


'''simple docstring'''



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      :Optional[Any]			    =       logging.getLogger(__name__)




def      _a     (						_lowercase :     Optional[int]=2      , _lowercase :     List[Any]=3      , _lowercase :     List[str]=16      , _lowercase :     int = 10      , _lowercase :     int = 2  ):





     '''simple docstring'''






     def get_dataset(_lowercase :     Union[str, Any]  ):
          __UpperCAmelCase     :			int  							=      torch.randn(batch_size * n_batches      , 1  )
          return TensorDataset(_lowercase      , a * x + b + 0.1 * torch.randn(batch_size * n_batches      , 1  )  )

     __UpperCAmelCase     :			Dict  							=      get_dataset(_lowercase  )
     __UpperCAmelCase     :			List[Any]  							=      get_dataset(_lowercase  )
     __UpperCAmelCase     :			Dict  							=      DataLoader(_lowercase      , shuffle=_lowercase      , batch_size=_lowercase      , num_workers=4  )
     __UpperCAmelCase     :			Any  							=      DataLoader(_lowercase      , shuffle=_lowercase      , batch_size=_lowercase      , num_workers=4  )
     return (train_dataloader, valid_dataloader)




def      _a     (						_lowercase :     Union[str, Any]      , _lowercase :     int      , _lowercase :     Any      , _lowercase :     str      , _lowercase :     Tuple      , _lowercase :     List[Any]=None  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      []
     for epoch in range(_lowercase  ):
          # Train quickly
          model.train()
          for batch in dataloader:
               __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      batch
               __UpperCAmelCase     :			Optional[Any]  							=      model(_lowercase  )
               __UpperCAmelCase     :			Any  							=      torch.nn.functional.mse_loss(_lowercase      , _lowercase  )
               accelerator.backward(_lowercase  )
               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					:		List[Any]      )						->							int:
          super().__init__()
          __UpperCAmelCase     :			int  							=      nn.Parameter(torch.randn(1      )      )
          __UpperCAmelCase     :			Union[str, Any]  							=      nn.Parameter(torch.randn(1      )      )






     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		str      )						->							Dict:
          return x * self.a + self.b



class      a    (		unittest.TestCase	):


     """simple docstring"""






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

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

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

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

               __UpperCAmelCase     :			str  							=      train(2							,			snake_case							,			snake_case							,			snake_case							,			snake_case      )
               # Save everything
               __UpperCAmelCase     :			Any  							=      os.path.join(snake_case							,			'''checkpoint'''      )
               accelerator.save_state(snake_case      )

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

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

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

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

               __UpperCAmelCase     :			Any  							=      train(2							,			snake_case							,			snake_case							,			snake_case							,			snake_case      )
               # Save everything
               accelerator.save_state()

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

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

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

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






     @require_cuda
     def 			lowerCamelCase__			(    self					:		str      )						->							List[Any]:
          __UpperCAmelCase     :			int  							=      ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
          execute_subprocess_async(snake_case							,			env=os.environ.copy()      )


if __name__ == "__main__":
      __UpperCAmelCase      :Optional[Any]			    =       "/tmp/accelerate/state_checkpointing"
      __UpperCAmelCase      :Tuple			    =       DummyModel()
      __UpperCAmelCase      :Dict			    =       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      :Any			    =       dummy_dataloaders()
      __UpperCAmelCase      :Optional[int]			    =       ProjectConfiguration(automatic_checkpoint_naming=True)
      # Train baseline
      __UpperCAmelCase      :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      :Optional[int]			    =       group["params"][0].device
            break
      assert param_device.type == accelerator.device.type
      __UpperCAmelCase      :Union[str, 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:
            __UpperCAmelCase      :Optional[int]			    =       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()



'''simple docstring'''



from abc import ABC, abstractmethod
from typing import List, Optional



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Optional[Any]      )						->							int:
          # test for the above condition
          self.test()

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Optional[int]  							=      0
          __UpperCAmelCase     :			Any  							=      False
          while not completed:
               if counter == 1:
                    self.reset()
               __UpperCAmelCase     :			Optional[int]  							=      self.advance()
               if not self.does_advance(snake_case      ):
                    raise Exception(
                        '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''      )

               __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      self.update(snake_case      )
               counter += 1

               if counter > 1_0000:
                    raise Exception('''update() does not fulfill the constraint.'''      )

          if self.remaining() != 0:
               raise Exception('''Custom Constraint is not defined correctly.'''      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[str]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int      )						->							Optional[int]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		int      )						->							int:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[int]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		int      )						->							Tuple:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )






     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		List[Any]=False      )						->							Any:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		int							,			snake_case					:		List[int]      )						->							Tuple:
          super(snake_case							,			self      ).__init__()

          if not isinstance(snake_case							,			snake_case      ) or len(snake_case      ) == 0:
               raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.'      )
          if any((not isinstance(snake_case							,			snake_case      ) or token_id < 0) for token_id in token_ids      ):
               raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'      )

          __UpperCAmelCase     :			Dict  							=      token_ids

          __UpperCAmelCase     :			Tuple  							=      len(self.token_ids      )
          __UpperCAmelCase     :			List[str]  							=      -1  # the index of the currently fulfilled step
          __UpperCAmelCase     :			int  							=      False

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							str:
          if self.completed:
               return None
          return self.token_ids[self.fulfilled_idx + 1]

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Optional[int]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case      )}'      )

          if self.completed:
               return False

          return token_id == self.token_ids[self.fulfilled_idx + 1]

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		int      )						->							Optional[int]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			Any  							=      False
          __UpperCAmelCase     :			Tuple  							=      False
          __UpperCAmelCase     :			Tuple  							=      False

          if self.does_advance(snake_case      ):
               self.fulfilled_idx += 1
               __UpperCAmelCase     :			Union[str, Any]  							=      True
               if self.fulfilled_idx == (self.seqlen - 1):
                    __UpperCAmelCase     :			List[Any]  							=      True
               __UpperCAmelCase     :			Union[str, Any]  							=      completed
          else:
               # failed to make progress.
               __UpperCAmelCase     :			List[str]  							=      True
               self.reset()
          return stepped, completed, reset

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      False
          __UpperCAmelCase     :			Union[str, Any]  							=      0

     def 			lowerCamelCase__			(    self					:		str      )						->							Optional[int]:
          return self.seqlen - (self.fulfilled_idx + 1)






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Dict=False      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      PhrasalConstraint(self.token_ids      )

          if stateful:
               __UpperCAmelCase     :			int  							=      self.seqlen
               __UpperCAmelCase     :			Optional[Any]  							=      self.fulfilled_idx
               __UpperCAmelCase     :			List[Any]  							=      self.completed

          return new_constraint



class      a    :


     """simple docstring"""






     def __init__(    self					:		List[str]							,			snake_case					:		List[List[int]]							,			snake_case					:		Dict=True      )						->							Any:
          __UpperCAmelCase     :			List[Any]  							=      max([len(snake_case      ) for one in nested_token_ids]      )

          __UpperCAmelCase     :			Union[str, Any]  							=      {}
          for token_ids in nested_token_ids:
               __UpperCAmelCase     :			List[str]  							=      root
               for tidx, token_id in enumerate(snake_case      ):
                    if token_id not in level:
                         __UpperCAmelCase     :			List[Any]  							=      {}

                    __UpperCAmelCase     :			Tuple  							=      level[token_id]

          if no_subsets and self.has_subsets(snake_case							,			snake_case      ):
               raise ValueError(
                   '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
                   f' {nested_token_ids}.'      )

          __UpperCAmelCase     :			Tuple  							=      root

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[str]      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      self.trie

          for current_token in current_seq:
               __UpperCAmelCase     :			List[str]  							=      start[current_token]

          __UpperCAmelCase     :			str  							=      list(start.keys()      )

          return next_tokens

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		List[str]      )						->							Any:
          __UpperCAmelCase     :			Optional[Any]  							=      self.next_tokens(snake_case      )

          return len(snake_case      ) == 0

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		int      )						->							Optional[int]:
          __UpperCAmelCase     :			str  							=      list(root.values()      )
          if len(snake_case      ) == 0:
               return 1
          else:
               return sum([self.count_leaves(snake_case      ) for nn in next_nodes]      )






     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		Dict      )						->							str:
          __UpperCAmelCase     :			Dict  							=      self.count_leaves(snake_case      )
          return len(snake_case      ) != leaf_count



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		List[List[int]]      )						->							str:
          super(snake_case							,			self      ).__init__()

          if not isinstance(snake_case							,			snake_case      ) or len(snake_case      ) == 0:
               raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'      )
          if any(not isinstance(snake_case							,			snake_case      ) for token_ids in nested_token_ids      ):
               raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'      )
          if any(
              any((not isinstance(snake_case							,			snake_case      ) or token_id < 0) for token_id in token_ids      )
              for token_ids in nested_token_ids      ):
               raise ValueError(
                   f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'      )

          __UpperCAmelCase     :			Optional[int]  							=      DisjunctiveTrie(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      nested_token_ids

          __UpperCAmelCase     :			List[Any]  							=      self.trie.max_height
          __UpperCAmelCase     :			List[Any]  							=      []
          __UpperCAmelCase     :			Union[str, Any]  							=      False

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Optional[int]  							=      self.trie.next_tokens(self.current_seq      )

          if len(snake_case      ) == 0:
               return None
          else:
               return token_list

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		int      )						->							Dict:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			List[str]  							=      self.trie.next_tokens(self.current_seq      )

          return token_id in next_tokens

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Tuple:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			Dict  							=      False
          __UpperCAmelCase     :			Optional[Any]  							=      False
          __UpperCAmelCase     :			str  							=      False

          if self.does_advance(snake_case      ):
               self.current_seq.append(snake_case      )
               __UpperCAmelCase     :			int  							=      True
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      True
               self.reset()

          __UpperCAmelCase     :			Optional[Any]  							=      self.trie.reached_leaf(self.current_seq      )
          __UpperCAmelCase     :			Tuple  							=      completed

          return stepped, completed, reset

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			str  							=      False
          __UpperCAmelCase     :			Tuple  							=      []

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          if self.completed:
               # since this can be completed without reaching max height
               return 0
          else:
               return self.seqlen - len(self.current_seq      )






     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		Optional[int]=False      )						->							Tuple:
          __UpperCAmelCase     :			str  							=      DisjunctiveConstraint(self.token_ids      )

          if stateful:
               __UpperCAmelCase     :			Tuple  							=      self.seqlen
               __UpperCAmelCase     :			Dict  							=      self.current_seq
               __UpperCAmelCase     :			str  							=      self.completed

          return new_constraint



class      a    :


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		List[Constraint]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      constraints

          # max # of steps required to fulfill a given constraint
          __UpperCAmelCase     :			int  							=      max([c.seqlen for c in constraints]      )
          __UpperCAmelCase     :			int  							=      len(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      False

          self.init_state()

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							List[str]:
          __UpperCAmelCase     :			List[Any]  							=      []
          __UpperCAmelCase     :			List[str]  							=      None
          __UpperCAmelCase     :			str  							=      [constraint.copy(stateful=snake_case      ) for constraint in self.constraints]

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Dict  							=      0
          if self.inprogress_constraint:
               # extra points for having a constraint mid-fulfilled
               add += self.max_seqlen - self.inprogress_constraint.remaining()

          return (len(self.complete_constraints      ) * self.max_seqlen) + add

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							int:
          __UpperCAmelCase     :			int  							=      []
          if self.inprogress_constraint is None:
               for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                    __UpperCAmelCase     :			Optional[int]  							=      constraint.advance()
                    if isinstance(snake_case							,			snake_case      ):
                         token_list.append(snake_case      )
                    elif isinstance(snake_case							,			snake_case      ):
                         token_list.extend(snake_case      )
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      self.inprogress_constraint.advance()
               if isinstance(snake_case							,			snake_case      ):
                    token_list.append(snake_case      )
               elif isinstance(snake_case							,			snake_case      ):
                    token_list.extend(snake_case      )

          if len(snake_case      ) == 0:
               return None
          else:
               return token_list

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Optional[List[int]]      )						->							Optional[int]:
          self.init_state()

          if token_ids is not None:
               for token in token_ids:
                    # completes or steps **one** constraint
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.add(snake_case      )

                    # the entire list of constraints are fulfilled
                    if self.completed:
                         break

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		int      )						->							List[str]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.'      )

          __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      False, False

          if self.completed:
               __UpperCAmelCase     :			Union[str, Any]  							=      True
               __UpperCAmelCase     :			str  							=      False
               return complete, stepped

          if self.inprogress_constraint is not None:
               # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
               # job, simply update the state

               __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Any  							=      self.inprogress_constraint.update(snake_case      )
               if reset:
                    # 1. If the next token breaks the progress, then we must restart.
                    #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

                    #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
                    #     constraint, not the full list of constraints.

                    self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case      )      )
                    __UpperCAmelCase     :			Optional[Any]  							=      None

               if complete:
                    # 2. If the next token completes the constraint, move it to completed list, set
                    #     inprogress to None. If there are no pending constraints either, then this full list of constraints
                    #     is complete.

                    self.complete_constraints.append(self.inprogress_constraint      )
                    __UpperCAmelCase     :			str  							=      None

                    if len(self.pending_constraints      ) == 0:
                         # we're done!
                         __UpperCAmelCase     :			Optional[int]  							=      True

          else:
               # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
               # of constraints?

               for cidx, pending_constraint in enumerate(self.pending_constraints      ):
                    if pending_constraint.does_advance(snake_case      ):
                         __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Union[str, Any]  							=      pending_constraint.update(snake_case      )

                         if not stepped:
                              raise Exception(
                                  '''`constraint.update(token_id)` is not yielding incremental progress, '''
                                  '''even though `constraint.does_advance(token_id)` is true.'''      )

                         if complete:
                              self.complete_constraints.append(snake_case      )
                              __UpperCAmelCase     :			Tuple  							=      None

                         if not complete and stepped:
                              __UpperCAmelCase     :			List[Any]  							=      pending_constraint

                         if complete or stepped:
                              # If we made any progress at all, then it's at least not a "pending constraint".

                              __UpperCAmelCase     :			Optional[int]  							=      (
                                  self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                              )

                              if len(self.pending_constraints      ) == 0 and self.inprogress_constraint is None:
                                   # If there's no longer any pending after this and no inprogress either, then we must be
                                   # complete.

                                   __UpperCAmelCase     :			Any  							=      True

                              break  # prevent accidentally stepping through multiple constraints with just one token.

          return complete, stepped






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Optional[int]=True      )						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      ConstraintListState(self.constraints      )  # we actually never though self.constraints objects
          # throughout this process. So it's at initialization state.

          if stateful:
               __UpperCAmelCase     :			str  							=      [
                   constraint.copy(stateful=snake_case      ) for constraint in self.complete_constraints
               ]
               if self.inprogress_constraint is not None:
                    __UpperCAmelCase     :			Union[str, Any]  							=      self.inprogress_constraint.copy(stateful=snake_case      )
               __UpperCAmelCase     :			Tuple  							=      [constraint.copy() for constraint in self.pending_constraints]

          return new_state