



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


a__      :				str   	=			logging.getLogger(__name__)

def   _lowerCAmelCase   (						A__=2						,  A__=3						,  A__=16						,  A__ = 10						,  A__ = 2	):

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

      lowercase__     =  get_dataset(A__	)
      lowercase__     =  get_dataset(A__	)
      lowercase__     =  DataLoader(A__						,  shuffle=A__						,  batch_size=A__						,  num_workers=4	)
      lowercase__     =  DataLoader(A__						,  shuffle=A__						,  batch_size=A__						,  num_workers=4	)
      return (train_dataloader, valid_dataloader)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__						,  A__=None	):
      lowercase__     =  []
      for epoch in range(A__	):
            # Train quickly
            model.train()
            for batch in dataloader:
                  lowercase__,	lowercase__     =  batch
                  lowercase__     =  model(A__	)
                  lowercase__     =  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  :			Tuple)	->				Tuple:




         """simple docstring"""

         super().__init__()
         lowercase__     =  nn.Parameter(torch.randn(1))
         lowercase__     =  nn.Parameter(torch.randn(1))



   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			str)	->				List[Any]:




         """simple docstring"""

         return x * self.a + self.b



class       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			int)	->				str:




         """simple docstring"""

         with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42)
               lowercase__     =  DummyModel()
               lowercase__     =  torch.optim.Adam(params=model.parameters()			,				lr=1E-3)
               lowercase__,	lowercase__     =  dummy_dataloaders()
               lowercase__     =  ProjectConfiguration(total_limit=1			,				project_dir=lowerCAmelCase			,				automatic_checkpoint_naming=lowerCAmelCase)
               # Train baseline
               lowercase__     =  Accelerator(project_config=lowerCAmelCase)
               lowercase__,	lowercase__,	lowercase__,	lowercase__     =  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[Any])	->				str:




         """simple docstring"""

         with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42)
               lowercase__     =  DummyModel()
               lowercase__     =  torch.optim.Adam(params=model.parameters()			,				lr=1E-3)
               lowercase__,	lowercase__     =  dummy_dataloaders()
               # Train baseline
               lowercase__     =  Accelerator()
               lowercase__,	lowercase__,	lowercase__,	lowercase__     =  accelerator.prepare(
                   lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               # Save initial
               lowercase__     =  os.path.join(lowerCAmelCase			,				'initial')
               accelerator.save_state(lowerCAmelCase)
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()
               lowercase__     =  train(3			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()

               # Train partially
               set_seed(42)
               lowercase__     =  DummyModel()
               lowercase__     =  torch.optim.Adam(params=model.parameters()			,				lr=1E-3)
               lowercase__,	lowercase__     =  dummy_dataloaders()
               lowercase__     =  Accelerator()
               lowercase__,	lowercase__,	lowercase__,	lowercase__     =  accelerator.prepare(
                   lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               accelerator.load_state(lowerCAmelCase)
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)

               lowercase__     =  train(2			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               # Save everything
               lowercase__     =  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)
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)


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




         """simple docstring"""

         with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42)
               lowercase__     =  DummyModel()
               lowercase__     =  torch.optim.Adam(params=model.parameters()			,				lr=1E-3)
               lowercase__,	lowercase__     =  dummy_dataloaders()
               lowercase__     =  ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase)

               # Train baseline
               lowercase__     =  Accelerator(project_dir=lowerCAmelCase			,				project_config=lowerCAmelCase)
               lowercase__,	lowercase__,	lowercase__,	lowercase__     =  accelerator.prepare(
                   lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               # Save initial
               accelerator.save_state()
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()
               lowercase__     =  train(3			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()

               # Train partially
               set_seed(42)
               lowercase__     =  DummyModel()
               lowercase__     =  torch.optim.Adam(params=model.parameters()			,				lr=1E-3)
               lowercase__,	lowercase__     =  dummy_dataloaders()
               lowercase__     =  ProjectConfiguration(iteration=1			,				automatic_checkpoint_naming=lowerCAmelCase)
               lowercase__     =  Accelerator(project_dir=lowerCAmelCase			,				project_config=lowerCAmelCase)
               lowercase__,	lowercase__,	lowercase__,	lowercase__     =  accelerator.prepare(
                   lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               accelerator.load_state(os.path.join(lowerCAmelCase			,				'checkpoints'			,				'checkpoint_0'))
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)

               lowercase__     =  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)
               ((lowercase__),	(lowercase__))     =  model.a.item(), model.b.item()
               lowercase__     =  optimizer.state_dict()
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)
               self.assertEqual(lowerCAmelCase			,				lowerCAmelCase)


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




         """simple docstring"""

         lowercase__     =  torch.tensor([1, 2, 3])
         lowercase__     =  torch.tensor([2, 3, 4])
         lowercase__     =  DummyModel()
         lowercase__     =  torch.optim.Adam(net.parameters())
         lowercase__     =  Accelerator()
         with self.assertRaises(lowerCAmelCase) as ve:
               accelerator.register_for_checkpointing(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
         lowercase__     =  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  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42)
               lowercase__     =  DummyModel()
               lowercase__     =  torch.optim.Adam(params=model.parameters()			,				lr=1E-3)
               lowercase__     =  torch.optim.lr_scheduler.StepLR(lowerCAmelCase			,				step_size=1			,				gamma=0.99)
               lowercase__,	lowercase__     =  dummy_dataloaders()
               lowercase__     =  ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase)
               # Train baseline
               lowercase__     =  Accelerator(project_dir=lowerCAmelCase			,				project_config=lowerCAmelCase)
               lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  accelerator.prepare(
                   lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase)
               # Save initial
               accelerator.save_state()
               lowercase__     =  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  :			List[Any])	->				List[str]:




         """simple docstring"""

         with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(42)
               lowercase__     =  DummyModel()
               lowercase__     =  ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase			,				total_limit=2)
               # Train baseline
               lowercase__     =  Accelerator(project_dir=lowerCAmelCase			,				project_config=lowerCAmelCase)
               lowercase__     =  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  :			Optional[int])	->				Any:




         """simple docstring"""

         lowercase__     =  ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__)]
         execute_subprocess_async(lowerCAmelCase			,				env=os.environ.copy())


if __name__ == "__main__":
       a__      :				Tuple   	=			"/tmp/accelerate/state_checkpointing"
       a__      :				List[str]   	=			DummyModel()
       a__      :				Any   	=			torch.optim.Adam(params=model.parameters(), lr=1E-3)
       a__      :				Any   	=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
       a__	,		a__      :				List[Any]   	=			dummy_dataloaders()
       a__      :				Optional[int]   	=			ProjectConfiguration(automatic_checkpoint_naming=True)
       # Train baseline
       a__      :				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)
       a__	,		a__	,		a__	,		a__	,		a__      :				Any   	=			accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       a__	,		a__      :				Union[str, Any]   	=			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:
              a__      :				Dict   	=			group["params"][0].device
              break
       assert param_device.type == accelerator.device.type
       a__      :				Tuple   	=			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:
              a__      :				List[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:
              a__      :				Union[str, Any]   	=			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()





import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__      :				Optional[int]   	=			logging.get_logger(__name__)


a__      :				Dict   	=			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

a__      :				str   	=			{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

a__      :				Any   	=			{"facebook/blenderbot_small-90M": 5_12}

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  set()
      lowercase__     =  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)	)
            lowercase__     =  char

      lowercase__     =  set(A__	)
      return pairs



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[str]       				=    VOCAB_FILES_NAMES
   A    :  str       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  Tuple       				=    ["input_ids", "attention_mask"]


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			int="__start__"			,				lowerCAmelCase  :			Dict="__end__"			,				lowerCAmelCase  :			Any="__unk__"			,				lowerCAmelCase  :			str="__null__"			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				List[str]:




         """simple docstring"""

         super().__init__(unk_token=lowerCAmelCase			,				bos_token=lowerCAmelCase			,				eos_token=lowerCAmelCase			,				pad_token=lowerCAmelCase			,				**lowerCAmelCase)

         with open(lowerCAmelCase			,				encoding='utf-8') as vocab_handle:
               lowercase__     =  json.load(lowerCAmelCase)
         lowercase__     =  {v: k for k, v in self.encoder.items()}
         with open(lowerCAmelCase			,				encoding='utf-8') as merges_handle:
               lowercase__     =  merges_handle.read().split('\n')[1:-1]
         lowercase__     =  [tuple(merge.split()) for merge in merges]
         lowercase__     =  dict(zip(lowerCAmelCase			,				range(len(lowerCAmelCase))))
         lowercase__     =  {}


   @property
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return len(self.encoder)


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         return dict(self.encoder			,				**self.added_tokens_encoder)


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str)	->				str:




         """simple docstring"""

         if token in self.cache:
               return self.cache[token]
         lowercase__     =  re.sub('([.,!?()])'			,				R' \1'			,				lowerCAmelCase)
         lowercase__     =  re.sub('(\')'			,				R' \1 '			,				lowerCAmelCase)
         lowercase__     =  re.sub(R'\s{2,}'			,				' '			,				lowerCAmelCase)
         if "\n" in token:
               lowercase__     =  token.replace('\n'			,				' __newln__')

         lowercase__     =  token.split(' ')
         lowercase__     =  []
         for token in tokens:
               if not len(lowerCAmelCase):
                     continue

               lowercase__     =  token.lower()
               lowercase__     =  tuple(lowerCAmelCase)
               lowercase__     =  tuple(list(word[:-1]) + [word[-1] + '</w>'])
               lowercase__     =  get_pairs(lowerCAmelCase)

               if not pairs:
                     words.append(lowerCAmelCase)
                     continue

               while True:
                     lowercase__     =  min(lowerCAmelCase			,				key=lambda lowerCAmelCase: self.bpe_ranks.get(lowerCAmelCase			,				float('inf')))
                     if bigram not in self.bpe_ranks:
                           break
                     lowercase__,	lowercase__     =  bigram
                     lowercase__     =  []
                     lowercase__     =  0

                     while i < len(lowerCAmelCase):
                           try:
                                 lowercase__     =  word.index(lowerCAmelCase			,				lowerCAmelCase)
                                 new_word.extend(word[i:j])
                                 lowercase__     =  j
                           except ValueError:
                                 new_word.extend(word[i:])
                                 break

                           if word[i] == first and i < len(lowerCAmelCase) - 1 and word[i + 1] == second:
                                 new_word.append(first + second)
                                 i += 2
                           else:
                                 new_word.append(word[i])
                                 i += 1
                     lowercase__     =  tuple(lowerCAmelCase)
                     lowercase__     =  new_word
                     if len(lowerCAmelCase) == 1:
                           break
                     else:
                           lowercase__     =  get_pairs(lowerCAmelCase)
               lowercase__     =  '@@ '.join(lowerCAmelCase)
               lowercase__     =  word[:-4]

               lowercase__     =  word
               words.append(lowerCAmelCase)
         return " ".join(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__     =  []

         lowercase__     =  re.findall(R'\S+\n?'			,				lowerCAmelCase)

         for token in words:
               split_tokens.extend(list(self.bpe(lowerCAmelCase).split(' ')))
         return split_tokens


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         lowercase__     =  token.lower()
         return self.encoder.get(lowerCAmelCase			,				self.encoder.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self.decoder.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			List[str])	->				str:




         """simple docstring"""

         lowercase__     =  ' '.join(lowerCAmelCase).replace('@@ '			,				'').strip()
         return out_string



   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str] = None)	->				Tuple[str]:




         """simple docstring"""

         if not os.path.isdir(lowerCAmelCase):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
               return
         lowercase__     =  os.path.join(
             lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
         lowercase__     =  os.path.join(
             lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

         with open(lowerCAmelCase			,				'w'			,				encoding='utf-8') as f:
               f.write(json.dumps(self.encoder			,				indent=2			,				sort_keys=lowerCAmelCase			,				ensure_ascii=lowerCAmelCase) + '\n')

         lowercase__     =  0
         with open(lowerCAmelCase			,				'w'			,				encoding='utf-8') as writer:
               writer.write('#version: 0.2\n')
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,				key=lambda lowerCAmelCase: kv[1]):
                     if index != token_index:
                           logger.warning(
                               f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                               ' Please check that the tokenizer is not corrupted!')
                           lowercase__     =  token_index
                     writer.write(' '.join(lowerCAmelCase) + '\n')
                     index += 1

         return vocab_file, merge_file
