


'''simple docstring'''



import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase    :Union[str, Any]				       =     1_6
__UpperCAmelCase    :Any				       =     3_2




def 			_a			(		_lowercase      :			List[Any]    ,      _lowercase      :			Any = 16						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    AutoTokenizer.from_pretrained('''bert-base-cased'''						)
     __UpperCAmelCase			:				List[str]													=    load_dataset('''glue'''    ,      '''mrpc'''						)

     def tokenize_function(_lowercase      :			Tuple						):
          # max_length=None => use the model max length (it's actually the default)
          __UpperCAmelCase			:				List[Any]													=    tokenizer(examples['''sentence1''']    ,      examples['''sentence2''']    ,      truncation=lowerCAmelCase_    ,      max_length=lowerCAmelCase_						)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          __UpperCAmelCase			:				str													=    datasets.map(
              lowerCAmelCase_    ,      batched=lowerCAmelCase_    ,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,      )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     __UpperCAmelCase			:				int													=    tokenized_datasets.rename_column('''label'''    ,      '''labels'''						)

     def collate_fn(_lowercase      :			Dict						):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __UpperCAmelCase			:				List[str]													=    128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __UpperCAmelCase			:				Dict													=    16
          elif accelerator.mixed_precision != "no":
               __UpperCAmelCase			:				Optional[int]													=    8
          else:
               __UpperCAmelCase			:				Optional[Any]													=    None

          return tokenizer.pad(
              lowerCAmelCase_    ,      padding='''longest'''    ,      max_length=lowerCAmelCase_    ,      pad_to_multiple_of=lowerCAmelCase_    ,      return_tensors='''pt'''    ,      )

     # Instantiate dataloaders.
     __UpperCAmelCase			:				Optional[int]													=    DataLoader(
         tokenized_datasets['''train''']    ,      shuffle=lowerCAmelCase_    ,      collate_fn=lowerCAmelCase_    ,      batch_size=lowerCAmelCase_						)
     __UpperCAmelCase			:				str													=    DataLoader(
         tokenized_datasets['''validation''']    ,      shuffle=lowerCAmelCase_    ,      collate_fn=lowerCAmelCase_    ,      batch_size=lowerCAmelCase_						)

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __UpperCAmelCase    :Union[str, Any]				       =     mocked_dataloaders  # noqa: F811




def 			_a			(		_lowercase      :			Optional[int]    ,      _lowercase      :			List[str]						):





     '''simple docstring'''






     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,      lowerCAmelCase_						) == "1":
          __UpperCAmelCase			:				List[str]													=    2
     # Initialize accelerator
     __UpperCAmelCase			:				List[Any]													=    Accelerator(cpu=args.cpu    ,      mixed_precision=args.mixed_precision						)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __UpperCAmelCase			:				Dict													=    config["lr"]
     __UpperCAmelCase			:				Dict													=    int(config['''num_epochs''']						)
     __UpperCAmelCase			:				int													=    int(config['''seed''']						)
     __UpperCAmelCase			:				Union[str, Any]													=    int(config['''batch_size''']						)

     __UpperCAmelCase			:				List[str]													=    evaluate.load('''glue'''    ,      '''mrpc'''						)

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=lowerCAmelCase_						)
     def inner_training_loop(_lowercase      :			Optional[Any]						):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(lowerCAmelCase_						)

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          __UpperCAmelCase			:				List[Any]													=    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,      return_dict=lowerCAmelCase_						)

          # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
          # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
          # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
          __UpperCAmelCase			:				int													=    model.to(accelerator.device						)

          # Instantiate optimizer
          __UpperCAmelCase			:				Dict													=    AdamW(params=model.parameters()    ,      lr=lowerCAmelCase_						)
          __UpperCAmelCase			:				Dict													=    get_dataloaders(lowerCAmelCase_    ,      lowerCAmelCase_						)

          # Instantiate scheduler
          __UpperCAmelCase			:				str													=    get_linear_schedule_with_warmup(
              optimizer=lowerCAmelCase_    ,      num_warmup_steps=100    ,      num_training_steps=(len(lowerCAmelCase_						) * num_epochs)    ,      )

          # Prepare everything
          # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
          # prepare method.
          __UpperCAmelCase			:				List[Any]													=    accelerator.prepare(
              lowerCAmelCase_    ,      lowerCAmelCase_    ,      lowerCAmelCase_    ,      lowerCAmelCase_    ,      lowerCAmelCase_						)

          # Now we train the model
          for epoch in range(lowerCAmelCase_						):
               model.train()
               for step, batch in enumerate(lowerCAmelCase_						):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device						)
                    __UpperCAmelCase			:				Tuple													=    model(**lowerCAmelCase_						)
                    __UpperCAmelCase			:				str													=    outputs.loss
                    accelerator.backward(lowerCAmelCase_						)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               model.eval()
               for step, batch in enumerate(lowerCAmelCase_						):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device						)
                    with torch.no_grad():
                         __UpperCAmelCase			:				List[str]													=    model(**lowerCAmelCase_						)
                    __UpperCAmelCase			:				str													=    outputs.logits.argmax(dim=-1						)
                    __UpperCAmelCase			:				Optional[int]													=    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                    metric.add_batch(
                        predictions=lowerCAmelCase_    ,      references=lowerCAmelCase_    ,      )

               __UpperCAmelCase			:				Union[str, Any]													=    metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'epoch {epoch}:'    ,      lowerCAmelCase_						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    argparse.ArgumentParser(description='''Simple example of training script.'''						)
     parser.add_argument(
         '''--mixed_precision'''    ,      type=lowerCAmelCase_    ,      default=lowerCAmelCase_    ,      choices=['''no''', '''fp16''', '''bf16''', '''fp8''']    ,      help='''Whether to use mixed precision. Choose'''
         '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
         '''and an Nvidia Ampere GPU.'''    ,      )
     parser.add_argument('''--cpu'''    ,      action='''store_true'''    ,      help='''If passed, will train on the CPU.'''						)
     __UpperCAmelCase			:				List[Any]													=    parser.parse_args()
     __UpperCAmelCase			:				int													=    {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowerCAmelCase_    ,      lowerCAmelCase_						)


if __name__ == "__main__":
      main()



'''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 			a       :


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	str							,				snake_case  :	Union[str, Any]=13							,				snake_case  :	int=7							,				snake_case  :	Union[str, Any]=True							,				snake_case  :	Tuple=True							,				snake_case  :	Tuple=True							,				snake_case  :	Union[str, Any]=True							,				snake_case  :	Tuple=99							,				snake_case  :	str=32							,				snake_case  :	Tuple=5							,				snake_case  :	int=4							,				snake_case  :	Union[str, Any]=37							,				snake_case  :	Union[str, Any]="gelu"							,				snake_case  :	List[Any]=0.1							,				snake_case  :	List[Any]=0.1							,				snake_case  :	Any=512							,				snake_case  :	Dict=16							,				snake_case  :	Dict=2							,				snake_case  :	int=0.02							,				snake_case  :	Dict=3							,				snake_case  :	Tuple=4							,				snake_case  :	Any=None							,				)		->							Tuple:
          __UpperCAmelCase			:				Optional[Any]													=    parent
          __UpperCAmelCase			:				int													=    batch_size
          __UpperCAmelCase			:				List[Any]													=    seq_length
          __UpperCAmelCase			:				Dict													=    is_training
          __UpperCAmelCase			:				Union[str, Any]													=    use_input_mask
          __UpperCAmelCase			:				Union[str, Any]													=    use_token_type_ids
          __UpperCAmelCase			:				Optional[Any]													=    use_labels
          __UpperCAmelCase			:				str													=    vocab_size
          __UpperCAmelCase			:				List[str]													=    hidden_size
          __UpperCAmelCase			:				Any													=    num_hidden_layers
          __UpperCAmelCase			:				Optional[int]													=    num_attention_heads
          __UpperCAmelCase			:				str													=    intermediate_size
          __UpperCAmelCase			:				Tuple													=    hidden_act
          __UpperCAmelCase			:				Tuple													=    hidden_dropout_prob
          __UpperCAmelCase			:				Optional[Any]													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				List[str]													=    max_position_embeddings
          __UpperCAmelCase			:				Optional[Any]													=    type_vocab_size
          __UpperCAmelCase			:				Dict													=    type_sequence_label_size
          __UpperCAmelCase			:				Tuple													=    initializer_range
          __UpperCAmelCase			:				Union[str, Any]													=    num_labels
          __UpperCAmelCase			:				List[str]													=    num_choices
          __UpperCAmelCase			:				List[str]													=    scope

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Any:
          __UpperCAmelCase			:				int													=    ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size    )

          __UpperCAmelCase			:				Optional[int]													=    None
          if self.use_input_mask:
               __UpperCAmelCase			:				Tuple													=    random_attention_mask([self.batch_size, self.seq_length]    )

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

          __UpperCAmelCase			:				Union[str, Any]													=    None
          __UpperCAmelCase			:				int													=    None
          __UpperCAmelCase			:				Union[str, Any]													=    None
          if self.use_labels:
               __UpperCAmelCase			:				List[Any]													=    ids_tensor([self.batch_size]							,				self.type_sequence_label_size    )
               __UpperCAmelCase			:				int													=    ids_tensor([self.batch_size, self.seq_length]							,				self.num_labels    )
               __UpperCAmelCase			:				int													=    ids_tensor([self.batch_size]							,				self.num_choices    )

          __UpperCAmelCase			:				int													=    self.get_config()

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

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							int:
          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  :	int							,				snake_case  :	Optional[int]							,				snake_case  :	str							,				snake_case  :	Optional[Any]							,				snake_case  :	List[str]							,				snake_case  :	str							,				snake_case  :	Union[str, Any]							,				snake_case  :	Tuple    )		->							Dict:
          __UpperCAmelCase			:				Any													=    NystromformerModel(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				str													=    model(snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				List[str]													=    model(snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				Dict													=    model(snake_case    )
          self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)    )

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	int							,				snake_case  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	int							,				snake_case  :	List[str]							,				snake_case  :	Any							,				snake_case  :	Tuple    )		->							List[Any]:
          __UpperCAmelCase			:				List[Any]													=    NystromformerForMaskedLM(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				List[str]													=    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  :	str							,				snake_case  :	Optional[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[str]							,				snake_case  :	Optional[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	Tuple    )		->							Union[str, Any]:
          __UpperCAmelCase			:				List[str]													=    NystromformerForQuestionAnswering(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				List[str]													=    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  :	Dict							,				snake_case  :	Any							,				snake_case  :	int							,				snake_case  :	Dict							,				snake_case  :	Any							,				snake_case  :	Optional[int]							,				snake_case  :	Tuple							,				snake_case  :	Any    )		->							List[str]:
          __UpperCAmelCase			:				int													=    self.num_labels
          __UpperCAmelCase			:				str													=    NystromformerForSequenceClassification(snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				List[str]													=    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  :	Tuple							,				snake_case  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	List[str]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[str]							,				snake_case  :	List[str]    )		->							Optional[Any]:
          __UpperCAmelCase			:				str													=    self.num_labels
          __UpperCAmelCase			:				int													=    NystromformerForTokenClassification(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				str													=    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  :	int							,				snake_case  :	Tuple							,				snake_case  :	Optional[int]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Dict							,				snake_case  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Union[str, Any]    )		->							Any:
          __UpperCAmelCase			:				List[str]													=    self.num_choices
          __UpperCAmelCase			:				List[str]													=    NystromformerForMultipleChoice(config=snake_case    )
          model.to(snake_case    )
          model.eval()
          __UpperCAmelCase			:				Optional[int]													=    input_ids.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
          __UpperCAmelCase			:				Dict													=    token_type_ids.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
          __UpperCAmelCase			:				Optional[Any]													=    input_mask.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
          __UpperCAmelCase			:				Union[str, Any]													=    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  :	List[Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     
          )			:				int													=    config_and_inputs
          __UpperCAmelCase			:				Tuple													=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict



@require_torch
class 			a       (	_a			,						_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[str]							     =     (
         (
             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 :				Tuple							     =     False
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     False

     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[str]:
          __UpperCAmelCase			:				str													=    NystromformerModelTester(self    )
          __UpperCAmelCase			:				List[str]													=    ConfigTester(self							,				config_class=snake_case							,				hidden_size=37    )

     def 				lowerCamelCase__				(       self  :	int    )		->							Any:
          self.config_tester.run_common_tests()

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				List[str]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Tuple:
          __UpperCAmelCase			:				Dict													=    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               __UpperCAmelCase			:				List[str]													=    type
               self.model_tester.create_and_check_model(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							List[Any]:
          __UpperCAmelCase			:				List[Any]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Optional[Any]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*snake_case    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Optional[Any]:
          __UpperCAmelCase			:				int													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case    )

     def 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Any													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case    )

     def 				lowerCamelCase__				(       self  :	str    )		->							int:
          __UpperCAmelCase			:				Dict													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case    )






     @slow
     def 				lowerCamelCase__				(       self  :	Any    )		->							Union[str, Any]:
          for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				List[Any]													=    NystromformerModel.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )



@require_torch
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     @slow
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							List[str]:
          __UpperCAmelCase			:				Union[str, Any]													=    NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''    )
          __UpperCAmelCase			:				Any													=    torch.tensor([[0, 1, 2, 3, 4, 5]]    )

          with torch.no_grad():
               __UpperCAmelCase			:				Union[str, Any]													=    model(snake_case    )[0]

          __UpperCAmelCase			:				Tuple													=    torch.Size((1, 6, 768)    )
          self.assertEqual(output.shape							,				snake_case    )

          __UpperCAmelCase			:				Tuple													=    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 				lowerCamelCase__				(       self  :	Optional[int]    )		->							str:
          __UpperCAmelCase			:				str													=    '''the [MASK] of Belgium is Brussels'''

          __UpperCAmelCase			:				Dict													=    AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''    )
          __UpperCAmelCase			:				Tuple													=    NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''    )

          __UpperCAmelCase			:				Optional[Any]													=    tokenizer(snake_case							,				return_tensors='''pt'''    )

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

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

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