


'''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   __lowerCAmelCase						:





      '''simple docstring'''






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

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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

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

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

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

             lowercase__      			= self.get_config()

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



      def       UpperCamelCase__		(self    :    str ):

             '''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=UpperCamelCase   ,						initializer_range=self.initializer_range   ,						)



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    int ):

             '''simple docstring'''







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



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

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= NystromformerForQuestionAnswering(config=UpperCamelCase )
             model.to(UpperCamelCase )
             model.eval()
             lowercase__      			= model(
                 UpperCamelCase   ,						attention_mask=UpperCamelCase   ,						token_type_ids=UpperCamelCase   ,						start_positions=UpperCamelCase   ,						end_positions=UpperCamelCase   ,						)
             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    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any] ):

             '''simple docstring'''







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



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

             '''simple docstring'''







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



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

             '''simple docstring'''







             lowercase__      			= self.num_choices
             lowercase__      			= NystromformerForMultipleChoice(config=UpperCamelCase )
             model.to(UpperCamelCase )
             model.eval()
             lowercase__      			= input_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
             lowercase__      			= token_type_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
             lowercase__      			= input_mask.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
             lowercase__      			= model(
                 UpperCamelCase   ,						attention_mask=UpperCamelCase   ,						token_type_ids=UpperCamelCase   ,						labels=UpperCamelCase   ,						)
             self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_choices) )






      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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






@require_torch
class   __lowerCAmelCase						(lowercase_  ,     lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Any  					=							(
          (
              NystromformerModel,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__					:     Dict  					=							(
          {
              """feature-extraction""": NystromformerModel,
              """fill-mask""": NystromformerForMaskedLM,
              """question-answering""": NystromformerForQuestionAnswering,
              """text-classification""": NystromformerForSequenceClassification,
              """token-classification""": NystromformerForTokenClassification,
              """zero-shot""": NystromformerForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__					:     Tuple  					=							False
      lowerCAmelCase__					:     Dict  					=							False



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= NystromformerModelTester(self )
             lowercase__      			= ConfigTester(self   ,						config_class=UpperCamelCase   ,						hidden_size=37 )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase )






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

             '''simple docstring'''







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






@require_torch
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      @slow
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







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

             with torch.no_grad():
                    lowercase__      			= model(UpperCamelCase )[0]

             lowercase__      			= torch.Size((1, 6, 768) )
             self.assertEqual(output.shape   ,						UpperCamelCase )

             lowercase__      			= torch.tensor(
                 [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]] )

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






      @slow
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







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

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

             lowercase__      			= tokenizer(UpperCamelCase   ,						return_tensors='''pt''' )

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

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

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








'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         if dst_width < 0 or dst_height < 0:
              raise ValueError('Destination width/height should be > 0'	)

         __lowerCamelCase   :	Dict								=		img
         __lowerCamelCase   :	Any								=		img.shape[1]
         __lowerCamelCase   :	Optional[int]								=		img.shape[0]
         __lowerCamelCase   :	Dict								=		dst_width
         __lowerCamelCase   :	str								=		dst_height

         __lowerCamelCase   :	Dict								=		self.src_w / self.dst_w
         __lowerCamelCase   :	List[Any]								=		self.src_h / self.dst_h

         __lowerCamelCase   :	Optional[int]								=		(
             np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta	) * 2_55
         )







    def       lowercase_				(							self	)				->     List[Any]:
         for i in range(self.dst_h	):
              for j in range(self.dst_w	):
                   __lowerCamelCase   :	Union[str, Any]								=		self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_x * x	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_y * y	)


if __name__ == "__main__":
      A__						,					A__		:					Optional[Any]												=  800, 600
      A__		:					List[str]												=  imread("""image_data/lena.jpg""", 1)
      A__		:					List[Any]												=  NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
      )
      waitKey(0)
      destroyAllWindows()






