

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 _UpperCamelCase							:
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       parent
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       seq_length
				SCREAMING_SNAKE_CASE   :      int				     =       is_training
				SCREAMING_SNAKE_CASE   :      List[str]				     =       use_input_mask
				SCREAMING_SNAKE_CASE   :      str				     =       use_token_type_ids
				SCREAMING_SNAKE_CASE   :      List[str]				     =       use_labels
				SCREAMING_SNAKE_CASE   :      str				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      Tuple				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      str				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      Tuple				     =       type_vocab_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_labels
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_choices
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       scope
			def 							__UpperCamelCase (			self							: Any	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       None
				if self.use_input_mask:
					SCREAMING_SNAKE_CASE   :      Any				     =       random_attention_mask([self.batch_size, self.seq_length]	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
				if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size	)

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

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 							__UpperCamelCase (			self							: int	)							->       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=a      ,      initializer_range=self.initializer_range      ,      )
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Union[str, Any]      ,      a							: int      ,      a							: Optional[int]      ,      a							: int      ,      a							: Optional[Any]      ,      a							: Dict      ,      a							: List[str]	)							->       Dict:

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       NystromformerForQuestionAnswering(config=a	)
				model.to(a	)
				model.eval()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       model(
				    a      ,      attention_mask=a      ,      token_type_ids=a      ,      start_positions=a      ,      end_positions=a      ,      )
				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							: Union[str, Any]      ,      a							: Tuple      ,      a							: Union[str, Any]      ,      a							: Optional[Any]      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: Optional[int]      ,      a							: Optional[int]	)							->       Dict:

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

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






			def 							__UpperCamelCase (			self							: Dict	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.prepare_config_and_inputs()
				(
				    SCREAMING_SNAKE_CASE
				)   :      Tuple				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      int				     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class _UpperCamelCase							(							__A							,	__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(
			    (
			        NystromformerModel,
			        NystromformerForMaskedLM,
			        NystromformerForMultipleChoice,
			        NystromformerForQuestionAnswering,
			        NystromformerForSequenceClassification,
			        NystromformerForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__           =(
			    {
			        'feature-extraction': NystromformerModel,
			        'fill-mask': NystromformerForMaskedLM,
			        'question-answering': NystromformerForQuestionAnswering,
			        'text-classification': NystromformerForSequenceClassification,
			        'token-classification': NystromformerForTokenClassification,
			        'zero-shot': NystromformerForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: List[str]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       NystromformerModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ConfigTester(self      ,      config_class=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					SCREAMING_SNAKE_CASE   :      str				     =       type
					self.model_tester.create_and_check_model(*a	)
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*a	)
			def 							__UpperCamelCase (			self							: str	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*a	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*a	)
			def 							__UpperCamelCase (			self							: Dict	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*a	)






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

				"""simple docstring"""

				for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       NystromformerModel.from_pretrained(a	)
					self.assertIsNotNone(a	)






@require_torch
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

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

				with torch.no_grad():
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model(a	)[0]

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.Size((1, 6, 768)	)
				self.assertEqual(output.shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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]      ,      a      ,      atol=1e-4	)	)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       "the [MASK] of Belgium is Brussels"

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

				SCREAMING_SNAKE_CASE   :      Any				     =       tokenizer(a      ,      return_tensors="pt"	)

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

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

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


def  lowerCamelCase__ (					_a = 50):
	SCREAMING_SNAKE_CASE   :      int				     =       [1] * (length + 1)

	for row_length in range(length + 1):
		for tile_length in range(2							,			5):
			for tile_start in range(row_length - tile_length + 1):
				ways_number[row_length] += ways_number[
				    row_length - tile_start - tile_length
				]

	return ways_number[length]


if __name__ == "__main__":
			print(F'''{solution() = }''')