





"""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  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=13		,SCREAMING_SNAKE_CASE__=7		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=99		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=5		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=37		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=None		,)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  parent
             __SCREAMING_SNAKE_CASE       :Tuple		    =  batch_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  seq_length
             __SCREAMING_SNAKE_CASE       :Any		    =  is_training
             __SCREAMING_SNAKE_CASE       :Tuple		    =  use_input_mask
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  use_token_type_ids
             __SCREAMING_SNAKE_CASE       :int		    =  use_labels
             __SCREAMING_SNAKE_CASE       :Dict		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :int		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :int		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Any		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :str		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :List[str]		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :int		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Any		    =  type_vocab_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  type_sequence_label_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_labels
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_choices
             __SCREAMING_SNAKE_CASE       :str		    =  scope




       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None
             if self.use_input_mask:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  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       :Tuple		    =  ids_tensor([self.batch_size, self.seq_length]		,self.type_vocab_size							)

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

             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_config()

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




       def    _UpperCamelCase					(						self							)      ->				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=SCREAMING_SNAKE_CASE__		,initializer_range=self.initializer_range		,)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  NystromformerModel(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Any		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  model(SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  model(SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model(
                 SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,start_positions=SCREAMING_SNAKE_CASE__		,end_positions=SCREAMING_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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.num_labels
             __SCREAMING_SNAKE_CASE       :Any		    =  NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Dict		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.num_labels
             __SCREAMING_SNAKE_CASE       :Tuple		    =  NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Any		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  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
             )    ,
             )       :Dict		    =  config_and_inputs
             __SCREAMING_SNAKE_CASE       :str		    =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_torch
class  _SCREAMING_SNAKE_CASE(       A   ,     A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  (
           (
               NystromformerModel,
               NystromformerForMaskedLM,
               NystromformerForMultipleChoice,
               NystromformerForQuestionAnswering,
               NystromformerForSequenceClassification,
               NystromformerForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  (
           {
               '''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_       :				str     						=  False
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  False




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  NystromformerModelTester(self							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  ConfigTester(self		,config_class=SCREAMING_SNAKE_CASE__		,hidden_size=37							)




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				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(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)






@require_torch
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       @slow
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






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

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE       :str		    =  model(SCREAMING_SNAKE_CASE__							)[0]

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.Size((1, 6, 7_68)							)
             self.assertEqual(output.shape		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  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]		,SCREAMING_SNAKE_CASE__		,atol=1E-4							)							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  '''the [MASK] of Belgium is Brussels'''

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''							)
             __SCREAMING_SNAKE_CASE       :str		    =  NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer(SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model(encoding.input_ids							).logits

             __SCREAMING_SNAKE_CASE       :List[str]		    =  token_logits[:, 2, :].argmax(-1							)[0]

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






"""simple docstring"""
import colorsys

from PIL import Image  # type: ignore



def 					__lowerCamelCase       (							a_					:		float			,       a_					:		float			,       a_					:		int							)      ->				float:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  x
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  y
      for step in range(a_							):  # noqa: B007
            __SCREAMING_SNAKE_CASE       :Dict		    =  a * a - b * b + x
            __SCREAMING_SNAKE_CASE       :Tuple		    =  2 * a * b + y
            __SCREAMING_SNAKE_CASE       :Dict		    =  a_new

            # divergence happens for all complex number with an absolute value
            # greater than 4
            if a * a + b * b > 4:
                  break
      return step / (max_step - 1)



def 					__lowerCamelCase       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return (2_55, 2_55, 2_55)



def 					__lowerCamelCase       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 2_55							) for i in colorsys.hsv_to_rgb(a_			,       1			,       1							)							)



def 					__lowerCamelCase       (							a_					:		int = 8_00			,       a_					:		int = 6_00			,       a_					:		float = -0.6			,       a_					:		float = 0			,       a_					:		float = 3.2			,       a_					:		int = 50			,       a_					:		bool = True			,       )      ->				Image.Image:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  Image.new('''RGB'''			,       (image_width, image_height)							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  img.load()

      # loop through the image-coordinates
      for image_x in range(a_							):
            for image_y in range(a_							):
                  # determine the figure-coordinates based on the image-coordinates
                  __SCREAMING_SNAKE_CASE       :Dict		    =  figure_width / image_width * image_height
                  __SCREAMING_SNAKE_CASE       :str		    =  figure_center_x + (image_x / image_width - 0.5) * figure_width
                  __SCREAMING_SNAKE_CASE       :Tuple		    =  figure_center_y + (image_y / image_height - 0.5) * figure_height

                  __SCREAMING_SNAKE_CASE       :List[Any]		    =  get_distance(a_			,       a_			,       a_							)

                  # color the corresponding pixel based on the selected coloring-function
                  if use_distance_color_coding:
                        __SCREAMING_SNAKE_CASE       :Optional[int]		    =  get_color_coded_rgb(a_							)
                  else:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  get_black_and_white_rgb(a_							)

      return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  lowerCamelCase_       =		get_image()

  # uncomment for colored version, different section, zoomed in
  # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
  # figure_width = 0.8)

  # uncomment for black and white version, full figure
  # img = get_image(use_distance_color_coding = False)

  # uncomment to save the image
  # img.save("mandelbrot.png")

  img.show()