


"""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 			__snake_case   :




							def __init__(     self:    List[Any]     ,							A_:    List[str]     ,							A_:    List[Any]=13     ,							A_:    List[Any]=7     ,							A_:    Tuple=True     ,							A_:    str=True     ,							A_:    List[str]=True     ,							A_:    List[Any]=True     ,							A_:    Dict=99     ,							A_:    List[str]=32     ,							A_:    Any=5     ,							A_:    Optional[Any]=4     ,							A_:    Tuple=37     ,							A_:    int="gelu"     ,							A_:    Tuple=0.1     ,							A_:    Any=0.1     ,							A_:    List[str]=5_12     ,							A_:    Tuple=16     ,							A_:    Dict=2     ,							A_:    List[str]=0.02     ,							A_:    Any=3     ,							A_:    int=4     ,							A_:    List[str]=None     ,							):
										__lowerCamelCase          =      parent
										__lowerCamelCase          =      batch_size
										__lowerCamelCase          =      seq_length
										__lowerCamelCase          =      is_training
										__lowerCamelCase          =      use_input_mask
										__lowerCamelCase          =      use_token_type_ids
										__lowerCamelCase          =      use_labels
										__lowerCamelCase          =      vocab_size
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_hidden_layers
										__lowerCamelCase          =      num_attention_heads
										__lowerCamelCase          =      intermediate_size
										__lowerCamelCase          =      hidden_act
										__lowerCamelCase          =      hidden_dropout_prob
										__lowerCamelCase          =      attention_probs_dropout_prob
										__lowerCamelCase          =      max_position_embeddings
										__lowerCamelCase          =      type_vocab_size
										__lowerCamelCase          =      type_sequence_label_size
										__lowerCamelCase          =      initializer_range
										__lowerCamelCase          =      num_labels
										__lowerCamelCase          =      num_choices
										__lowerCamelCase          =      scope




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size   )

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

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

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

										__lowerCamelCase          =      self.get_config()

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




							def   __a   (     self:    Optional[Any]   ):
										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=snake_case_     ,							initializer_range=self.initializer_range     ,							)




							def   __a   (     self:    Tuple     ,							A_:    List[str]     ,							A_:    List[Any]     ,							A_:    List[str]     ,							A_:    Optional[int]     ,							A_:    Dict     ,							A_:    Any     ,							A_:    Any   ):
										__lowerCamelCase          =      NystromformerModel(config=snake_case_   )
										model.to(snake_case_   )
										model.eval()
										__lowerCamelCase          =      model(snake_case_     ,							attention_mask=snake_case_     ,							token_type_ids=snake_case_   )
										__lowerCamelCase          =      model(snake_case_     ,							token_type_ids=snake_case_   )
										__lowerCamelCase          =      model(snake_case_   )
										self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)   )




							def   __a   (     self:    Optional[int]     ,							A_:    int     ,							A_:    List[Any]     ,							A_:    Dict     ,							A_:    int     ,							A_:    str     ,							A_:    List[Any]     ,							A_:    List[Any]   ):
										__lowerCamelCase          =      NystromformerForMaskedLM(config=snake_case_   )
										model.to(snake_case_   )
										model.eval()
										__lowerCamelCase          =      model(snake_case_     ,							attention_mask=snake_case_     ,							token_type_ids=snake_case_     ,							labels=snake_case_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)   )




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




							def   __a   (     self:    List[Any]     ,							A_:    str     ,							A_:    Optional[Any]     ,							A_:    Dict     ,							A_:    Any     ,							A_:    Optional[int]     ,							A_:    int     ,							A_:    Optional[int]   ):
										__lowerCamelCase          =      self.num_labels
										__lowerCamelCase          =      NystromformerForTokenClassification(config=snake_case_   )
										model.to(snake_case_   )
										model.eval()
										__lowerCamelCase          =      model(snake_case_     ,							attention_mask=snake_case_     ,							token_type_ids=snake_case_     ,							labels=snake_case_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.num_labels)   )




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




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.prepare_config_and_inputs()
										(
										    (
										    __lowerCamelCase			
										)     ,(
										    __lowerCamelCase			
										)     ,(
										    __lowerCamelCase			
										)     ,(
										    __lowerCamelCase			
										)     ,(
										    __lowerCamelCase			
										)     ,(
										    __lowerCamelCase			
										)     ,(
										    __lowerCamelCase			
										)     ,			
										)          =      config_and_inputs
										__lowerCamelCase          =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict




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




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      NystromformerModelTester(self   )
										__lowerCamelCase          =      ConfigTester(self     ,							config_class=snake_case_     ,							hidden_size=37   )




							def   __a   (     self:    int   ):
										self.config_tester.run_common_tests()




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
													__lowerCamelCase          =      type
													self.model_tester.create_and_check_model(*snake_case_   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*snake_case_   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*snake_case_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*snake_case_   )




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*snake_case_   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*snake_case_   )




							@slow
							def   __a   (     self:    List[Any]   ):

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




@require_torch
class 			__snake_case   (unittest.TestCase  ):




							@slow
							def   __a   (     self:    int   ):
										__lowerCamelCase          =      NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""   )
										__lowerCamelCase          =      torch.tensor([[0, 1, 2, 3, 4, 5]]   )

										with torch.no_grad():
													__lowerCamelCase          =      model(snake_case_   )[0]

										__lowerCamelCase          =      torch.Size((1, 6, 7_68)   )
										self.assertEqual(output.shape     ,							snake_case_   )

										__lowerCamelCase          =      torch.tensor(
										    [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]   )

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




							@slow
							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      """the [MASK] of Belgium is Brussels"""

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

										__lowerCamelCase          =      tokenizer(snake_case_     ,							return_tensors="""pt"""   )

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

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

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








import doctest
from collections import deque

import numpy as np
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self    ) ->      None:







								'''simple docstring'''

								__lowercase			       = [2, 1, 2, -1]
								__lowercase			       = [1, 2, 3, 4]






					def A   (					self    ) ->      list[float]:







								'''simple docstring'''

								__lowercase			       = len(self.first_signal    )
								__lowercase			       = len(self.second_signal    )

								__lowercase			       = max(snake_case_			,							snake_case_    )

								# create a zero matrix of max_length x max_length
								__lowercase			       = [[0] * max_length for i in range(snake_case_    )]

								# fills the smaller signal with zeros to make both signals of same length
								if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
								elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
								for i in range(snake_case_    ):
											__lowercase			       = deque(self.second_signal    )
											rotated_signal.rotate(snake_case_    )
											for j, item in enumerate(snake_case_    ):
														matrix[i][j] += item

        # multiply the matrix with the first signal
								__lowercase			       = np.matmul(np.transpose(snake_case_    )			,							np.transpose(self.first_signal    )    )

								# rounding-off to two decimal places
								return [round(snake_case_			,							2    ) for i in final_signal]


if __name__ == "__main__":
	doctest.testmod()


