:







										if self.test_attn_probs:
														super().test_attention_outputs()





						@slow
						# copied from `test_modeling_flax_common` because it takes much longer than other models
						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a      ,     _a        =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__			):
																		_a        =   self._prepare_for_class(snake_case_	,    snake_case_			)
																		_a        =   model_class(snake_case_			)

																		@jax.jit
																		def model_jitted(snake_case_	,    snake_case_=None	,    **snake_case_			):
																						return model(input_ids=snake_case_	,    attention_mask=snake_case_	,    **snake_case_			)

																		with self.subTest("JIT Enabled"			):
																						_a        =   model_jitted(**snake_case_			).to_tuple()

																		with self.subTest("JIT Disabled"			):
																						with jax.disable_jit():
																										_a        =   model_jitted(**snake_case_			).to_tuple()

																		self.assertEqual(len(snake_case_			)	,    len(snake_case_			)			)
																		for jitted_output, output in zip(snake_case_	,    snake_case_			):
																						self.assertEqual(jitted_output.shape	,    output.shape			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_=1E-5	,    snake_case_="outputs"	,    snake_case_=None			)			->							List[str]:







										if name.startswith("outputs.attentions"			):
														return
										else:
														super().check_pt_flax_outputs(snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_			)









import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

UpperCAmelCase   :    Any     =		random.Random()






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Union[str, Any]=1.0  ,							lowerCamelCase__		:						Union[str, Any]=None  ,							lowerCamelCase__		:						int=None      ):





			'''simple docstring'''




			if rng is None:
						lowerCamelCase    		=  global_rng

			lowerCamelCase    		=  []
			for batch_idx in range(shape[0]      ):
						values.append([]      )
						for _ in range(shape[1]      ):
									values[-1].append(rng.random() * scale      )

			return values







@require_torch
@require_torchaudio
class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A=7		,      A=4_00		,      A=20_00		,      A=24		,      A=24		,      A=0.0		,      A=1_60_00		,      A=True		,      A=True		,      )  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  parent
					lowerCamelCase    		=  batch_size
					lowerCamelCase    		=  min_seq_length
					lowerCamelCase    		=  max_seq_length
					lowerCamelCase    		=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					lowerCamelCase    		=  feature_size
					lowerCamelCase    		=  num_mel_bins
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  return_attention_mask
					lowerCamelCase    		=  do_normalize







		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					return {
					    "feature_size": self.feature_size,
					    "num_mel_bins": self.num_mel_bins,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}







		def 					__A   ( self		,      A=False		,      A=False	)  -> Tuple:







					'''simple docstring'''







					def _flatten(A	):
								return list(itertools.chain(*A	)	)

					if equal_length:
								lowerCamelCase    		=  [floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
					else:
								# make sure that inputs increase in size
								lowerCamelCase    		=  [
								    floats_list((x, self.feature_size)	)
								    for x in range(self.min_seq_length		,      self.max_seq_length		,      self.seq_length_diff	)
								]
					if numpify:
								lowerCamelCase    		=  [np.asarray(A	) for x in speech_inputs]
					return speech_inputs







@require_torch
@require_torchaudio
class        __lowercase       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		SpeechaTextFeatureExtractor if is_speech_available() else None







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  SpeechaTextFeatureExtractionTester(self	)







		def 					__A   ( self		,      A	)  -> List[Any]:







					'''simple docstring'''







					self.assertTrue(np.all(np.mean(A		,      axis=0	) < 1e-3	)	)
					self.assertTrue(np.all(np.abs(np.var(A		,      axis=0	) - 1	) < 1e-3	)	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					# create three inputs of length 800, 1000, and 1200
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  [np.asarray(A	) for speech_input in speech_inputs]

					# Test feature size
					lowerCamelCase    		=  feature_extractor(A		,      padding=A		,      return_tensors="""np"""	).input_features
					self.assertTrue(input_features.ndim == 3	)
					self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

					# Test not batched input
					lowerCamelCase    		=  feature_extractor(speech_inputs[0]		,      return_tensors="""np"""	).input_features
					lowerCamelCase    		=  feature_extractor(np_speech_inputs[0]		,      return_tensors="""np"""	).input_features
					self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)

					# Test batched
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					for enc_seq_a, enc_seq_a in zip(A		,      A	):
								self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)

					# Test 2-D numpy arrays are batched.
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in (8_00, 8_00, 8_00)]
					lowerCamelCase    		=  np.asarray(A	)
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""np"""	).input_features
					for enc_seq_a, enc_seq_a in zip(A		,      A	):
								self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)







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







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]

					lowerCamelCase    		=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCamelCase    		=  [None, 16, None]
					for max_length, padding in zip(A		,      A	):
								lowerCamelCase    		=  feature_extractor(
								    A		,      padding=A		,      max_length=A		,      return_attention_mask=A	)
								lowerCamelCase    		=  inputs.input_features
								lowerCamelCase    		=  inputs.attention_mask
								lowerCamelCase    		=  [np.sum(A	) for x in attention_mask]

								self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
								self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
								self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]

					lowerCamelCase    		=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCamelCase    		=  [None, 16, None]
					for max_length, padding in zip(A		,      A	):
								lowerCamelCase    		=  feature_extractor(
								    A		,      max_length=A		,      padding=A		,      return_tensors="""np"""		,      return_attention_mask=A	)
								lowerCamelCase    		=  inputs.input_features
								lowerCamelCase    		=  inputs.attention_mask
								lowerCamelCase    		=  [np.sum(A	) for x in attention_mask]

								self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
								self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6	)
								self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
								self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6	)
								self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feature_extractor(
					    A		,      padding="""max_length"""		,      max_length=4		,      truncation=A		,      return_tensors="""np"""		,      return_attention_mask=A		,      )
					lowerCamelCase    		=  inputs.input_features
					lowerCamelCase    		=  inputs.attention_mask
					lowerCamelCase    		=  np.sum(attention_mask == 1		,      axis=1	)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
					self._check_zero_mean_unit_variance(input_features[1]	)
					self._check_zero_mean_unit_variance(input_features[2]	)







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feature_extractor(
					    A		,      padding="""longest"""		,      max_length=4		,      truncation=A		,      return_tensors="""np"""		,      return_attention_mask=A		,      )
					lowerCamelCase    		=  inputs.input_features
					lowerCamelCase    		=  inputs.attention_mask
					lowerCamelCase    		=  np.sum(attention_mask == 1		,      axis=1	)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
					self._check_zero_mean_unit_variance(input_features[2]	)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape		,      (3, 4, 24)	)

					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feature_extractor(
					    A		,      padding="""longest"""		,      max_length=16		,      truncation=A		,      return_tensors="""np"""		,      return_attention_mask=A		,      )
					lowerCamelCase    		=  inputs.input_features
					lowerCamelCase    		=  inputs.attention_mask
					lowerCamelCase    		=  np.sum(attention_mask == 1		,      axis=1	)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
					self._check_zero_mean_unit_variance(input_features[2]	)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape		,      (3, 6, 24)	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					import torch

					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  np.random.rand(1_00		,      32	).astype(np.floataa	)
					lowerCamelCase    		=  np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
								lowerCamelCase    		=  feature_extractor.pad([{"""input_features""": inputs}]		,      return_tensors="""np"""	)
								self.assertTrue(np_processed.input_features.dtype == np.floataa	)
								lowerCamelCase    		=  feature_extractor.pad([{"""input_features""": inputs}]		,      return_tensors="""pt"""	)
								self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)







		def 					__A   ( self		,      A	)  -> Any:







					'''simple docstring'''







					from datasets import load_dataset

					lowerCamelCase    		=  load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,      """clean"""		,      split="""validation"""	)
					# automatic decoding with librispeech
					lowerCamelCase    		=  ds.sort("""id"""	).select(range(A	)	)[:num_samples]["""audio"""]

					return [x["array"] for x in speech_samples]







		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  np.array([
					    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
					    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
					    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
					]	)
					# fmt: on

					lowerCamelCase    		=  self._load_datasamples(1	)
					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  feature_extractor(A		,      return_tensors="""pt"""	).input_features
					self.assertEquals(input_features.shape		,      (1, 5_84, 24)	)
					self.assertTrue(np.allclose(input_features[0, 0, :30]		,      A		,      atol=1e-4	)	)

