'''simple docstring'''




from __future__ import annotations

import numpy as np




def 							lowerCamelCase	(      UpperCAmelCase__       :					list[float] )			->      Union[str, Any]:
							return np.maximum(0      ,      UpperCAmelCase__ )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]






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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			int										=       ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size    )

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

										lowercase_					:			Optional[int]										=       None
										if self.use_token_type_ids:
																	lowercase_					:			Tuple										=       ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size    )

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

										lowercase_					:			Tuple										=       self.get_config()

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										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=lowercase_				,			initializer_range=self.initializer_range				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]				,			lowercase_				:  Any				,			lowercase_				:  List[str]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Tuple				,			lowercase_				:  Tuple				,			lowercase_				:  Optional[int]    ):
										lowercase_					:			Optional[Any]										=       NystromformerModel(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Optional[int]										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_    )
										lowercase_					:			Optional[Any]										=       model(lowercase_				,			token_type_ids=lowercase_    )
										lowercase_					:			Union[str, Any]										=       model(lowercase_    )
										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple				,			lowercase_				:  List[Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]				,			lowercase_				:  List[Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Any    ):
										lowercase_					:			List[Any]										=       NystromformerForMaskedLM(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Optional[int]										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			lowercase_				:  List[Any]				,			lowercase_				:  Any				,			lowercase_				:  str				,			lowercase_				:  Dict				,			lowercase_				:  Any				,			lowercase_				:  Tuple    ):
										lowercase_					:			Any										=       NystromformerForQuestionAnswering(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Union[str, Any]										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			start_positions=lowercase_				,			end_positions=lowercase_				,			)
										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 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[str]				,			lowercase_				:  List[Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[Any]				,			lowercase_				:  str				,			lowercase_				:  int    ):
										lowercase_					:			Any										=       self.num_labels
										lowercase_					:			Union[str, Any]										=       NystromformerForSequenceClassification(lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Any										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  List[str]				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  Dict				,			lowercase_				:  Tuple				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]    ):
										lowercase_					:			int										=       self.num_labels
										lowercase_					:			int										=       NystromformerForTokenClassification(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Tuple										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)    )







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Union[str, Any]										=       self.prepare_config_and_inputs()
										(
										    (
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						
										)					:			Tuple										=       config_and_inputs
										lowercase_					:			Optional[Any]										=       {"""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 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Any										=       NystromformerModelTester(self    )
										lowercase_					:			Optional[Any]										=       ConfigTester(self				,			config_class=lowercase_				,			hidden_size=37    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										self.config_tester.run_common_tests()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Union[str, Any]										=       self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowercase_					:			int										=       type
																	self.model_tester.create_and_check_model(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Tuple										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			int										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Union[str, Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Any										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Optional[int]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowercase_    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_					:			List[Any]										=       NystromformerModel.from_pretrained(lowercase_    )
																	self.assertIsNotNone(lowercase_    )





@require_torch
class     __magic_name__						(    unittest.TestCase):







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

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(lowercase_    )[0]

										lowercase_					:			Tuple										=       torch.Size((1, 6, 768)    )
										self.assertEqual(output.shape				,			lowercase_    )

										lowercase_					:			Dict										=       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]				,			lowercase_				,			atol=1E-4    )    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Optional[int]										=       """the [MASK] of Belgium is Brussels"""

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

										lowercase_					:			str										=       tokenizer(lowercase_				,			return_tensors="""pt"""    )

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

										lowercase_					:			Optional[int]										=       token_logits[:, 2, :].argmax(-1    )[0]

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





