






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      :







     '''simple docstring'''




     def __init__(				self       :			Union[str, Any] ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			Tuple=13 ,	UpperCamelCase_       :			List[Any]=7 ,	UpperCamelCase_       :			int=True ,	UpperCamelCase_       :			Optional[Any]=True ,	UpperCamelCase_       :			int=True ,	UpperCamelCase_       :			Dict=True ,	UpperCamelCase_       :			str=99 ,	UpperCamelCase_       :			Tuple=32 ,	UpperCamelCase_       :			Optional[int]=5 ,	UpperCamelCase_       :			int=4 ,	UpperCamelCase_       :			Tuple=37 ,	UpperCamelCase_       :			str="gelu" ,	UpperCamelCase_       :			List[Any]=0.1 ,	UpperCamelCase_       :			Tuple=0.1 ,	UpperCamelCase_       :			Optional[Any]=512 ,	UpperCamelCase_       :			Optional[int]=16 ,	UpperCamelCase_       :			Optional[Any]=2 ,	UpperCamelCase_       :			Union[str, Any]=0.02 ,	UpperCamelCase_       :			List[str]=3 ,	UpperCamelCase_       :			int=4 ,	UpperCamelCase_       :			Any=None ,	):


         """simple docstring"""



         __A         =	parent
         __A         =	batch_size
         __A         =	seq_length
         __A         =	is_training
         __A         =	use_input_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_labels
         __A         =	num_choices
         __A         =	scope




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



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

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

         __A         =	self.get_config()

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




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """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       :			Any ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			int				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Dict				):


         """simple docstring"""



         __A         =	NystromformerForMaskedLM(config=__UpperCamelCase				)
         model.to(__UpperCamelCase				)
         model.eval()
         __A         =	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       :			List[Any] ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



         __A         =	NystromformerForQuestionAnswering(config=__UpperCamelCase				)
         model.to(__UpperCamelCase				)
         model.eval()
         __A         =	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       :			Dict ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         __A         =	self.num_labels
         __A         =	NystromformerForSequenceClassification(__UpperCamelCase				)
         model.to(__UpperCamelCase				)
         model.eval()
         __A         =	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       :			Any ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         __A         =	self.num_labels
         __A         =	NystromformerForTokenClassification(config=__UpperCamelCase				)
         model.to(__UpperCamelCase				)
         model.eval()
         __A         =	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       :			Dict ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Dict				):


         """simple docstring"""



         __A         =	self.num_choices
         __A         =	NystromformerForMultipleChoice(config=__UpperCamelCase				)
         model.to(__UpperCamelCase				)
         model.eval()
         __A         =	input_ids.unsqueeze(1				).expand(-1 ,	self.num_choices ,	-1				).contiguous()
         __A         =	token_type_ids.unsqueeze(1				).expand(-1 ,	self.num_choices ,	-1				).contiguous()
         __A         =	input_mask.unsqueeze(1				).expand(-1 ,	self.num_choices ,	-1				).contiguous()
         __A         =	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       :			List[str]				):


         """simple docstring"""



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


@require_torch
class       __lowercase      (	__snake_case			,      __snake_case			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				(
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE   						=				(
         {
             "feature-extraction": NystromformerModel,
             "fill-mask": NystromformerForMaskedLM,
             "question-answering": NystromformerForQuestionAnswering,
             "text-classification": NystromformerForSequenceClassification,
             "token-classification": NystromformerForTokenClassification,
             "zero-shot": NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE   						=				False
     SCREAMING_SNAKE_CASE   						=				False




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	NystromformerModelTester(self				)
         __A         =	ConfigTester(self ,	config_class=__UpperCamelCase ,	hidden_size=37				)




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCamelCase				)




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase				)




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase				)




     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase				)




     def 		lowerCAmelCase_     (				self       :			int				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase				)




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase				)





     @slow
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



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


@require_torch
class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



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

         with torch.no_grad():
             __A         =	model(__UpperCamelCase				)[0]

         __A         =	torch.Size((1, 6, 768)				)
         self.assertEqual(output.shape ,	__UpperCamelCase				)

         __A         =	torch.tensor(
             [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]				)

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





     @slow
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



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

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

         __A         =	tokenizer(__UpperCamelCase ,	return_tensors="""pt"""				)

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

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

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












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

    if sum(__lowercase    ) > max_sum or (remaining_nums_sum + sum(__lowercase    )) < max_sum:
        return
    if sum(__lowercase    ) == max_sum:
        result.append(__lowercase    )
        return
    for index in range(__lowercase			,				len(__lowercase    )    ):
        create_state_space_tree(
            __lowercase			,				__lowercase			,				index + 1			,				[*path, nums[index]]			,				__lowercase			,				remaining_nums_sum - nums[index]			,				)
__a      :       str				  =   [3, 34, 4, 12, 5, 2]
__a      :       Optional[Any]				  =   9
__a      :       List[str]				  =   generate_sum_of_subsets_soln(nums, max_sum)






print(*result)




