



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


lowerCamelCase__			= logging.getLogger(__name__)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int=2				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]=3				,     _SCREAMING_SNAKE_CASE       :			List[str]=16				,     _SCREAMING_SNAKE_CASE       :			int = 10				,     _SCREAMING_SNAKE_CASE       :			int = 2    ):
				"""simple docstring"""







				def get_dataset(_SCREAMING_SNAKE_CASE       :			List[str]    ):
								__a       =		torch.randn(batch_size * n_batches				,     1    )
								return TensorDataset(_SCREAMING_SNAKE_CASE				,     a * x + b + 0.1 * torch.randn(batch_size * n_batches				,     1    )    )

				__a       =		get_dataset(_SCREAMING_SNAKE_CASE    )
				__a       =		get_dataset(_SCREAMING_SNAKE_CASE    )
				__a       =		DataLoader(_SCREAMING_SNAKE_CASE				,     shuffle=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE				,     num_workers=4    )
				__a       =		DataLoader(_SCREAMING_SNAKE_CASE				,     shuffle=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE				,     num_workers=4    )
				return (train_dataloader, valid_dataloader)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			Any=None    ):
				"""simple docstring"""







				__a       =		[]
				for epoch in range(_SCREAMING_SNAKE_CASE    ):
								# Train quickly
								model.train()
								for batch in dataloader:
												__a     ,					__a       =		batch
												__a       =		model(_SCREAMING_SNAKE_CASE    )
												__a       =		torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
												accelerator.backward(_SCREAMING_SNAKE_CASE    )
												optimizer.step()
												optimizer.zero_grad()
								rands.append(random.random()    )  # Introduce some randomness
								if scheduler is not None:
												scheduler.step()
				return rands






class    SCREAMING_SNAKE_CASE			(							nn.Module					):





							def __init__(   self		:		List[str]	):

											'''simple docstring'''
											super().__init__()
											__a       =		nn.Parameter(torch.randn(1	)	)
											__a       =		nn.Parameter(torch.randn(1	)	)







							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		Dict	):

											'''simple docstring'''
											return x * self.a + self.b






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											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=__lowercase      ,				automatic_checkpoint_naming=__lowercase	)
															# Train baseline
															__a       =		Accelerator(project_config=__lowercase	)
															__a     ,					__a     ,					__a     ,					__a       =		accelerator.prepare(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															# Save initial
															accelerator.save_state()

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





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											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(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															# Save initial
															__a       =		os.path.join(__lowercase      ,				"""initial"""	)
															accelerator.save_state(__lowercase	)
															((__a)     ,					(__a))       =		model.a.item(), model.b.item()
															__a       =		optimizer.state_dict()
															__a       =		train(3      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															((__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(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															accelerator.load_state(__lowercase	)
															((__a)     ,					(__a))       =		model.a.item(), model.b.item()
															__a       =		optimizer.state_dict()
															self.assertEqual(__lowercase      ,				__lowercase	)
															self.assertEqual(__lowercase      ,				__lowercase	)
															self.assertEqual(__lowercase      ,				__lowercase	)

															__a       =		train(2      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															# Save everything
															__a       =		os.path.join(__lowercase      ,				"""checkpoint"""	)
															accelerator.save_state(__lowercase	)

															# Load everything back in and make sure all states work
															accelerator.load_state(__lowercase	)
															test_rands += train(1      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															((__a)     ,					(__a))       =		model.a.item(), model.b.item()
															__a       =		optimizer.state_dict()
															self.assertEqual(__lowercase      ,				__lowercase	)
															self.assertEqual(__lowercase      ,				__lowercase	)
															self.assertEqual(__lowercase      ,				__lowercase	)
															self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											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=__lowercase	)

															# Train baseline
															__a       =		Accelerator(project_dir=__lowercase      ,				project_config=__lowercase	)
															__a     ,					__a     ,					__a     ,					__a       =		accelerator.prepare(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															# Save initial
															accelerator.save_state()
															((__a)     ,					(__a))       =		model.a.item(), model.b.item()
															__a       =		optimizer.state_dict()
															__a       =		train(3      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															((__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=__lowercase	)
															__a       =		Accelerator(project_dir=__lowercase      ,				project_config=__lowercase	)
															__a     ,					__a     ,					__a     ,					__a       =		accelerator.prepare(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															accelerator.load_state(os.path.join(__lowercase      ,				"""checkpoints"""      ,				"""checkpoint_0"""	)	)
															((__a)     ,					(__a))       =		model.a.item(), model.b.item()
															__a       =		optimizer.state_dict()
															self.assertEqual(__lowercase      ,				__lowercase	)
															self.assertEqual(__lowercase      ,				__lowercase	)
															self.assertEqual(__lowercase      ,				__lowercase	)

															__a       =		train(2      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															# Save everything
															accelerator.save_state()

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





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__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(__lowercase	) as ve:
															accelerator.register_for_checkpointing(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__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 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											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(__lowercase      ,				step_size=1      ,				gamma=0.99	)
															__a     ,					__a       =		dummy_dataloaders()
															__a       =		ProjectConfiguration(automatic_checkpoint_naming=__lowercase	)
															# Train baseline
															__a       =		Accelerator(project_dir=__lowercase      ,				project_config=__lowercase	)
															__a     ,					__a     ,					__a     ,					__a     ,					__a       =		accelerator.prepare(
															    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															# Save initial
															accelerator.save_state()
															__a       =		scheduler.state_dict()
															train(3      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
															self.assertNotEqual(__lowercase      ,				scheduler.state_dict()	)

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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42	)
															__a       =		DummyModel()
															__a       =		ProjectConfiguration(automatic_checkpoint_naming=__lowercase      ,				total_limit=2	)
															# Train baseline
															__a       =		Accelerator(project_dir=__lowercase      ,				project_config=__lowercase	)
															__a       =		accelerator.prepare(__lowercase	)
															# Save 3 states:
															for _ in range(11	):
																			accelerator.save_state()
															self.assertTrue(not os.path.exists(os.path.join(__lowercase      ,				"""checkpoints"""      ,				"""checkpoint_0"""	)	)	)
															self.assertTrue(os.path.exists(os.path.join(__lowercase      ,				"""checkpoints"""      ,				"""checkpoint_9"""	)	)	)
															self.assertTrue(os.path.exists(os.path.join(__lowercase      ,				"""checkpoints"""      ,				"""checkpoint_10"""	)	)	)







							@require_cuda
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		["""torchrun""", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__	)]
											execute_subprocess_async(__lowercase      ,				env=os.environ.copy()	)


if __name__ == "__main__":
						lowerCamelCase__			= """/tmp/accelerate/state_checkpointing"""
						lowerCamelCase__			= DummyModel()
						lowerCamelCase__			= torch.optim.Adam(params=model.parameters(), lr=1e-3)
						lowerCamelCase__			= torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
						lowerCamelCase__			,							lowerCamelCase__			= dummy_dataloaders()
						lowerCamelCase__			= ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						lowerCamelCase__			= 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)
						lowerCamelCase__			,							lowerCamelCase__			,							lowerCamelCase__			,							lowerCamelCase__			,							lowerCamelCase__			= accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						lowerCamelCase__			,							lowerCamelCase__			= 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:
												lowerCamelCase__			= group["""params"""][0].device
												break
						assert param_device.type == accelerator.device.type
						lowerCamelCase__			= 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:
												lowerCamelCase__			= 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:
												lowerCamelCase__			= 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
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =BlenderbotSmallTokenizer
							__lowerCamelCase	:	str    =False





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											super().setUp()

											__a       =		["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
											__a       =		dict(zip(__lowercase      ,				range(len(__lowercase	)	)	)	)

											__a       =		["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
											__a       =		{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""merges_file"""]	)
											with open(self.vocab_file      ,				"""w"""      ,				encoding="""utf-8"""	) as fp:
															fp.write(json.dumps(__lowercase	) + """\n"""	)
											with open(self.merges_file      ,				"""w"""      ,				encoding="""utf-8"""	) as fp:
															fp.write("""\n""".join(__lowercase	)	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				**__lowercase		:		Dict	):

											'''simple docstring'''
											kwargs.update(self.special_tokens_map	)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		"""adapt act apte"""
											__a       =		"""adapt act apte"""
											return input_text, output_text





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map	)
											__a       =		"""adapt act apte"""
											__a       =		["""adapt""", """act""", """ap@@""", """te"""]
											__a       =		tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											__a       =		[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase	)      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											assert tok("""sam"""	).input_ids == [1384]
											__a       =		"""I am a small frog."""
											__a       =		tok([src_text]      ,				padding=__lowercase      ,				truncation=__lowercase	)["""input_ids"""]
											__a       =		tok.batch_decode(__lowercase      ,				skip_special_tokens=__lowercase      ,				clean_up_tokenization_spaces=__lowercase	)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											__a       =		"""I am a small frog ."""
											__a       =		"""."""
											__a       =		tok(__lowercase	)["""input_ids"""]
											__a       =		tok(__lowercase	)["""input_ids"""]

											assert encoded[-1] == encoded_dot[0]
