






'''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     a_						:

  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=0.0_2      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=4      ,		_SCREAMING_SNAKE_CASE=None      ,		)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = seq_length
        UpperCamelCase      = is_training
        UpperCamelCase      = use_input_mask
        UpperCamelCase      = use_token_type_ids
        UpperCamelCase      = use_labels
        UpperCamelCase      = vocab_size
        UpperCamelCase      = hidden_size
        UpperCamelCase      = num_hidden_layers
        UpperCamelCase      = num_attention_heads
        UpperCamelCase      = intermediate_size
        UpperCamelCase      = hidden_act
        UpperCamelCase      = hidden_dropout_prob
        UpperCamelCase      = attention_probs_dropout_prob
        UpperCamelCase      = max_position_embeddings
        UpperCamelCase      = type_vocab_size
        UpperCamelCase      = type_sequence_label_size
        UpperCamelCase      = initializer_range
        UpperCamelCase      = num_labels
        UpperCamelCase      = num_choices
        UpperCamelCase      = scope

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size				)

        UpperCamelCase      = None
        if self.use_input_mask:
              UpperCamelCase      = random_attention_mask([self.batch_size, self.seq_length]				)

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

        UpperCamelCase      = None
        UpperCamelCase      = None
        UpperCamelCase      = None
        if self.use_labels:
              UpperCamelCase      = ids_tensor([self.batch_size]      ,		self.type_sequence_label_size				)
              UpperCamelCase      = ids_tensor([self.batch_size, self.seq_length]      ,		self.num_labels				)
              UpperCamelCase      = ids_tensor([self.batch_size]      ,		self.num_choices				)

        UpperCamelCase      = self.get_config()

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

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



        """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=_SCREAMING_SNAKE_CASE      ,		initializer_range=self.initializer_range      ,		)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = NystromformerModel(config=_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE      ,		attention_mask=_SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = NystromformerForMaskedLM(config=_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = 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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = NystromformerForQuestionAnswering(config=_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = 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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = self.num_labels
        UpperCamelCase      = NystromformerForSequenceClassification(_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = 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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = self.num_labels
        UpperCamelCase      = NystromformerForTokenClassification(config=_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = 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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.num_choices
        UpperCamelCase      = NystromformerForMultipleChoice(config=_SCREAMING_SNAKE_CASE				)
        model.to(_SCREAMING_SNAKE_CASE				)
        model.eval()
        UpperCamelCase      = input_ids.unsqueeze(1				).expand(-1      ,		self.num_choices      ,		-1				).contiguous()
        UpperCamelCase      = token_type_ids.unsqueeze(1				).expand(-1      ,		self.num_choices      ,		-1				).contiguous()
        UpperCamelCase      = input_mask.unsqueeze(1				).expand(-1      ,		self.num_choices      ,		-1				).contiguous()
        UpperCamelCase      = 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   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = self.prepare_config_and_inputs()
        (
            (
            UpperCamelCase    
        )  ,(
            UpperCamelCase    
        )  ,(
            UpperCamelCase    
        )  ,(
            UpperCamelCase    
        )  ,(
            UpperCamelCase    
        )  ,(
            UpperCamelCase    
        )  ,(
            UpperCamelCase    
        )  ,    
        )      = config_and_inputs
        UpperCamelCase      = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict






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

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = NystromformerModelTester(self				)
        UpperCamelCase      = ConfigTester(self      ,		config_class=_SCREAMING_SNAKE_CASE      ,		hidden_size=37				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCamelCase      = type
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE				)


  @slow
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase      = NystromformerModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)






@require_torch
class     a_						(      unittest.TestCase    ):

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



        """simple docstring"""






        UpperCamelCase      = NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""				)
        UpperCamelCase      = torch.tensor([[0, 1, 2, 3, 4, 5]]				)

        with torch.no_grad():
              UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)[0]

        UpperCamelCase      = torch.Size((1, 6, 768)				)
        self.assertEqual(output.shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor(
            [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]				)

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


  @slow
  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = """the [MASK] of Belgium is Brussels"""

        UpperCamelCase      = AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""				)
        UpperCamelCase      = NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""				)

        UpperCamelCase      = tokenizer(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

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

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

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








'''simple docstring'''

from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = prime_factors(__UpperCamelCase   )
      if is_square_free(__UpperCamelCase   ):
            return -1 if len(__UpperCamelCase   ) % 2 else 1
      return 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()
