

'''simple docstring'''





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


UpperCamelCase_    						=       logging.get_logger(__name__)

enable_full_determinism()





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    UNetaDModel
				lowerCamelCase_		       =    'sample'







				@property
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=4
										lowercase :							Tuple    		=3
										lowercase :							List[str]    		=(32, 32)

										lowercase :							Dict    		=floats_tensor((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Any    		=torch.tensor([10]   ).to(UpperCAmelCase__   )

										return {"sample": noise, "timestep": time_step}







				@property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Dict    		={
										    '''block_out_channels''': (32, 64),
										    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
										    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
										    '''attention_head_dim''': 3,
										    '''out_channels''': 3,
										    '''in_channels''': 3,
										    '''layers_per_block''': 2,
										    '''sample_size''': 32,
										}
										lowercase :							int    		=self.dummy_input
										return init_dict, inputs_dict





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    UNetaDModel
				lowerCamelCase_		       =    'sample'







				@property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=4
										lowercase :							Any    		=4
										lowercase :							Any    		=(32, 32)

										lowercase :							List[Any]    		=floats_tensor((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=torch.tensor([10]   ).to(UpperCAmelCase__   )

										return {"sample": noise, "timestep": time_step}







				@property
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return (4, 32, 32)







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return (4, 32, 32)







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Any    		={
										    '''sample_size''': 32,
										    '''in_channels''': 4,
										    '''out_channels''': 4,
										    '''layers_per_block''': 2,
										    '''block_out_channels''': (32, 64),
										    '''attention_head_dim''': 32,
										    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
										    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
										}
										lowercase :							str    		=self.dummy_input
										return init_dict, inputs_dict







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Dict    		=UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=UpperCAmelCase__   )

										self.assertIsNotNone(UpperCAmelCase__   )
										self.assertEqual(len(loading_info['''missing_keys''']   )		,					0   )

										model.to(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=model(**self.dummy_input   ).sample

										assert image is not None, "Make sure output is not None"







				@unittest.skipIf(torch_device != '''cuda'''		,					'''This test is supposed to run on GPU'''   )
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							List[Any]    		=UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=model(**self.dummy_input   ).sample

										assert image is not None, "Make sure output is not None"







				@unittest.skipIf(torch_device != '''cuda'''		,					'''This test is supposed to run on GPU'''   )
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
										lowercase		,	lowercase :							Tuple    		=UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=UpperCAmelCase__   )
										model_accelerate.to(UpperCAmelCase__   )
										model_accelerate.eval()

										lowercase :							int    		=torch.randn(
										    1		,					model_accelerate.config.in_channels		,					model_accelerate.config.sample_size		,					model_accelerate.config.sample_size		,					generator=torch.manual_seed(0   )		,					)
										lowercase :							Tuple    		=noise.to(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=torch.tensor([10] * noise.shape[0]   ).to(UpperCAmelCase__   )

										lowercase :							Dict    		=model_accelerate(UpperCAmelCase__		,					UpperCAmelCase__   )['''sample''']

										# two models don't need to stay in the device at the same time
										del model_accelerate
										torch.cuda.empty_cache()
										gc.collect()

										lowercase		,	lowercase :							Optional[Any]    		=UNetaDModel.from_pretrained(
										    '''fusing/unet-ldm-dummy-update'''		,					output_loading_info=UpperCAmelCase__		,					low_cpu_mem_usage=UpperCAmelCase__   )
										model_normal_load.to(UpperCAmelCase__   )
										model_normal_load.eval()
										lowercase :							List[str]    		=model_normal_load(UpperCAmelCase__		,					UpperCAmelCase__   )['''sample''']

										assert torch_all_close(UpperCAmelCase__		,					UpperCAmelCase__		,					rtol=1E-3   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   )
										model.eval()
										model.to(UpperCAmelCase__   )

										lowercase :							Dict    		=torch.randn(
										    1		,					model.config.in_channels		,					model.config.sample_size		,					model.config.sample_size		,					generator=torch.manual_seed(0   )		,					)
										lowercase :							List[Any]    		=noise.to(UpperCAmelCase__   )
										lowercase :							Dict    		=torch.tensor([10] * noise.shape[0]   ).to(UpperCAmelCase__   )

										with torch.no_grad():
																lowercase :							Optional[int]    		=model(UpperCAmelCase__		,					UpperCAmelCase__   ).sample

										lowercase :							List[str]    		=output[0, -1, -3:, -3:].flatten().cpu()
										# fmt: off
										lowercase :							Tuple    		=torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]   )
										# fmt: on

										self.assertTrue(torch_all_close(UpperCAmelCase__		,					UpperCAmelCase__		,					rtol=1E-3   )   )





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    UNetaDModel
				lowerCamelCase_		       =    'sample'







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Optional[Any]=(32, 32)   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=4
										lowercase :							Dict    		=3

										lowercase :							Optional[Any]    		=floats_tensor((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Dict    		=torch.tensor(batch_size * [10]   ).to(dtype=torch.intaa		,					device=UpperCAmelCase__   )

										return {"sample": noise, "timestep": time_step}







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				@property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		={
										    '''block_out_channels''': [32, 64, 64, 64],
										    '''in_channels''': 3,
										    '''layers_per_block''': 1,
										    '''out_channels''': 3,
										    '''time_embedding_type''': '''fourier''',
										    '''norm_eps''': 1E-6,
										    '''mid_block_scale_factor''': math.sqrt(2.0   ),
										    '''norm_num_groups''': None,
										    '''down_block_types''': [
										        '''SkipDownBlock2D''',
										        '''AttnSkipDownBlock2D''',
										        '''SkipDownBlock2D''',
										        '''SkipDownBlock2D''',
										    ],
										    '''up_block_types''': [
										        '''SkipUpBlock2D''',
										        '''SkipUpBlock2D''',
										        '''AttnSkipUpBlock2D''',
										        '''SkipUpBlock2D''',
										    ],
										}
										lowercase :							Union[str, Any]    		=self.dummy_input
										return init_dict, inputs_dict







				@slow
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Dict    		=UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''		,					output_loading_info=UpperCAmelCase__   )
										self.assertIsNotNone(UpperCAmelCase__   )
										self.assertEqual(len(loading_info['''missing_keys''']   )		,					0   )

										model.to(UpperCAmelCase__   )
										lowercase :							int    		=self.dummy_input
										lowercase :							Tuple    		=floats_tensor((4, 3) + (256, 256)   ).to(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=noise
										lowercase :							List[str]    		=model(**UpperCAmelCase__   )

										assert image is not None, "Make sure output is not None"







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							str    		=UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''   )
										model.to(UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=4
										lowercase :							Tuple    		=3
										lowercase :							int    		=(256, 256)

										lowercase :							Optional[int]    		=torch.ones((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=torch.tensor(batch_size * [1E-4]   ).to(UpperCAmelCase__   )

										with torch.no_grad():
																lowercase :							Dict    		=model(UpperCAmelCase__		,					UpperCAmelCase__   ).sample

										lowercase :							Optional[Any]    		=output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowercase :							int    		=torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]   )
										# fmt: on

										self.assertTrue(torch_all_close(UpperCAmelCase__		,					UpperCAmelCase__		,					rtol=1E-2   )   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''   )
										model.to(UpperCAmelCase__   )

										lowercase :							List[Any]    		=4
										lowercase :							List[str]    		=3
										lowercase :							Optional[Any]    		=(32, 32)

										lowercase :							Dict    		=torch.ones((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=torch.tensor(batch_size * [1E-4]   ).to(UpperCAmelCase__   )

										with torch.no_grad():
																lowercase :							int    		=model(UpperCAmelCase__		,					UpperCAmelCase__   ).sample

										lowercase :							List[Any]    		=output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowercase :							str    		=torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]   )
										# fmt: on

										self.assertTrue(torch_all_close(UpperCAmelCase__		,					UpperCAmelCase__		,					rtol=1E-2   )   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										# not required for this model
										pass












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				,     snake_case_				,     snake_case_=13				,     snake_case_=7				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=True				,     snake_case_=99				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=37				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=16				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=3				,     snake_case_=4				,     snake_case_=None				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							seq_length
         _A     =							is_training
         _A     =							use_input_mask
         _A     =							use_token_type_ids
         _A     =							use_labels
         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							intermediate_size
         _A     =							hidden_act
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							type_vocab_size
         _A     =							type_sequence_label_size
         _A     =							initializer_range
         _A     =							num_labels
         _A     =							num_choices
         _A     =							scope




     def        lowerCAmelCase__		(    self							):
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size							)

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

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

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

         _A     =							self.get_config()

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




     def        lowerCAmelCase__		(    self							):
         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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerModel(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     attention_mask=snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_				,     token_type_ids=snake_case_							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForMaskedLM(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							NystromformerForQuestionAnswering(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForSequenceClassification(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							NystromformerForTokenClassification(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_choices
         _A     =							NystromformerForMultipleChoice(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							input_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							token_type_ids.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							input_mask.unsqueeze(1							).expand(-1				,     self.num_choices				,     -1							).contiguous()
         _A     =							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        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							config_and_inputs
         _A     =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


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




     def        lowerCAmelCase__		(    self							):
         _A     =							NystromformerModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.config_tester.run_common_tests()




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							NystromformerModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)







@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


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

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

         _A     =							torch.Size((1, 6, 768)							)
         self.assertEqual(output.shape				,     snake_case_							)

         _A     =							torch.tensor(
             [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]							)

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




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							'the [MASK] of Belgium is Brussels'

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

         _A     =							tokenizer(snake_case_				,     return_tensors='pt'							)

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

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

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





