





import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_SCREAMING_SNAKE_CASE							=						logging.getLogger(__name__)


def 							SCREAMING_SNAKE_CASE__  (			__a=2      ,       __a=3      ,       __a=16      ,       __a = 10      ,       __a = 2      ):

	def get_dataset(__a      ):
		snake_case_						:	Any      =		torch.randn(batch_size * n_batches      ,       1      )
		return TensorDataset(A__      ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,       1      )      )

	snake_case_						:	int      =		get_dataset(A__      )
	snake_case_						:	List[str]      =		get_dataset(A__      )
	snake_case_						:	Tuple      =		DataLoader(A__      ,       shuffle=A__      ,       batch_size=A__      ,       num_workers=4      )
	snake_case_						:	Tuple      =		DataLoader(A__      ,       shuffle=A__      ,       batch_size=A__      ,       num_workers=4      )
	return (train_dataloader, valid_dataloader)


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ,       __a      ,       __a=None      ):
	snake_case_						:	str      =		[]
	for epoch in range(A__      ):
		# Train quickly
		model.train()
		for batch in dataloader:
			snake_case_  ,snake_case_						:	Optional[int]      =		batch
			snake_case_						:	Optional[Any]      =		model(A__      )
			snake_case_						:	str      =		torch.nn.functional.mse_loss(A__      ,       A__      )
			accelerator.backward(A__      )
			optimizer.step()
			optimizer.zero_grad()
		rands.append(random.random()      )  # Introduce some randomness
		if scheduler is not None:
			scheduler.step()
	return rands
class SCREAMING_SNAKE_CASE_  (    nn.Module    ):






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







							"""simple docstring"""


							super().__init__()
							snake_case_						:	List[Any]      =		nn.Parameter(torch.randn(1		)		)
							snake_case_						:	str      =		nn.Parameter(torch.randn(1		)		)






						def UpperCAmelCase_ (					self					:					str						,      _A					:					Dict		)							->			List[str]:







							"""simple docstring"""


							return x * self.a + self.b
class SCREAMING_SNAKE_CASE_  (    unittest.TestCase    ):






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







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(42		)
								snake_case_						:	Optional[Any]      =		DummyModel()
								snake_case_						:	Any      =		torch.optim.Adam(params=model.parameters()						,      lr=1E-3		)
								snake_case_  ,snake_case_						:	Any      =		dummy_dataloaders()
								snake_case_						:	List[Any]      =		ProjectConfiguration(total_limit=1						,      project_dir=UpperCamelCase__						,      automatic_checkpoint_naming=UpperCamelCase__		)
								# Train baseline
								snake_case_						:	str      =		Accelerator(project_config=UpperCamelCase__		)
								snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:	Optional[Any]      =		accelerator.prepare(
								    UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								# Save initial
								accelerator.save_state()

								# Save second state
								accelerator.save_state()
								self.assertEqual(len(os.listdir(accelerator.project_dir		)		)						,      1		)






						def UpperCAmelCase_ (					self					:					Optional[int]		)							->			Dict:







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(42		)
								snake_case_						:	Optional[Any]      =		DummyModel()
								snake_case_						:	Tuple      =		torch.optim.Adam(params=model.parameters()						,      lr=1E-3		)
								snake_case_  ,snake_case_						:	Dict      =		dummy_dataloaders()
								# Train baseline
								snake_case_						:	Optional[int]      =		Accelerator()
								snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:	Union[str, Any]      =		accelerator.prepare(
								    UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								# Save initial
								snake_case_						:	Optional[Any]      =		os.path.join(UpperCamelCase__						,      'initial'		)
								accelerator.save_state(UpperCamelCase__		)
								((snake_case_)  ,(snake_case_))						:	Union[str, Any]      =		model.a.item(), model.b.item()
								snake_case_						:	Union[str, Any]      =		optimizer.state_dict()
								snake_case_						:	str      =		train(3						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								((snake_case_)  ,(snake_case_))						:	Union[str, Any]      =		model.a.item(), model.b.item()
								snake_case_						:	str      =		optimizer.state_dict()

								# Train partially
								set_seed(42		)
								snake_case_						:	int      =		DummyModel()
								snake_case_						:	Optional[int]      =		torch.optim.Adam(params=model.parameters()						,      lr=1E-3		)
								snake_case_  ,snake_case_						:	Dict      =		dummy_dataloaders()
								snake_case_						:	Dict      =		Accelerator()
								snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:	Optional[int]      =		accelerator.prepare(
								    UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								accelerator.load_state(UpperCamelCase__		)
								((snake_case_)  ,(snake_case_))						:	Any      =		model.a.item(), model.b.item()
								snake_case_						:	Any      =		optimizer.state_dict()
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)

								snake_case_						:	int      =		train(2						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								# Save everything
								snake_case_						:	str      =		os.path.join(UpperCamelCase__						,      'checkpoint'		)
								accelerator.save_state(UpperCamelCase__		)

								# Load everything back in and make sure all states work
								accelerator.load_state(UpperCamelCase__		)
								test_rands += train(1						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								((snake_case_)  ,(snake_case_))						:	List[str]      =		model.a.item(), model.b.item()
								snake_case_						:	int      =		optimizer.state_dict()
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)






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







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(42		)
								snake_case_						:	Any      =		DummyModel()
								snake_case_						:	str      =		torch.optim.Adam(params=model.parameters()						,      lr=1E-3		)
								snake_case_  ,snake_case_						:	List[Any]      =		dummy_dataloaders()
								snake_case_						:	int      =		ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__		)

								# Train baseline
								snake_case_						:	Any      =		Accelerator(project_dir=UpperCamelCase__						,      project_config=UpperCamelCase__		)
								snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:	List[Any]      =		accelerator.prepare(
								    UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								# Save initial
								accelerator.save_state()
								((snake_case_)  ,(snake_case_))						:	Any      =		model.a.item(), model.b.item()
								snake_case_						:	Tuple      =		optimizer.state_dict()
								snake_case_						:	List[str]      =		train(3						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								((snake_case_)  ,(snake_case_))						:	List[str]      =		model.a.item(), model.b.item()
								snake_case_						:	str      =		optimizer.state_dict()

								# Train partially
								set_seed(42		)
								snake_case_						:	List[Any]      =		DummyModel()
								snake_case_						:	Union[str, Any]      =		torch.optim.Adam(params=model.parameters()						,      lr=1E-3		)
								snake_case_  ,snake_case_						:	Optional[int]      =		dummy_dataloaders()
								snake_case_						:	str      =		ProjectConfiguration(iteration=1						,      automatic_checkpoint_naming=UpperCamelCase__		)
								snake_case_						:	str      =		Accelerator(project_dir=UpperCamelCase__						,      project_config=UpperCamelCase__		)
								snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:	int      =		accelerator.prepare(
								    UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								accelerator.load_state(os.path.join(UpperCamelCase__						,      'checkpoints'						,      'checkpoint_0'		)		)
								((snake_case_)  ,(snake_case_))						:	Optional[int]      =		model.a.item(), model.b.item()
								snake_case_						:	int      =		optimizer.state_dict()
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)

								snake_case_						:	int      =		train(2						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								# Save everything
								accelerator.save_state()

								# Load everything back in and make sure all states work
								accelerator.load_state(os.path.join(UpperCamelCase__						,      'checkpoints'						,      'checkpoint_1'		)		)
								test_rands += train(1						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								((snake_case_)  ,(snake_case_))						:	Tuple      =		model.a.item(), model.b.item()
								snake_case_						:	int      =		optimizer.state_dict()
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)
								self.assertEqual(UpperCamelCase__						,      UpperCamelCase__		)






						def UpperCAmelCase_ (					self					:					Optional[int]		)							->			str:







							"""simple docstring"""


							snake_case_						:	Tuple      =		torch.tensor([1, 2, 3]		)
							snake_case_						:	List[str]      =		torch.tensor([2, 3, 4]		)
							snake_case_						:	Any      =		DummyModel()
							snake_case_						:	Any      =		torch.optim.Adam(net.parameters()		)
							snake_case_						:	str      =		Accelerator()
							with self.assertRaises(UpperCamelCase__		) as ve:
								accelerator.register_for_checkpointing(UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
							snake_case_						:	Union[str, Any]      =		str(ve.exception		)
							self.assertTrue('Item at index 0' in message		)
							self.assertTrue('Item at index 1' in message		)
							self.assertFalse('Item at index 2' in message		)
							self.assertFalse('Item at index 3' in message		)






						def UpperCAmelCase_ (					self					:					Dict		)							->			Optional[Any]:







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(42		)
								snake_case_						:	List[Any]      =		DummyModel()
								snake_case_						:	Tuple      =		torch.optim.Adam(params=model.parameters()						,      lr=1E-3		)
								snake_case_						:	str      =		torch.optim.lr_scheduler.StepLR(UpperCamelCase__						,      step_size=1						,      gamma=0.9_9		)
								snake_case_  ,snake_case_						:	Tuple      =		dummy_dataloaders()
								snake_case_						:	List[str]      =		ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__		)
								# Train baseline
								snake_case_						:	Dict      =		Accelerator(project_dir=UpperCamelCase__						,      project_config=UpperCamelCase__		)
								snake_case_  ,snake_case_  ,snake_case_  ,snake_case_  ,snake_case_						:	List[Any]      =		accelerator.prepare(
								    UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								# Save initial
								accelerator.save_state()
								snake_case_						:	Optional[int]      =		scheduler.state_dict()
								train(3						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__						,      UpperCamelCase__		)
								self.assertNotEqual(UpperCamelCase__						,      scheduler.state_dict()		)

								# Load everything back in and make sure all states work
								accelerator.load_state(os.path.join(UpperCamelCase__						,      'checkpoints'						,      'checkpoint_0'		)		)
								self.assertEqual(UpperCamelCase__						,      scheduler.state_dict()		)






						def UpperCAmelCase_ (					self					:					List[str]		)							->			Optional[int]:







							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(42		)
								snake_case_						:	List[str]      =		DummyModel()
								snake_case_						:	Tuple      =		ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__						,      total_limit=2		)
								# Train baseline
								snake_case_						:	Any      =		Accelerator(project_dir=UpperCamelCase__						,      project_config=UpperCamelCase__		)
								snake_case_						:	Tuple      =		accelerator.prepare(UpperCamelCase__		)
								# Save 3 states:
								for _ in range(11		):
									accelerator.save_state()
								self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase__						,      'checkpoints'						,      'checkpoint_0'		)		)		)
								self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,      'checkpoints'						,      'checkpoint_9'		)		)		)
								self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,      'checkpoints'						,      'checkpoint_10'		)		)		)






						@require_cuda
						def UpperCAmelCase_ (					self					:					Dict		)							->			Optional[int]:







							"""simple docstring"""


							snake_case_						:	List[Any]      =		['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
							execute_subprocess_async(UpperCamelCase__						,      env=os.environ.copy()		)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE							=						"/tmp/accelerate/state_checkpointing"
						_SCREAMING_SNAKE_CASE							=						DummyModel()
						_SCREAMING_SNAKE_CASE							=						torch.optim.Adam(params=model.parameters(), lr=1e-3)
						_SCREAMING_SNAKE_CASE							=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
						_SCREAMING_SNAKE_CASE							=						dummy_dataloaders()
						_SCREAMING_SNAKE_CASE							=						ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						_SCREAMING_SNAKE_CASE							=						Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
						if accelerator.process_index == 0:
												if os.path.exists(savedir):
																		shutil.rmtree(savedir)
												os.makedirs(savedir)
						_SCREAMING_SNAKE_CASE							=						accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						_SCREAMING_SNAKE_CASE							=						accelerator.prepare(model, optimizer)
						train(3, model, train_dataloader, optimizer, accelerator, scheduler)
						# Check that the intial optimizer is loaded on the GPU
						for group in optimizer.param_groups:
												_SCREAMING_SNAKE_CASE							=						group["params"][0].device
												break
						assert param_device.type == accelerator.device.type
						_SCREAMING_SNAKE_CASE							=						model.cpu()
						accelerator.wait_for_everyone()
						accelerator.save_state()
						accelerator.wait_for_everyone()

						# Check CPU state
						accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
						for group in optimizer.param_groups:
												_SCREAMING_SNAKE_CASE							=						group["params"][0].device
												break
						assert (
						    param_device.type == torch.device("""cpu""").type
						), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

						# Check device state
						model.to(accelerator.device)
						accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
						for group in optimizer.param_groups:
												_SCREAMING_SNAKE_CASE							=						group["params"][0].device
												break
						assert (
						    param_device.type == accelerator.device.type
						), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

						# Check error
						with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
												accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
						accelerator.wait_for_everyone()
						if accelerator.process_index == 0:
												shutil.rmtree(savedir)
						accelerator.wait_for_everyone()






'''simple docstring'''


import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
   import torch

   from transformers import (
       MraForMaskedLM,
       MraForMultipleChoice,
       MraForQuestionAnswering,
       MraForSequenceClassification,
       MraForTokenClassification,
       MraModel,
   )
   from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Any=2  ,						UpperCamelCase__      :					Union[str, Any]=8  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[Any]=9_9  ,						UpperCamelCase__      :					List[Any]=1_6  ,						UpperCamelCase__      :					List[str]=5  ,						UpperCamelCase__      :					Dict=2  ,						UpperCamelCase__      :					Optional[int]=3_6  ,						UpperCamelCase__      :					str="gelu"  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Optional[int]=5_1_2  ,						UpperCamelCase__      :					Dict=1_6  ,						UpperCamelCase__      :					List[str]=2  ,						UpperCamelCase__      :					Any=0.0_2  ,						UpperCamelCase__      :					str=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						):


       """simple docstring"""







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






    def  A       (  self      :					int ):


       """simple docstring"""







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

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

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

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

       UpperCamelCase					=  self.get_config()

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






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return MraConfig(
           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  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.get_config()
       UpperCamelCase					=  3_0_0
       return config






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  self.prepare_config_and_inputs()

       UpperCamelCase					=  True
       UpperCamelCase					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )






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


       """simple docstring"""







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






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  True
       UpperCamelCase					=  MraModel(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						encoder_attention_mask=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






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


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  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  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForQuestionAnswering(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  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  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







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






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


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForTokenClassification(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  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  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







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






    def  A       (  self      :					int ):


       """simple docstring"""







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

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






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False

    _SCREAMING_SNAKE_CASE	  =  ()






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  MraModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					str ):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







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






    def  A       (  self      :					str ):


       """simple docstring"""







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






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







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






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







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






    def  A       (  self      :					Tuple ):


       """simple docstring"""







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






    def  A       (  self      :					Any ):


       """simple docstring"""







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






    def  A       (  self      :					Any ):


       """simple docstring"""







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






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  MraModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )






    @unittest.skip(reason='MRA does not output attentions' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @slow
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  MraModel.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

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

       UpperCamelCase					=  torch.Size((1, 2_5_6, 7_6_8) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )

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






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

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

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 2_5_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )

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






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
       UpperCamelCase					=  torch.arange(4_0_9_6 ).unsqueeze(0 )

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

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 4_0_9_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )

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

