


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


UpperCamelCase					:       int			    =     logging.getLogger(__name__)



def UpperCamelCase_				(				__a=2			,				__a=3			,				__a=16			,				__a = 10			,				__a = 2      )	->   Optional[int]:

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

      a__	:  str     =   get_dataset(__a      )
      a__	:  Union[str, Any]     =   get_dataset(__a      )
      a__	:  str     =   DataLoader(__a			,				shuffle=__a			,				batch_size=__a			,				num_workers=4      )
      a__	:  str     =   DataLoader(__a			,				shuffle=__a			,				batch_size=__a			,				num_workers=4      )
      return (train_dataloader, valid_dataloader)



def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a			,				__a			,				__a=None      )	->   Optional[int]:
      a__	:  Optional[Any]     =   []
      for epoch in range(__a      ):
            # Train quickly
            model.train()
            for batch in dataloader:
                  a__,	a__	:  str     =   batch
                  a__	:  Optional[int]     =   model(__a      )
                  a__	:  Tuple     =   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 A__     ( nn.Module  ):



  """simple docstring"""



  def __init__( self			:  Union[str, Any]       ):
        super().__init__()
        a__	:  List[Any]     =   nn.Parameter(torch.randn(1       )       )
        a__	:  Optional[Any]     =   nn.Parameter(torch.randn(1       )       )

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  List[Any]       ):
        return x * self.a + self.b




class A__     ( unittest.TestCase  ):



  """simple docstring"""



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

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

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

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

              a__	:  List[str]     =   train(2				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              # Save everything
              a__	:  Tuple     =   os.path.join(lowerCamelCase__				,				"checkpoint"       )
              accelerator.save_state(lowerCamelCase__       )

              # Load everything back in and make sure all states work
              accelerator.load_state(lowerCamelCase__       )
              test_rands += train(1				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              ((a__),	(a__))	:  Any     =   model.a.item(), model.b.item()
              a__	:  Optional[Any]     =   optimizer.state_dict()
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )

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

              # Train baseline
              a__	:  int     =   Accelerator(project_dir=lowerCamelCase__				,				project_config=lowerCamelCase__       )
              a__,	a__,	a__,	a__	:  List[str]     =   accelerator.prepare(
                  lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              # Save initial
              accelerator.save_state()
              ((a__),	(a__))	:  Optional[Any]     =   model.a.item(), model.b.item()
              a__	:  List[Any]     =   optimizer.state_dict()
              a__	:  Union[str, Any]     =   train(3				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              ((a__),	(a__))	:  str     =   model.a.item(), model.b.item()
              a__	:  List[Any]     =   optimizer.state_dict()

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

              a__	:  List[str]     =   train(2				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              # Save everything
              accelerator.save_state()

              # Load everything back in and make sure all states work
              accelerator.load_state(os.path.join(lowerCamelCase__				,				"checkpoints"				,				"checkpoint_1"       )       )
              test_rands += train(1				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              ((a__),	(a__))	:  Any     =   model.a.item(), model.b.item()
              a__	:  Dict     =   optimizer.state_dict()
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )
              self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  int     =   torch.tensor([1, 2, 3]       )
        a__	:  List[Any]     =   torch.tensor([2, 3, 4]       )
        a__	:  Tuple     =   DummyModel()
        a__	:  List[str]     =   torch.optim.Adam(net.parameters()       )
        a__	:  Optional[Any]     =   Accelerator()
        with self.assertRaises(lowerCamelCase__       ) as ve:
              accelerator.register_for_checkpointing(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
        a__	:  Dict     =   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			:  str       ):
        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42       )
              a__	:  List[Any]     =   DummyModel()
              a__	:  Any     =   torch.optim.Adam(params=model.parameters()				,				lr=1E-3       )
              a__	:  List[Any]     =   torch.optim.lr_scheduler.StepLR(lowerCamelCase__				,				step_size=1				,				gamma=0.99       )
              a__,	a__	:  Optional[int]     =   dummy_dataloaders()
              a__	:  Optional[Any]     =   ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__       )
              # Train baseline
              a__	:  int     =   Accelerator(project_dir=lowerCamelCase__				,				project_config=lowerCamelCase__       )
              a__,	a__,	a__,	a__,	a__	:  List[Any]     =   accelerator.prepare(
                  lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              # Save initial
              accelerator.save_state()
              a__	:  Optional[int]     =   scheduler.state_dict()
              train(3				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
              self.assertNotEqual(lowerCamelCase__				,				scheduler.state_dict()       )

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

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

  @require_cuda
  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Union[str, Any]     =   ["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__       )]
        execute_subprocess_async(lowerCamelCase__				,				env=os.environ.copy()       )


if __name__ == "__main__":
    UpperCamelCase					:       Optional[Any]			    =     """/tmp/accelerate/state_checkpointing"""
    UpperCamelCase					:       Optional[int]			    =     DummyModel()
    UpperCamelCase					:       List[Any]			    =     torch.optim.Adam(params=model.parameters(), lr=1E-3)
    UpperCamelCase					:       Optional[Any]			    =     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
    UpperCamelCase    ,							UpperCamelCase					:       Tuple			    =     dummy_dataloaders()
    UpperCamelCase					:       Union[str, Any]			    =     ProjectConfiguration(automatic_checkpoint_naming=True)
    # Train baseline
    UpperCamelCase					:       Union[str, Any]			    =     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)
    UpperCamelCase    ,							UpperCamelCase    ,							UpperCamelCase    ,							UpperCamelCase    ,							UpperCamelCase					:       Dict			    =     accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, scheduler
    )
    UpperCamelCase    ,							UpperCamelCase					:       List[str]			    =     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:
        UpperCamelCase					:       Tuple			    =     group["""params"""][0].device
        break
    assert param_device.type == accelerator.device.type
    UpperCamelCase					:       Dict			    =     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:
        UpperCamelCase					:       str			    =     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:
        UpperCamelCase					:       Tuple			    =     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()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)