


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 							__lowercase   ( __lowerCAmelCase							:				str   ):
							a__      ,					a__             =     analyze_text(__lowerCAmelCase   )
							a__             =     list(' ' + ascii_lowercase   )
							# what is our total sum of probabilities.
							a__             =     sum(single_char_strings.values()   )

							# one length string
							a__             =     0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					a__             =     single_char_strings[ch]
																					a__             =     my_str / all_sum
																					my_fir_sum += prob * math.loga(__lowerCAmelCase   )  # entropy formula.

    # print entropy
							print(F'{round(-1 * my_fir_sum   ):.1f}'   )

							# two len string
							a__             =     sum(two_char_strings.values()   )
							a__             =     0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					a__             =     cha + cha
																					if sequence in two_char_strings:
																												a__             =     two_char_strings[sequence]
																												a__             =     int(__lowerCAmelCase   ) / all_sum
																												my_sec_sum += prob * math.loga(__lowerCAmelCase   )

    # print second entropy
							print(F'{round(-1 * my_sec_sum   ):.1f}'   )

							# print the difference between them
							print(F'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)   ):.1f}'   )






def 							__lowercase   ( __lowerCAmelCase							:				str   ):
							a__             =     Counter()  # type: ignore
							a__             =     Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0					,    len(__lowerCAmelCase   ) - 1   ):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings






def 							__lowercase   ( ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
						main()








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


snake_case							:       List[Any]     			=      logging.getLogger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				str=2					,    __lowerCAmelCase							:				Optional[int]=3					,    __lowerCAmelCase							:				List[str]=1_6					,    __lowerCAmelCase							:				int = 1_0					,    __lowerCAmelCase							:				int = 2   ):

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

							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							return (train_dataloader, valid_dataloader)






def 							__lowercase   ( __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Optional[int]=None   ):
							a__             =     []
							for epoch in range(__lowerCAmelCase   ):
														# Train quickly
														model.train()
														for batch in dataloader:
																					a__      ,					a__             =     batch
																					a__             =     model(__lowerCAmelCase   )
																					a__             =     torch.nn.functional.mse_loss(__lowerCAmelCase					,    __lowerCAmelCase   )
																					accelerator.backward(__lowerCAmelCase   )
																					optimizer.step()
																					optimizer.zero_grad()
														rands.append(random.random()   )  # Introduce some randomness
														if scheduler is not None:
																					scheduler.step()
							return rands







class        snake_case_   (nn.Module      ):
					def __init__(      self   :Any      )					->    Union[str, Any]:
												super().__init__()
												a__             =     nn.Parameter(torch.randn(1      )      )
												a__             =     nn.Parameter(torch.randn(1      )      )



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







class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(total_limit=1			,project_dir=__snake_case			,automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     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[Any]      )					->    str:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			# Train baseline
																			a__             =     Accelerator()
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			a__             =     os.path.join(__snake_case			,'initial'      )
																			accelerator.save_state(__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			a__             =     train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()

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

																			a__             =     train(2			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save everything
																			a__             =     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      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     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      )					->    List[str]:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case      )

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

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

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




