






'''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 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     List[str]				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     List[Any]=13				,     __lowerCamelCase     :     Any=7				,     __lowerCamelCase     :     Tuple=True				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     int=True				,     __lowerCamelCase     :     Tuple=99				,     __lowerCamelCase     :     str=32				,     __lowerCamelCase     :     Union[str, Any]=5				,     __lowerCamelCase     :     str=4				,     __lowerCamelCase     :     List[Any]=37				,     __lowerCamelCase     :     Optional[int]="gelu"				,     __lowerCamelCase     :     int=0.1				,     __lowerCamelCase     :     Dict=0.1				,     __lowerCamelCase     :     List[str]=512				,     __lowerCamelCase     :     int=16				,     __lowerCamelCase     :     Tuple=2				,     __lowerCamelCase     :     Dict=0.0_2				,     __lowerCamelCase     :     Optional[Any]=3				,     __lowerCamelCase     :     int=4				,     __lowerCamelCase     :     Optional[Any]=None				,     )							->							str:

     '''simple docstring'''




     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     :     Dict     )							->							Dict:

     '''simple docstring'''




     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     :     List[Any]     )							->							Dict:

     '''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=__lowerCamelCase				,     initializer_range=self.initializer_range				,     )

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     str     )							->							int:

     '''simple docstring'''




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

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Union[str, Any]     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     NystromformerForMaskedLM(config=__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     token_type_ids=__lowerCamelCase				,     labels=__lowerCamelCase     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     int     )							->							Any:

     '''simple docstring'''




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

     '''simple docstring'''




     lowerCamelCase__  =     self.num_labels
     lowerCamelCase__  =     NystromformerForSequenceClassification(__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     token_type_ids=__lowerCamelCase				,     labels=__lowerCamelCase     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)     )

    def    a__				(					self     :     str				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     self.num_labels
     lowerCamelCase__  =     NystromformerForTokenClassification(config=__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     model.eval()
     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     token_type_ids=__lowerCamelCase				,     labels=__lowerCamelCase     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)     )

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     int     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     self.num_choices
     lowerCamelCase__  =     NystromformerForMultipleChoice(config=__lowerCamelCase     )
     model.to(__lowerCamelCase     )
     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(
         __lowerCamelCase				,     attention_mask=__lowerCamelCase				,     token_type_ids=__lowerCamelCase				,     labels=__lowerCamelCase				,     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)     )




    def    a__				(					self     :     int     )							->							List[str]:

     '''simple docstring'''




     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 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase ,       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    a__				(					self     :     Optional[int]     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     NystromformerModelTester(self     )
     lowerCamelCase__  =     ConfigTester(self				,     config_class=__lowerCamelCase				,     hidden_size=37     )

    def    a__				(					self     :     Optional[int]     )							->							List[str]:

     '''simple docstring'''




     self.config_tester.run_common_tests()

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

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase     )

    def    a__				(					self     :     int     )							->							int:

     '''simple docstring'''




     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(*__lowerCamelCase     )

    def    a__				(					self     :     List[Any]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase     )

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

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase     )

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

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase     )

    def    a__				(					self     :     List[Any]     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase     )

    def    a__				(					self     :     Dict     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase     )




    @slow
    def    a__				(					self     :     List[str]     )							->							int:

     '''simple docstring'''




     for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      lowerCamelCase__  =     NystromformerModel.from_pretrained(__lowerCamelCase     )
      self.assertIsNotNone(__lowerCamelCase     )
@require_torch
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    @slow
    def    a__				(					self     :     Tuple     )							->							Tuple:

     '''simple docstring'''




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

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

     lowerCamelCase__  =     torch.Size((1, 6, 768)     )
     self.assertEqual(output.shape				,     __lowerCamelCase     )

     lowerCamelCase__  =     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]				,     __lowerCamelCase				,     atol=1E-4     )     )




    @slow
    def    a__				(					self     :     str     )							->							str:

     '''simple docstring'''




     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(__lowerCamelCase				,     return_tensors="pt"     )

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

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

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









'''simple docstring'''





from __future__ import annotations
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     order

     # a_{0} ... a_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order
     # b_{0} ... b_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     lowerCamelCase__  =     [0.0] * self.order
     # y[n-1] ... y[n-k]
     lowerCamelCase__  =     [0.0] * self.order

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     list[float]				,     __lowerCamelCase     :     list[float]     )							->							None:

     '''simple docstring'''




     if len(__lowerCamelCase     ) < self.order:
      lowerCamelCase__  =     [1.0, *a_coeffs]

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected a_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected b_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     lowerCamelCase__  =     a_coeffs
     lowerCamelCase__  =     b_coeffs




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     float     )							->							float:

     '''simple docstring'''




     lowerCamelCase__  =     0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1				,     self.order + 1     ):
      result += (
          self.b_coeffs[i] * self.input_history[i - 1]
          - self.a_coeffs[i] * self.output_history[i - 1]
      )

     lowerCamelCase__  =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     lowerCamelCase__  =     self.input_history[:-1]
     lowerCamelCase__  =     self.output_history[:-1]

     lowerCamelCase__  =     sample
     lowerCamelCase__  =     result

     return result

