





'''simple docstring'''


import argparse
import json
import os

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.deepspeed import DummyOptim, DummyScheduler


_SCREAMING_SNAKE_CASE									=				16
_SCREAMING_SNAKE_CASE									=				32


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any] = 16		,							SCREAMING_SNAKE_CASE_	:       Optional[Any] = "bert-base-cased"     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
    _lowerCAmelCase						      =   load_dataset("glue"		,							"mrpc"     )

    def tokenize_function(SCREAMING_SNAKE_CASE_	:       Any     ):
        # max_length=None => use the model max length (it's actually the default)
        _lowerCAmelCase						      =   tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=_SCREAMING_SNAKE_CASE		,							max_length=_SCREAMING_SNAKE_CASE     )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    _lowerCAmelCase						      =   datasets.map(
        _SCREAMING_SNAKE_CASE		,							batched=_SCREAMING_SNAKE_CASE		,							remove_columns=["idx", "sentence1", "sentence2"]		,							load_from_cache_file=_SCREAMING_SNAKE_CASE     )

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

    def collate_fn(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_SCREAMING_SNAKE_CASE		,							padding="max_length"		,							max_length=128		,							return_tensors="pt"     )
        return tokenizer.pad(_SCREAMING_SNAKE_CASE		,							padding="longest"		,							return_tensors="pt"     )

    # Instantiate dataloaders.
    _lowerCAmelCase						      =   DataLoader(
        tokenized_datasets["train"]		,							shuffle=_SCREAMING_SNAKE_CASE		,							collate_fn=_SCREAMING_SNAKE_CASE		,							batch_size=_SCREAMING_SNAKE_CASE     )
    _lowerCAmelCase						      =   DataLoader(
        tokenized_datasets["validation"]		,							shuffle=_SCREAMING_SNAKE_CASE		,							collate_fn=_SCREAMING_SNAKE_CASE		,							batch_size=_SCREAMING_SNAKE_CASE     )

    return train_dataloader, eval_dataloader


def 						__a(SCREAMING_SNAKE_CASE_	:       List[Any]		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    model.eval()
    _lowerCAmelCase						      =   0
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE     ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device     )
        with torch.no_grad():
            _lowerCAmelCase						      =   model(**_SCREAMING_SNAKE_CASE     )
        _lowerCAmelCase						      =   outputs.logits.argmax(dim=-1     )
        # It is slightly faster to call this once, than multiple times
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   accelerator.gather(
            (predictions, batch["labels"])     )  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_SCREAMING_SNAKE_CASE     ) - 1:
                _lowerCAmelCase						      =   predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                _lowerCAmelCase						      =   references[: len(eval_dataloader.dataset     ) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_SCREAMING_SNAKE_CASE		,							references=_SCREAMING_SNAKE_CASE		,							)

    _lowerCAmelCase						      =   metric.compute()
    return eval_metric["accuracy"]


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       List[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowerCAmelCase						      =   config["lr"]
    _lowerCAmelCase						      =   int(config["num_epochs"]     )
    _lowerCAmelCase						      =   int(config["seed"]     )
    _lowerCAmelCase						      =   int(config["batch_size"]     )
    _lowerCAmelCase						      =   args.model_name_or_path

    set_seed(_SCREAMING_SNAKE_CASE     )
    _lowerCAmelCase      ,      _lowerCAmelCase						      =   get_dataloaders(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE     )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowerCAmelCase						      =   AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE		,							return_dict=_SCREAMING_SNAKE_CASE     )

    # Instantiate optimizer
    _lowerCAmelCase						      =   (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    _lowerCAmelCase						      =   optimizer_cls(params=model.parameters()		,							lr=_SCREAMING_SNAKE_CASE     )

    if accelerator.state.deepspeed_plugin is not None:
        _lowerCAmelCase						      =   accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        _lowerCAmelCase						      =   1
    _lowerCAmelCase						      =   (len(_SCREAMING_SNAKE_CASE     ) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        _lowerCAmelCase						      =   get_linear_schedule_with_warmup(
            optimizer=_SCREAMING_SNAKE_CASE		,							num_warmup_steps=0		,							num_training_steps=_SCREAMING_SNAKE_CASE		,							)
    else:
        _lowerCAmelCase						      =   DummyScheduler(_SCREAMING_SNAKE_CASE		,							total_num_steps=_SCREAMING_SNAKE_CASE		,							warmup_num_steps=0     )

    # 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.
    _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   accelerator.prepare(
        _SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE     )

    # We need to keep track of how many total steps we have iterated over
    _lowerCAmelCase						      =   0
    # We also need to keep track of the stating epoch so files are named properly
    _lowerCAmelCase						      =   0
    _lowerCAmelCase						      =   evaluate.load("glue"		,							"mrpc"     )
    _lowerCAmelCase						      =   num_epochs

    if args.partial_train_epoch is not None:
        _lowerCAmelCase						      =   args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint     )
        _lowerCAmelCase						      =   args.resume_from_checkpoint.split("epoch_"     )[1]
        _lowerCAmelCase						      =   ""
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        _lowerCAmelCase						      =   int(_SCREAMING_SNAKE_CASE     ) + 1
        _lowerCAmelCase						      =   evaluation_loop(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE     )
        accelerator.print("resumed checkpoint performance:"		,							_SCREAMING_SNAKE_CASE     )
        accelerator.print("resumed checkpoint\'s scheduler\'s lr:"		,							lr_scheduler.get_lr()[0]     )
        accelerator.print("resumed optimizers\'s lr:"		,							optimizer.param_groups[0]["lr"]     )
        with open(os.path.join(args.output_dir		,							F'''state_{starting_epoch-1}.json'''     )		,							"r"     ) as f:
            _lowerCAmelCase						      =   json.load(_SCREAMING_SNAKE_CASE     )
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    _lowerCAmelCase						      =   {}
    for epoch in range(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE     ):
        model.train()
        for step, batch in enumerate(_SCREAMING_SNAKE_CASE     ):
            _lowerCAmelCase						      =   model(**_SCREAMING_SNAKE_CASE     )
            _lowerCAmelCase						      =   outputs.loss
            _lowerCAmelCase						      =   loss / gradient_accumulation_steps
            accelerator.backward(_SCREAMING_SNAKE_CASE     )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        _lowerCAmelCase						      =   F'''epoch_{epoch}'''
        _lowerCAmelCase						      =   os.path.join(args.output_dir		,							_SCREAMING_SNAKE_CASE     )
        accelerator.save_state(_SCREAMING_SNAKE_CASE     )
        _lowerCAmelCase						      =   evaluation_loop(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE     )
        _lowerCAmelCase						      =   accuracy
        _lowerCAmelCase						      =   lr_scheduler.get_lr()[0]
        _lowerCAmelCase						      =   optimizer.param_groups[0]["lr"]
        _lowerCAmelCase						      =   epoch
        _lowerCAmelCase						      =   overall_step
        accelerator.print(F'''epoch {epoch}:'''		,							_SCREAMING_SNAKE_CASE     )

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir		,							F'''state_{epoch}.json'''     )		,							"w"     ) as f:
                json.dump(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE     )


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."     )
    parser.add_argument(
        "--model_name_or_path"		,							type=_SCREAMING_SNAKE_CASE		,							default="bert-base-cased"		,							help="Path to pretrained model or model identifier from huggingface.co/models."		,							required=_SCREAMING_SNAKE_CASE		,							)
    parser.add_argument(
        "--output_dir"		,							type=_SCREAMING_SNAKE_CASE		,							default="."		,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,							)
    parser.add_argument(
        "--resume_from_checkpoint"		,							type=_SCREAMING_SNAKE_CASE		,							default=_SCREAMING_SNAKE_CASE		,							help="If the training should continue from a checkpoint folder."		,							)
    parser.add_argument(
        "--partial_train_epoch"		,							type=_SCREAMING_SNAKE_CASE		,							default=_SCREAMING_SNAKE_CASE		,							help="If passed, the training will stop after this number of epochs."		,							)
    parser.add_argument(
        "--num_epochs"		,							type=_SCREAMING_SNAKE_CASE		,							default=2		,							help="Number of train epochs."		,							)
    _lowerCAmelCase						      =   parser.parse_args()
    _lowerCAmelCase						      =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

    training_function(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       main()









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'							)





