


'''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  __magic_name__							:






							def __init__(      self  ,							snake_case  ,							snake_case=1_3  ,							snake_case=7  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=9_9  ,							snake_case=3_2  ,							snake_case=5  ,							snake_case=4  ,							snake_case=3_7  ,							snake_case="gelu"  ,							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=3  ,							snake_case=4  ,							snake_case=None  ,							)						->  Optional[Any]:




										'''simple docstring'''
										_UpperCAmelCase       :       List[Any]							   =parent
										_UpperCAmelCase       :       List[str]							   =batch_size
										_UpperCAmelCase       :       List[str]							   =seq_length
										_UpperCAmelCase       :       List[str]							   =is_training
										_UpperCAmelCase       :       Optional[int]							   =use_input_mask
										_UpperCAmelCase       :       int							   =use_token_type_ids
										_UpperCAmelCase       :       List[Any]							   =use_labels
										_UpperCAmelCase       :       int							   =vocab_size
										_UpperCAmelCase       :       Union[str, Any]							   =hidden_size
										_UpperCAmelCase       :       Optional[int]							   =num_hidden_layers
										_UpperCAmelCase       :       List[str]							   =num_attention_heads
										_UpperCAmelCase       :       Dict							   =intermediate_size
										_UpperCAmelCase       :       Any							   =hidden_act
										_UpperCAmelCase       :       Dict							   =hidden_dropout_prob
										_UpperCAmelCase       :       Dict							   =attention_probs_dropout_prob
										_UpperCAmelCase       :       Optional[Any]							   =max_position_embeddings
										_UpperCAmelCase       :       Dict							   =type_vocab_size
										_UpperCAmelCase       :       Dict							   =type_sequence_label_size
										_UpperCAmelCase       :       Optional[int]							   =initializer_range
										_UpperCAmelCase       :       List[Any]							   =num_labels
										_UpperCAmelCase       :       Dict							   =num_choices
										_UpperCAmelCase       :       List[Any]							   =scope






							def       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										_UpperCAmelCase       :       str							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size)

										_UpperCAmelCase       :       Optional[int]							   =None
										if self.use_input_mask:
													_UpperCAmelCase       :       List[str]							   =random_attention_mask([self.batch_size, self.seq_length])

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

										_UpperCAmelCase       :       Optional[int]							   =None
										_UpperCAmelCase       :       List[str]							   =None
										_UpperCAmelCase       :       List[str]							   =None
										if self.use_labels:
													_UpperCAmelCase       :       Union[str, Any]							   =ids_tensor([self.batch_size]  ,							self.type_sequence_label_size)
													_UpperCAmelCase       :       Optional[Any]							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.num_labels)
													_UpperCAmelCase       :       Any							   =ids_tensor([self.batch_size]  ,							self.num_choices)

										_UpperCAmelCase       :       Tuple							   =self.get_config()

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






							def       lowerCAmelCase		(      self)						->  Tuple:




										'''simple docstring'''
										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=_UpperCAmelCase  ,							initializer_range=self.initializer_range  ,							)






							def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  List[str]:




										'''simple docstring'''
										_UpperCAmelCase       :       Any							   =NystromformerModel(config=_UpperCAmelCase)
										model.to(_UpperCAmelCase)
										model.eval()
										_UpperCAmelCase       :       Optional[Any]							   =model(_UpperCAmelCase  ,							attention_mask=_UpperCAmelCase  ,							token_type_ids=_UpperCAmelCase)
										_UpperCAmelCase       :       Tuple							   =model(_UpperCAmelCase  ,							token_type_ids=_UpperCAmelCase)
										_UpperCAmelCase       :       List[str]							   =model(_UpperCAmelCase)
										self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size))






							def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  Union[str, Any]:




										'''simple docstring'''
										_UpperCAmelCase       :       Any							   =NystromformerForMaskedLM(config=_UpperCAmelCase)
										model.to(_UpperCAmelCase)
										model.eval()
										_UpperCAmelCase       :       Optional[int]							   =model(_UpperCAmelCase  ,							attention_mask=_UpperCAmelCase  ,							token_type_ids=_UpperCAmelCase  ,							labels=_UpperCAmelCase)
										self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.vocab_size))






							def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  List[Any]:




										'''simple docstring'''
										_UpperCAmelCase       :       List[Any]							   =NystromformerForQuestionAnswering(config=_UpperCAmelCase)
										model.to(_UpperCAmelCase)
										model.eval()
										_UpperCAmelCase       :       Dict							   =model(
										    _UpperCAmelCase  ,							attention_mask=_UpperCAmelCase  ,							token_type_ids=_UpperCAmelCase  ,							start_positions=_UpperCAmelCase  ,							end_positions=_UpperCAmelCase  ,							)
										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       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  Optional[int]:




										'''simple docstring'''
										_UpperCAmelCase       :       List[Any]							   =self.num_labels
										_UpperCAmelCase       :       int							   =NystromformerForSequenceClassification(_UpperCAmelCase)
										model.to(_UpperCAmelCase)
										model.eval()
										_UpperCAmelCase       :       Union[str, Any]							   =model(_UpperCAmelCase  ,							attention_mask=_UpperCAmelCase  ,							token_type_ids=_UpperCAmelCase  ,							labels=_UpperCAmelCase)
										self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_labels))






							def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  List[Any]:




										'''simple docstring'''
										_UpperCAmelCase       :       int							   =self.num_labels
										_UpperCAmelCase       :       Union[str, Any]							   =NystromformerForTokenClassification(config=_UpperCAmelCase)
										model.to(_UpperCAmelCase)
										model.eval()
										_UpperCAmelCase       :       int							   =model(_UpperCAmelCase  ,							attention_mask=_UpperCAmelCase  ,							token_type_ids=_UpperCAmelCase  ,							labels=_UpperCAmelCase)
										self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.num_labels))






							def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case)						->  Any:




										'''simple docstring'''
										_UpperCAmelCase       :       Tuple							   =self.num_choices
										_UpperCAmelCase       :       Optional[int]							   =NystromformerForMultipleChoice(config=_UpperCAmelCase)
										model.to(_UpperCAmelCase)
										model.eval()
										_UpperCAmelCase       :       Union[str, Any]							   =input_ids.unsqueeze(1).expand(-1  ,							self.num_choices  ,							-1).contiguous()
										_UpperCAmelCase       :       Optional[Any]							   =token_type_ids.unsqueeze(1).expand(-1  ,							self.num_choices  ,							-1).contiguous()
										_UpperCAmelCase       :       List[str]							   =input_mask.unsqueeze(1).expand(-1  ,							self.num_choices  ,							-1).contiguous()
										_UpperCAmelCase       :       str							   =model(
										    _UpperCAmelCase  ,							attention_mask=_UpperCAmelCase  ,							token_type_ids=_UpperCAmelCase  ,							labels=_UpperCAmelCase  ,							)
										self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_choices))






							def       lowerCAmelCase		(      self)						->  Tuple:




										'''simple docstring'''
										_UpperCAmelCase       :       int							   =self.prepare_config_and_inputs()
										(
										    _UpperCAmelCase			
										)       :       List[Any]							   =config_and_inputs
										_UpperCAmelCase       :       Optional[int]							   ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict






@require_torch
class  __magic_name__							(		_UpperCAmelCase ,_UpperCAmelCase ,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       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										_UpperCAmelCase       :       Optional[Any]							   =NystromformerModelTester(self)
										_UpperCAmelCase       :       Any							   =ConfigTester(self  ,							config_class=_UpperCAmelCase  ,							hidden_size=3_7)






							def       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										self.config_tester.run_common_tests()






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




										'''simple docstring'''
										_UpperCAmelCase       :       Any							   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_UpperCAmelCase)






							def       lowerCAmelCase		(      self)						->  Dict:




										'''simple docstring'''
										_UpperCAmelCase       :       Optional[int]							   =self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
													_UpperCAmelCase       :       List[str]							   =type
													self.model_tester.create_and_check_model(*_UpperCAmelCase)






							def       lowerCAmelCase		(      self)						->  int:




										'''simple docstring'''
										_UpperCAmelCase       :       Tuple							   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase)






							def       lowerCAmelCase		(      self)						->  int:




										'''simple docstring'''
										_UpperCAmelCase       :       int							   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase)






							def       lowerCAmelCase		(      self)						->  int:




										'''simple docstring'''
										_UpperCAmelCase       :       List[str]							   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase)






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




										'''simple docstring'''
										_UpperCAmelCase       :       List[Any]							   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase)






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




										'''simple docstring'''
										_UpperCAmelCase       :       Optional[int]							   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase)






							@slow
							def       lowerCAmelCase		(      self)						->  Any:




										'''simple docstring'''
										for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCAmelCase       :       str							   =NystromformerModel.from_pretrained(_UpperCAmelCase)
													self.assertIsNotNone(_UpperCAmelCase)






@require_torch
class  __magic_name__							(		unittest.TestCase    ):






							@slow
							def       lowerCAmelCase		(      self)						->  Optional[Any]:




										'''simple docstring'''
										_UpperCAmelCase       :       Dict							   =NystromformerModel.from_pretrained('uw-madison/nystromformer-512')
										_UpperCAmelCase       :       Union[str, Any]							   =torch.tensor([[0, 1, 2, 3, 4, 5]])

										with torch.no_grad():
													_UpperCAmelCase       :       Any							   =model(_UpperCAmelCase)[0]

										_UpperCAmelCase       :       str							   =torch.Size((1, 6, 7_6_8))
										self.assertEqual(output.shape  ,							_UpperCAmelCase)

										_UpperCAmelCase       :       Union[str, Any]							   =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]  ,							_UpperCAmelCase  ,							atol=1E-4))






							@slow
							def       lowerCAmelCase		(      self)						->  Optional[int]:




										'''simple docstring'''
										_UpperCAmelCase       :       List[Any]							   ='''the [MASK] of Belgium is Brussels'''

										_UpperCAmelCase       :       str							   =AutoTokenizer.from_pretrained('uw-madison/nystromformer-512')
										_UpperCAmelCase       :       Optional[int]							   =NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512')

										_UpperCAmelCase       :       Any							   =tokenizer(_UpperCAmelCase  ,							return_tensors='pt')

										with torch.no_grad():
													_UpperCAmelCase       :       Optional[int]							   =model(encoding.input_ids).logits

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

										self.assertEqual(tokenizer.decode(_UpperCAmelCase)  ,							'capital')





'''simple docstring'''






from __future__ import annotations

from random import choice





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   return choice(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =random_pivot(__lowerCamelCase							)

   # partition based on pivot
   # linear time
   _UpperCAmelCase       :       str							   =[e for e in lst if e < pivot]
   _UpperCAmelCase       :       Dict							   =[e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__lowerCamelCase							) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__lowerCamelCase							) < k - 1:
      return kth_number(__lowerCamelCase	,   k - len(__lowerCamelCase							) - 1							)
   # pivot is in elements smaller than k
   else:
      return kth_number(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

