


"""simple docstring"""
import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     NystromformerForMaskedLM,
     NystromformerForMultipleChoice,
     NystromformerForQuestionAnswering,
     NystromformerForSequenceClassification,
     NystromformerForTokenClassification,
     NystromformerModel,
 )
 from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=13      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=99      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=5      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=37      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=16      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=None      ,   )  ->  List[Any]:
      _lowerCamelCase	:					Union[str, Any]   =		parent
      _lowerCamelCase	:					Optional[Any]   =		batch_size
      _lowerCamelCase	:					Optional[int]   =		seq_length
      _lowerCamelCase	:					Any   =		is_training
      _lowerCamelCase	:					int   =		use_input_mask
      _lowerCamelCase	:					Tuple   =		use_token_type_ids
      _lowerCamelCase	:					List[str]   =		use_labels
      _lowerCamelCase	:					Any   =		vocab_size
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					Any   =		num_hidden_layers
      _lowerCamelCase	:					Dict   =		num_attention_heads
      _lowerCamelCase	:					List[Any]   =		intermediate_size
      _lowerCamelCase	:					str   =		hidden_act
      _lowerCamelCase	:					Tuple   =		hidden_dropout_prob
      _lowerCamelCase	:					List[str]   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Dict   =		max_position_embeddings
      _lowerCamelCase	:					str   =		type_vocab_size
      _lowerCamelCase	:					Any   =		type_sequence_label_size
      _lowerCamelCase	:					List[str]   =		initializer_range
      _lowerCamelCase	:					Optional[Any]   =		num_labels
      _lowerCamelCase	:					List[str]   =		num_choices
      _lowerCamelCase	:					int   =		scope





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Any   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

      _lowerCamelCase	:					Union[str, Any]   =		None
      if self.use_input_mask:
         _lowerCamelCase	:					Optional[Any]   =		random_attention_mask([self.batch_size, self.seq_length])

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

      _lowerCamelCase	:					Dict   =		None
      _lowerCamelCase	:					Any   =		None
      _lowerCamelCase	:					List[Any]   =		None
      if self.use_labels:
         _lowerCamelCase	:					List[str]   =		ids_tensor([self.batch_size]      ,   self.type_sequence_label_size)
         _lowerCamelCase	:					List[str]   =		ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels)
         _lowerCamelCase	:					Dict   =		ids_tensor([self.batch_size]      ,   self.num_choices)

      _lowerCamelCase	:					int   =		self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





   def 		UpperCamelCase_  (							self)  ->  Dict:
      return NystromformerConfig(
          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=SCREAMING_SNAKE_CASE      ,   initializer_range=self.initializer_range      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					str   =		NystromformerModel(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		model(SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					Union[str, Any]   =		NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					List[str]   =		NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Dict   =		model(
          SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   start_positions=SCREAMING_SNAKE_CASE      ,   end_positions=SCREAMING_SNAKE_CASE      ,   )
      self.parent.assertEqual(result.start_logits.shape      ,   (self.batch_size, self.seq_length))
      self.parent.assertEqual(result.end_logits.shape      ,   (self.batch_size, self.seq_length))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      _lowerCamelCase	:					Union[str, Any]   =		self.num_labels
      _lowerCamelCase	:					int   =		NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					str   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Tuple   =		self.num_labels
      _lowerCamelCase	:					int   =		NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					int   =		self.num_choices
      _lowerCamelCase	:					Any   =		NystromformerForMultipleChoice(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()
      _lowerCamelCase	:					Optional[Any]   =		input_ids.unsqueeze(1).expand(-1      ,   self.num_choices      ,   -1).contiguous()
      _lowerCamelCase	:					List[str]   =		token_type_ids.unsqueeze(1).expand(-1      ,   self.num_choices      ,   -1).contiguous()
      _lowerCamelCase	:					Dict   =		input_mask.unsqueeze(1).expand(-1      ,   self.num_choices      ,   -1).contiguous()
      _lowerCamelCase	:					str   =		model(
          SCREAMING_SNAKE_CASE      ,   attention_mask=SCREAMING_SNAKE_CASE      ,   token_type_ids=SCREAMING_SNAKE_CASE      ,   labels=SCREAMING_SNAKE_CASE      ,   )
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_choices))





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Tuple   =		self.prepare_config_and_inputs()
      (
          (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    
      )	:					Union[str, Any]   =		config_and_inputs
      _lowerCamelCase	:					Optional[int]   =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_torch
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(
       (
           NystromformerModel,
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase							       =					(
       {
           '''feature-extraction''': NystromformerModel,
           '''fill-mask''': NystromformerForMaskedLM,
           '''question-answering''': NystromformerForQuestionAnswering,
           '''text-classification''': NystromformerForSequenceClassification,
           '''token-classification''': NystromformerForTokenClassification,
           '''zero-shot''': NystromformerForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		NystromformerModelTester(self)
      _lowerCamelCase	:					List[Any]   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   hidden_size=37)





   def 		UpperCamelCase_  (							self)  ->  str:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[int]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCamelCase	:					int   =		type
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Dict   =		NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)







@require_torch
class 	lowercase__				(					unittest.TestCase				):





   @slow
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Union[str, Any]   =		NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""")
      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE)[0]

      _lowerCamelCase	:					Any   =		torch.Size((1, 6, 768))
      self.assertEqual(output.shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		torch.tensor(
          [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))





   @slow
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[Any]   =		"""the [MASK] of Belgium is Brussels"""

      _lowerCamelCase	:					Optional[Any]   =		AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""")
      _lowerCamelCase	:					List[Any]   =		NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""")

      _lowerCamelCase	:					str   =		tokenizer(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")

      with torch.no_grad():
         _lowerCamelCase	:					Tuple   =		model(encoding.input_ids).logits

      _lowerCamelCase	:					str   =		token_logits[:, 2, :].argmax(-1)[0]

      self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE)      ,   """capital""")





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	)	)

