


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       :		Tuple         =				logging.getLogger(__name__)




def     UpperCamelCase    (      _a=2       ,		_a=3       ,		_a=1_6       ,		_a = 1_0       ,		_a = 2		)					->							List[str]:
     '''simple docstring'''





     def get_dataset(_a		):
          lowercase_       :int     =       torch.randn(batch_size * n_batches       ,		1		)
          return TensorDataset(_a       ,		a * x + b + 0.1 * torch.randn(batch_size * n_batches       ,		1		)		)

     lowercase_       :Dict     =       get_dataset(_a		)
     lowercase_       :Dict     =       get_dataset(_a		)
     lowercase_       :Dict     =       DataLoader(_a       ,		shuffle=_a       ,		batch_size=_a       ,		num_workers=4		)
     lowercase_       :Any     =       DataLoader(_a       ,		shuffle=_a       ,		batch_size=_a       ,		num_workers=4		)
     return (train_dataloader, valid_dataloader)




def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a       ,		_a       ,		_a=None		)					->							str:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       []
     for epoch in range(_a		):
          # Train quickly
          model.train()
          for batch in dataloader:
               lowercase_    ,      lowercase_       :Optional[Any]     =       batch
               lowercase_       :Dict     =       model(_a		)
               lowercase_       :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      UpperCamelCase					(		nn.Module     ):


   '''simple docstring'''







   def __init__(     self		):
        super().__init__()
        lowercase_       :Tuple     =       nn.Parameter(torch.randn(1		)		)
        lowercase_       :Optional[Any]     =       nn.Parameter(torch.randn(1		)		)




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        return x * self.a + self.b






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             lowercase_       :Optional[int]     =       DummyModel()
             lowercase_       :List[str]     =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             lowercase_    ,      lowercase_       :Union[str, Any]     =       dummy_dataloaders()
             lowercase_       :Any     =       ProjectConfiguration(total_limit=1   ,							project_dir=UpperCamelCase_   ,							automatic_checkpoint_naming=UpperCamelCase_		)
             # Train baseline
             lowercase_       :List[str]     =       Accelerator(project_config=UpperCamelCase_		)
             lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Optional[int]     =       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		):
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             lowercase_       :List[Any]     =       DummyModel()
             lowercase_       :List[str]     =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             lowercase_    ,      lowercase_       :Optional[int]     =       dummy_dataloaders()
             # Train baseline
             lowercase_       :int     =       Accelerator()
             lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :int     =       accelerator.prepare(
                 UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
             # Save initial
             lowercase_       :List[str]     =       os.path.join(UpperCamelCase_   ,							'''initial'''		)
             accelerator.save_state(UpperCamelCase_		)
             ((lowercase_)    ,      (lowercase_))       :Optional[Any]     =       model.a.item(), model.b.item()
             lowercase_       :List[str]     =       optimizer.state_dict()
             lowercase_       :int     =       train(3   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
             ((lowercase_)    ,      (lowercase_))       :str     =       model.a.item(), model.b.item()
             lowercase_       :str     =       optimizer.state_dict()

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

             lowercase_       :str     =       train(2   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
             # Save everything
             lowercase_       :Optional[Any]     =       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_		)
             ((lowercase_)    ,      (lowercase_))       :str     =       model.a.item(), model.b.item()
             lowercase_       :Optional[int]     =       optimizer.state_dict()
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             lowercase_       :Union[str, Any]     =       DummyModel()
             lowercase_       :Tuple     =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             lowercase_    ,      lowercase_       :Tuple     =       dummy_dataloaders()
             lowercase_       :List[str]     =       ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_		)

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

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

             lowercase_       :Any     =       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_		)
             ((lowercase_)    ,      (lowercase_))       :List[str]     =       model.a.item(), model.b.item()
             lowercase_       :Union[str, Any]     =       optimizer.state_dict()
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
             self.assertEqual(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       torch.tensor([1, 2, 3]		)
        lowercase_       :Union[str, Any]     =       torch.tensor([2, 3, 4]		)
        lowercase_       :str     =       DummyModel()
        lowercase_       :List[Any]     =       torch.optim.Adam(net.parameters()		)
        lowercase_       :Union[str, Any]     =       Accelerator()
        with self.assertRaises(UpperCamelCase_		) as ve:
             accelerator.register_for_checkpointing(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
        lowercase_       :List[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		):
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             lowercase_       :List[Any]     =       DummyModel()
             lowercase_       :Dict     =       torch.optim.Adam(params=model.parameters()   ,							lr=1E-3		)
             lowercase_       :str     =       torch.optim.lr_scheduler.StepLR(UpperCamelCase_   ,							step_size=1   ,							gamma=0.99		)
             lowercase_    ,      lowercase_       :Optional[int]     =       dummy_dataloaders()
             lowercase_       :str     =       ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_		)
             # Train baseline
             lowercase_       :int     =       Accelerator(project_dir=UpperCamelCase_   ,							project_config=UpperCamelCase_		)
             lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Optional[Any]     =       accelerator.prepare(
                 UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
             # Save initial
             accelerator.save_state()
             lowercase_       :Union[str, Any]     =       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		):
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42		)
             lowercase_       :List[Any]     =       DummyModel()
             lowercase_       :Union[str, Any]     =       ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_   ,							total_limit=2		)
             # Train baseline
             lowercase_       :Dict     =       Accelerator(project_dir=UpperCamelCase_   ,							project_config=UpperCamelCase_		)
             lowercase_       :Any     =       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		):
        lowercase_       :Optional[int]     =       ['''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       :		int         =				"/tmp/accelerate/state_checkpointing"
   SCREAMING_SNAKE_CASE       :		List[str]         =				DummyModel()
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				torch.optim.Adam(params=model.parameters(), lr=1e-3)
   SCREAMING_SNAKE_CASE       :		Any         =				torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
   SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		List[Any]         =				dummy_dataloaders()
   SCREAMING_SNAKE_CASE       :		str         =				ProjectConfiguration(automatic_checkpoint_naming=True)
   # Train baseline
   SCREAMING_SNAKE_CASE       :		int         =				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     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Dict         =				accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		Tuple         =				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       :		List[Any]         =				group["params"][0].device
      break
   assert param_device.type == accelerator.device.type
   SCREAMING_SNAKE_CASE       :		List[Any]         =				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       :		Optional[Any]         =				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       :		List[str]         =				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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

