

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import BridgeTowerImageProcessor




class      UpperCamelCase (     unittest.TestCase   ):

    def __init__(			self :      Dict							,    snake_case__ :      List[str]							,    snake_case__ :      bool = True							,    snake_case__ :      Dict[str, int] = None							,    snake_case__ :      int = 3_2							,    snake_case__ :      bool = True							,    snake_case__ :      Union[int, float] = 1 / 2_5_5							,    snake_case__ :      bool = True							,    snake_case__ :      bool = True							,    snake_case__ :      Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]							,    snake_case__ :      Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]							,    snake_case__ :      bool = True							,    snake_case__ :      List[Any]=7							,    snake_case__ :      Optional[int]=3_0							,    snake_case__ :      Tuple=4_0_0							,    snake_case__ :      Any=3							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			do_resize
     SCREAMING_SNAKE_CASE								=			size if size is not None else {'shortest_edge': 2_8_8}
     SCREAMING_SNAKE_CASE								=			size_divisor
     SCREAMING_SNAKE_CASE								=			do_rescale
     SCREAMING_SNAKE_CASE								=			rescale_factor
     SCREAMING_SNAKE_CASE								=			do_normalize
     SCREAMING_SNAKE_CASE								=			do_center_crop
     SCREAMING_SNAKE_CASE								=			image_mean
     SCREAMING_SNAKE_CASE								=			image_std
     SCREAMING_SNAKE_CASE								=			do_pad
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			min_resolution
     SCREAMING_SNAKE_CASE								=			max_resolution

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "size_divisor": self.size_divisor,
     }




    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]=False    ):

     """simple docstring"""






     if not batched:
      SCREAMING_SNAKE_CASE								=			self.size['shortest_edge']
      SCREAMING_SNAKE_CASE								=			image_inputs[0]
      if isinstance(snake_case__							,    Image.Image    ):
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			image.size
      else:
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			image.shape[1], image.shape[2]
      SCREAMING_SNAKE_CASE								=			size / min(snake_case__							,    snake_case__    )
      if h < w:
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			size, scale * w
      else:
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			scale * h, size

      SCREAMING_SNAKE_CASE								=			int((1_3_3_3 / 8_0_0) * size    )
      if max(snake_case__							,    snake_case__    ) > max_size:
       SCREAMING_SNAKE_CASE								=			max_size / max(snake_case__							,    snake_case__    )
       SCREAMING_SNAKE_CASE								=			newh * scale
       SCREAMING_SNAKE_CASE								=			neww * scale

      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			int(newh + 0.5    ), int(neww + 0.5    )
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			(
          newh // self.size_divisor * self.size_divisor,
          neww // self.size_divisor * self.size_divisor,
      )

     else:
      SCREAMING_SNAKE_CASE								=			[]
      for image in image_inputs:
       SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.get_expected_values([image]    )
       expected_values.append((expected_height, expected_width)    )
      SCREAMING_SNAKE_CASE								=			max(snake_case__							,    key=lambda snake_case__    : item[0]    )[0]
      SCREAMING_SNAKE_CASE								=			max(snake_case__							,    key=lambda snake_case__    : item[1]    )[1]

     return expected_height, expected_width




@require_torch
@require_vision
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =BridgeTowerImageProcessor if is_vision_available() else None

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BridgeTowerImageProcessingTester(self    )

    @property
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processing_class(**self.image_processor_dict    )
     self.assertTrue(hasattr(snake_case__							,    'image_mean'    )    )
     self.assertTrue(hasattr(snake_case__							,    'image_std'    )    )
     self.assertTrue(hasattr(snake_case__							,    'do_normalize'    )    )
     self.assertTrue(hasattr(snake_case__							,    'do_resize'    )    )
     self.assertTrue(hasattr(snake_case__							,    'size'    )    )
     self.assertTrue(hasattr(snake_case__							,    'size_divisor'    )    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     pass

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processing_class(**self.image_processor_dict    )
     # create random PIL images
     SCREAMING_SNAKE_CASE								=			prepare_image_inputs(self.image_processor_tester							,    equal_resolution=snake_case__    )
     for image in image_inputs:
      self.assertIsInstance(snake_case__							,    Image.Image    )

     # Test not batched input
     SCREAMING_SNAKE_CASE								=			image_processing(image_inputs[0]							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.image_processor_tester.get_expected_values(snake_case__    )
     self.assertEqual(
         encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

     # Test batched
     SCREAMING_SNAKE_CASE								=			image_processing(snake_case__							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.image_processor_tester.get_expected_values(snake_case__							,    batched=snake_case__    )
     self.assertEqual(
         encoded_images.shape							,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processing_class(**self.image_processor_dict    )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE								=			prepare_image_inputs(self.image_processor_tester							,    equal_resolution=snake_case__							,    numpify=snake_case__    )
     for image in image_inputs:
      self.assertIsInstance(snake_case__							,    np.ndarray    )

     # Test not batched input
     SCREAMING_SNAKE_CASE								=			image_processing(image_inputs[0]							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.image_processor_tester.get_expected_values(snake_case__    )
     self.assertEqual(
         encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

     # Test batched
     SCREAMING_SNAKE_CASE								=			image_processing(snake_case__							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.image_processor_tester.get_expected_values(snake_case__							,    batched=snake_case__    )
     self.assertEqual(
         encoded_images.shape							,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,    )




    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processing_class(**self.image_processor_dict    )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE								=			prepare_image_inputs(self.image_processor_tester							,    equal_resolution=snake_case__							,    torchify=snake_case__    )
     for image in image_inputs:
      self.assertIsInstance(snake_case__							,    torch.Tensor    )

     # Test not batched input
     SCREAMING_SNAKE_CASE								=			image_processing(image_inputs[0]							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.image_processor_tester.get_expected_values(snake_case__    )
     self.assertEqual(
         encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

     # Test batched
     SCREAMING_SNAKE_CASE								=			image_processing(snake_case__							,    return_tensors='pt'    ).pixel_values

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.image_processor_tester.get_expected_values(snake_case__							,    batched=snake_case__    )
     self.assertEqual(
         encoded_images.shape							,    (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,    )




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 :

    def __init__(			self :      List[str]							,    snake_case__ :      Dict							,    snake_case__ :      Optional[Any]=1_3							,    snake_case__ :      Union[str, Any]=7							,    snake_case__ :      List[str]=True							,    snake_case__ :      Any=True							,    snake_case__ :      List[str]=True							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      List[str]=9_9							,    snake_case__ :      str=3_2							,    snake_case__ :      Dict=5							,    snake_case__ :      str=4							,    snake_case__ :      int=3_7							,    snake_case__ :      Union[str, Any]="gelu"							,    snake_case__ :      Any=0.1							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Optional[Any]=5_1_2							,    snake_case__ :      List[Any]=1_6							,    snake_case__ :      str=2							,    snake_case__ :      int=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      Dict=4							,    snake_case__ :      str=None							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






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

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

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

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

     SCREAMING_SNAKE_CASE								=			self.get_config()

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

    def UpperCamelCase			(			self :      Optional[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=snake_case__							,    initializer_range=self.initializer_range							,    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Tuple							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Dict							,    snake_case__ :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[Any]							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForQuestionAnswering(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Tuple							,    snake_case__ :      List[str]							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForTokenClassification(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_choices
     SCREAMING_SNAKE_CASE								=			NystromformerForMultipleChoice(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			input_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			token_type_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			input_mask.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






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




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

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case__    )

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case__    )




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

     """simple docstring"""






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




@require_torch
class      UpperCamelCase (     unittest.TestCase   ):

    @slow
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






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

     with torch.no_grad():
      SCREAMING_SNAKE_CASE								=			model(snake_case__    )[0]

     SCREAMING_SNAKE_CASE								=			torch.Size((1, 6, 7_6_8)    )
     self.assertEqual(output.shape							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'the [MASK] of Belgium is Brussels'

     SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'    )
     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'    )

     SCREAMING_SNAKE_CASE								=			tokenizer(snake_case__							,    return_tensors='pt'    )

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

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

     self.assertEqual(tokenizer.decode(snake_case__    )							,    'capital'    )

