

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_ :				Union[str, Any]						 =    logging.get_logger(__name__)

A_ :				Any						 =    {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Tuple													= '''table-transformer'''
					UpperCAmelCase__:     Optional[int]													= ['''past_key_values''']
					UpperCAmelCase__:     List[Any]													= {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}






					def __init__(				self					, A__=True					, A__=None					, A__=3					, A__=100					, A__=6					, A__=2048					, A__=8					, A__=6					, A__=2048					, A__=8					, A__=0.0					, A__=0.0					, A__=True					, A__="relu"					, A__=256					, A__=0.1					, A__=0.0					, A__=0.0					, A__=0.0_2					, A__=1.0					, A__=False					, A__="sine"					, A__="resnet50"					, A__=True					, A__=False					, A__=1					, A__=5					, A__=2					, A__=1					, A__=1					, A__=5					, A__=2					, A__=0.1					, **A__					, ):
						if backbone_config is not None and use_timm_backbone:
							raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""    )

						if not use_timm_backbone:
							if backbone_config is None:
								logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
								A__							:					Dict       =	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
							elif isinstance(A__					, A__    ):
								A__							:					Any       =	backbone_config.get("""model_type"""    )
								A__							:					int       =	CONFIG_MAPPING[backbone_model_type]
								A__							:					str       =	config_class.from_dict(A__    )
							# set timm attributes to None
							A__     ,     A__     ,     A__							:					Any       =	None, None, None

						A__							:					Optional[int]       =	use_timm_backbone
						A__							:					List[str]       =	backbone_config
						A__							:					Any       =	num_channels
						A__							:					Union[str, Any]       =	num_queries
						A__							:					Optional[int]       =	d_model
						A__							:					Union[str, Any]       =	encoder_ffn_dim
						A__							:					str       =	encoder_layers
						A__							:					List[Any]       =	encoder_attention_heads
						A__							:					str       =	decoder_ffn_dim
						A__							:					Any       =	decoder_layers
						A__							:					Tuple       =	decoder_attention_heads
						A__							:					List[Any]       =	dropout
						A__							:					int       =	attention_dropout
						A__							:					Any       =	activation_dropout
						A__							:					Tuple       =	activation_function
						A__							:					Tuple       =	init_std
						A__							:					Union[str, Any]       =	init_xavier_std
						A__							:					int       =	encoder_layerdrop
						A__							:					List[str]       =	decoder_layerdrop
						A__							:					Any       =	encoder_layers
						A__							:					List[str]       =	auxiliary_loss
						A__							:					List[str]       =	position_embedding_type
						A__							:					Tuple       =	backbone
						A__							:					List[Any]       =	use_pretrained_backbone
						A__							:					List[Any]       =	dilation
						# Hungarian matcher
						A__							:					Tuple       =	class_cost
						A__							:					Union[str, Any]       =	bbox_cost
						A__							:					List[Any]       =	giou_cost
						# Loss coefficients
						A__							:					Any       =	mask_loss_coefficient
						A__							:					int       =	dice_loss_coefficient
						A__							:					Union[str, Any]       =	bbox_loss_coefficient
						A__							:					str       =	giou_loss_coefficient
						A__							:					int       =	eos_coefficient
						super().__init__(is_encoder_decoder=A__					, **A__    )






					@property
					def 						__A    (				self    ):
						return self.encoder_attention_heads






					@property
					def 						__A    (				self    ):
						return self.d_model



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Dict													= version.parse('''1.11'''       )






					@property
					def 						__A    (				self    ):
						return OrderedDict(
						    [
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						        ("""pixel_mask""", {0: """batch"""}),
						    ]    )






					@property
					def 						__A    (				self    ):
						return 1e-5






					@property
					def 						__A    (				self    ):
						return 12






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_ :				Any						 =    logging.getLogger(__name__)

def 		UpperCamelCase      (lowercase_:				Optional[Any]=2					,   lowercase_:				Union[str, Any]=3					,   lowercase_:				int=16					,   lowercase_:				int = 10					,   lowercase_:				int = 2      )  ->   int:

	def get_dataset(lowercase_:				Optional[int]      ):
		A__							:					Optional[Any]       =	torch.randn(batch_size * n_batches					,   1      )
		return TensorDataset(lowercase_					,   a * x + b + 0.1 * torch.randn(batch_size * n_batches					,   1      )      )

	A__							:					Dict       =	get_dataset(lowercase_      )
	A__							:					Any       =	get_dataset(lowercase_      )
	A__							:					Dict       =	DataLoader(lowercase_					,   shuffle=lowercase_					,   batch_size=lowercase_					,   num_workers=4      )
	A__							:					Optional[Any]       =	DataLoader(lowercase_					,   shuffle=lowercase_					,   batch_size=lowercase_					,   num_workers=4      )
	return (train_dataloader, valid_dataloader)

def 		UpperCamelCase      (lowercase_:				Optional[Any]					,   lowercase_:				List[str]					,   lowercase_:				int					,   lowercase_:				int					,   lowercase_:				List[str]					,   lowercase_:				Dict=None      )  ->   List[Any]:
	A__							:					List[Any]       =	[]
	for epoch in range(lowercase_      ):
		# Train quickly
		model.train()
		for batch in dataloader:
			A__     ,     A__							:					Any       =	batch
			A__							:					Any       =	model(lowercase_      )
			A__							:					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    ):
						super().__init__()
						A__							:					str       =	nn.Parameter(torch.randn(1    )    )
						A__							:					Any       =	nn.Parameter(torch.randn(1    )    )






					def 						__A    (				self					, A__    ):
						return x * self.a + self.b



class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






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

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






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

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

							A__							:					List[str]       =	train(2					, A__					, A__					, A__					, A__    )
							# Save everything
							A__							:					Optional[int]       =	os.path.join(A__					, """checkpoint"""    )
							accelerator.save_state(A__    )

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






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					int       =	DummyModel()
							A__							:					Optional[int]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__     ,     A__							:					List[str]       =	dummy_dataloaders()
							A__							:					str       =	ProjectConfiguration(automatic_checkpoint_naming=A__    )

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

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

							A__							:					str       =	train(2					, A__					, A__					, A__					, A__    )
							# Save everything
							accelerator.save_state()

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(A__					, """checkpoints"""					, """checkpoint_1"""    )    )
							test_rands += train(1					, A__					, A__					, A__					, A__    )
							((A__)     ,     (A__))							:					Optional[int]       =	model.a.item(), model.b.item()
							A__							:					List[Any]       =	optimizer.state_dict()
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	torch.tensor([1, 2, 3]    )
						A__							:					int       =	torch.tensor([2, 3, 4]    )
						A__							:					List[Any]       =	DummyModel()
						A__							:					List[Any]       =	torch.optim.Adam(net.parameters()    )
						A__							:					Tuple       =	Accelerator()
						with self.assertRaises(A__    ) as ve:
							accelerator.register_for_checkpointing(A__					, A__					, A__					, A__    )
						A__							:					Any       =	str(ve.exception    )
						self.assertTrue("""Item at index 0""" in message    )
						self.assertTrue("""Item at index 1""" in message    )
						self.assertFalse("""Item at index 2""" in message    )
						self.assertFalse("""Item at index 3""" in message    )






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					Any       =	DummyModel()
							A__							:					Union[str, Any]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__							:					Dict       =	torch.optim.lr_scheduler.StepLR(A__					, step_size=1					, gamma=0.9_9    )
							A__     ,     A__							:					List[Any]       =	dummy_dataloaders()
							A__							:					Tuple       =	ProjectConfiguration(automatic_checkpoint_naming=A__    )
							# Train baseline
							A__							:					Optional[Any]       =	Accelerator(project_dir=A__					, project_config=A__    )
							A__     ,     A__     ,     A__     ,     A__     ,     A__							:					Union[str, Any]       =	accelerator.prepare(
							    A__					, A__					, A__					, A__					, A__    )
							# Save initial
							accelerator.save_state()
							A__							:					Tuple       =	scheduler.state_dict()
							train(3					, A__					, A__					, A__					, A__					, A__    )
							self.assertNotEqual(A__					, scheduler.state_dict()    )

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






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






					@require_cuda
					def 						__A    (				self    ):
						A__							:					Dict       =	["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
						execute_subprocess_async(A__					, env=os.environ.copy()    )


if __name__ == "__main__":
	A_ :				List[str]						 =    '/tmp/accelerate/state_checkpointing'
	A_ :				Optional[Any]						 =    DummyModel()
	A_ :				Union[str, Any]						 =    torch.optim.Adam(params=model.parameters(), lr=1E-3)
	A_ :				str						 =    torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
	A_ ,    A_ :				List[Any]						 =    dummy_dataloaders()
	A_ :				int						 =    ProjectConfiguration(automatic_checkpoint_naming=True)
	# Train baseline
	A_ :				List[str]						 =    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_ :				List[Any]						 =    accelerator.prepare(
	    model, optimizer, train_dataloader, valid_dataloader, scheduler
	)
	A_ ,    A_ :				Dict						 =    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_ :				str						 =    group['params'][0].device
		break
	assert param_device.type == accelerator.device.type
	A_ :				Optional[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:
		A_ :				str						 =    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_ :				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()



