
import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment





def   _lowerCAmelCase				(   ) ->   Optional[Any]:





	"""simple docstring"""







	A								=				torch.nn.Linear(2							,					4						)
	A								=				torch.optim.AdamW(model.parameters()							,					lr=1.0						)
	A								=				torch.optim.lr_scheduler.OneCycleLR(_lowercase							,					max_lr=0.01							,					steps_per_epoch=2							,					epochs=1						)
	A								=				DataLoader(TensorDataset(torch.tensor([1, 2, 3]						)						)						)
	A								=				DataLoader(TensorDataset(torch.tensor([4, 5, 6]						)						)						)

	return model, optimizer, scheduler, train_dl, valid_dl





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   List[str]:





	"""simple docstring"""







	return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				torch.nn.Linear(*tuple(model.weight.T.shape						)						).state_dict()
	model.load_state_dict(_lowercase						)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				@require_cuda
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				Accelerator()
					assert PartialState._shared_state["_cpu"] is False
					assert PartialState._shared_state["device"].type == "cuda"
					with self.assertRaises(__A   ):
						A								=				Accelerator(cpu=__A   )

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				Accelerator()
					A								=				GradientState()
					assert state.num_steps == 1
					A								=				4
					assert state.num_steps == 4

					assert state.sync_gradients is True
					A								=				False
					assert state.sync_gradients is False
					GradientState._reset_state()

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					A								=				Accelerator()
					A					,			A					,			A					,			A					,			A								=				create_components()

					(
					    (
					    A
					)					,			(
					    A
					)					,			(
					    A
					)					,			(
					    A
					)					,			(
					    A
					)					,			
					)								=				accelerator.prepare(__A				,   __A				,   __A				,   __A				,   __A   )

					self.assertTrue(prepared_model in accelerator._models   )
					self.assertTrue(prepared_optimizer in accelerator._optimizers   )
					self.assertTrue(prepared_scheduler in accelerator._schedulers   )
					self.assertTrue(prepared_train_dl in accelerator._dataloaders   )
					self.assertTrue(prepared_valid_dl in accelerator._dataloaders   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				Accelerator()
					A					,			A					,			A					,			A					,			A								=				create_components()
					accelerator.prepare(__A				,   __A				,   __A				,   __A				,   __A   )
					accelerator.free_memory()

					self.assertTrue(len(accelerator._models   ) == 0   )
					self.assertTrue(len(accelerator._optimizers   ) == 0   )
					self.assertTrue(len(accelerator._schedulers   ) == 0   )
					self.assertTrue(len(accelerator._dataloaders   ) == 0   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					PartialState._reset_state()

					# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
					def noop(*a__				,   **a__   ):
						pass

					with patch("""torch.cuda.set_device"""				,   __A   ), patch_environment(ACCELERATE_TORCH_DEVICE="""cuda:64"""   ):
						A								=				Accelerator()
						self.assertEqual(str(accelerator.state.device   )				,   """cuda:64"""   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				Accelerator()
					A					,			A					,			A					,			A					,			A								=				create_components()
					accelerator.prepare(__A				,   __A				,   __A				,   __A				,   __A   )

					A								=				get_signature(__A   )

					with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(__A   )

						# make sure random weights don't match
						load_random_weights(__A   )
						self.assertTrue(abs(model_signature - get_signature(__A   )   ) > 1e-3   )

						# make sure loaded weights match
						accelerator.load_state(__A   )
						self.assertTrue(abs(model_signature - get_signature(__A   )   ) < 1e-3   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				Accelerator()
					A					,			A					,			A					,			A					,			A								=				create_components()
					accelerator.prepare(__A				,   __A				,   __A				,   __A				,   __A   )

					A								=				get_signature(__A   )

					# saving hook
					def save_config(a__				,   a__				,   a__   ):
						A								=				{"""class_name""": models[0].__class__.__name__}

						with open(os.path.join(__A				,   """data.json"""   )				,   """w"""   ) as f:
							json.dump(__A				,   __A   )

        # loading hook
					def load_config(a__				,   a__   ):
						with open(os.path.join(__A				,   """data.json"""   )				,   """r"""   ) as f:
							A								=				json.load(__A   )

						A								=				config["""class_name"""]

					A								=				accelerator.register_save_state_pre_hook(__A   )
					A								=				accelerator.register_load_state_pre_hook(__A   )

					with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(__A   )

						# make sure random weights don't match with hooks
						load_random_weights(__A   )
						self.assertTrue(abs(model_signature - get_signature(__A   )   ) > 1e-3   )

						# random class name to verify correct one is loaded
						A								=				"""random"""

						# make sure loaded weights match with hooks
						accelerator.load_state(__A   )
						self.assertTrue(abs(model_signature - get_signature(__A   )   ) < 1e-3   )

						# mode.class_name is loaded from config
						self.assertTrue(model.class_name == model.__class__.__name__   )

					# remove hooks
					save_hook.remove()
					load_hook.remove()

					with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(__A   )

						# make sure random weights don't match with hooks removed
						load_random_weights(__A   )
						self.assertTrue(abs(model_signature - get_signature(__A   )   ) > 1e-3   )

						# random class name to verify correct one is loaded
						A								=				"""random"""

						# make sure loaded weights match with hooks removed
						accelerator.load_state(__A   )
						self.assertTrue(abs(model_signature - get_signature(__A   )   ) < 1e-3   )

						# mode.class_name is NOT loaded from config
						self.assertTrue(model.class_name != model.__class__.__name__   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				Accelerator()
					A					,			A					,			A					,			A					,			A								=				create_components()
					A								=				None

					# This should work
					A					,			A					,			A					,			A					,			A					,			A								=				accelerator.prepare(
					    __A				,   __A				,   __A				,   __A				,   __A				,   __A   )
					self.assertTrue(dummy_obj is None   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				Accelerator()
					A					,			A					,			A					,			A					,			A								=				create_components()
					A								=				[1, 2, 3]

					# This should work
					A					,			A					,			A					,			A					,			A					,			A								=				accelerator.prepare(
					    __A				,   __A				,   __A				,   __A				,   __A				,   __A   )
					self.assertEqual(
					    getattr(__A				,   """_is_accelerate_prepared"""				,   __A   )				,   __A				,   """Dummy object should have `_is_accelerate_prepared` set to `True`"""				,   )
					self.assertEqual(
					    getattr(__A				,   """_is_accelerate_prepared"""				,   __A   )				,   __A				,   """Model is missing `_is_accelerator_prepared` or is set to `False`"""				,   )
					self.assertEqual(
					    getattr(__A				,   """_is_accelerate_prepared"""				,   __A   )				,   __A				,   """Optimizer is missing `_is_accelerator_prepared` or is set to `False`"""				,   )
					self.assertEqual(
					    getattr(__A				,   """_is_accelerate_prepared"""				,   __A   )				,   __A				,   """Scheduler is missing `_is_accelerator_prepared` or is set to `False`"""				,   )
					self.assertEqual(
					    getattr(__A				,   """_is_accelerate_prepared"""				,   __A   )				,   __A				,   """Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""				,   )
					self.assertEqual(
					    getattr(__A				,   """_is_accelerate_prepared"""				,   __A   )				,   __A				,   """Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"""				,   )

				@slow
				@require_bnb
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					from transformers import AutoModelForCausalLM

					A								=				AutoModelForCausalLM.from_pretrained(
					    """EleutherAI/gpt-neo-125m"""				,   load_in_abit=__A				,   device_map={"""""": 0}				,   )
					A								=				Accelerator()

					# This should work
					A								=				accelerator.prepare(__A   )

				@slow
				@require_bnb
				def      _UpperCAmelCase					(						self   )	->				Dict:
					from transformers import AutoModelForCausalLM

					A								=				Accelerator()

					with init_empty_weights():
						A								=				AutoModelForCausalLM.from_pretrained(
						    """EleutherAI/gpt-neo-125m"""				,   )
						model.tie_weights()
						A								=				infer_auto_device_map(__A   )
						A								=				"""cpu"""

					A								=				AutoModelForCausalLM.from_pretrained(
					    """EleutherAI/gpt-neo-125m"""				,   device_map=__A				,   load_in_abit=__A				,   llm_inta_enable_fpaa_cpu_offload=__A   )

					# This should not work and get value error
					with self.assertRaises(__A   ):
						A								=				accelerator.prepare(__A   )

				@slow
				@require_bnb
				@require_multi_gpu
				def      _UpperCAmelCase					(						self   )	->				int:
					from transformers import AutoModelForCausalLM

					A								=				{"""distributed_type""": DistributedType.MULTI_GPU}

					with init_empty_weights():
						A								=				AutoModelForCausalLM.from_pretrained(
						    """EleutherAI/gpt-neo-125m"""				,   )
						model.tie_weights()
						A								=				infer_auto_device_map(__A   )
						A								=				1

					A								=				AutoModelForCausalLM.from_pretrained(
					    """EleutherAI/gpt-neo-125m"""				,   load_in_abit=__A				,   device_map=__A				,   )
					A								=				Accelerator()

					# This should not work and get value error
					with self.assertRaises(__A   ):
						A								=				accelerator.prepare(__A   )

					PartialState._reset_state()

				@slow
				@require_bnb
				@require_multi_gpu
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					from transformers import AutoModelForCausalLM

					with init_empty_weights():
						A								=				AutoModelForCausalLM.from_pretrained(
						    """EleutherAI/gpt-neo-125m"""				,   )
						A								=				infer_auto_device_map(__A   )
						A								=				1

					A								=				AutoModelForCausalLM.from_pretrained(
					    """EleutherAI/gpt-neo-125m"""				,   load_in_abit=__A				,   device_map=__A				,   )
					A								=				Accelerator()

					# This should work
					A								=				accelerator.prepare(__A   )

				@require_cuda
				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				torch.nn.Linear(10				,   10   )
					A								=				torch.optim.SGD(model.parameters()				,   lr=0.01   )
					A								=				Accelerator(cpu=__A   )
					A								=				accelerator.prepare(__A   )







import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Tuple:





	"""simple docstring"""







	def wrapper(*UpperCamelCase__:     Union[str, Any]							,					**UpperCamelCase__:     List[str]						):
		A								=				timeit.default_timer()
		A								=				func(*UpperCamelCase__							,					**UpperCamelCase__						)
		A								=				timeit.default_timer() - starttime
		return delta

	A								=				func.__name__

	return wrapper





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     int=None						) ->   Any:





	"""simple docstring"""







	A								=				[]
	A								=				seq_shapes or {}
	for i in range(UpperCamelCase__						):
		A								=				{}
		for col_id, (k, v) in enumerate(features.items()						):
			if isinstance(UpperCamelCase__							,					_ArrayXD						):
				A								=				np.random.rand(*v.shape						).astype(v.dtype						)
			elif isinstance(UpperCamelCase__							,					datasets.Value						):
				if v.dtype == "string":
					A								=				"""The small grey turtle was surprisingly fast when challenged."""
				else:
					A								=				np.random.randint(10							,					size=1						).astype(v.dtype						).item()
			elif isinstance(UpperCamelCase__							,					datasets.Sequence						):
				while isinstance(UpperCamelCase__							,					datasets.Sequence						):
					A								=				v.feature
				A								=				seq_shapes[k]
				A								=				np.random.rand(*UpperCamelCase__						).astype(v.dtype						)
			A								=				data

		dummy_data.append((i, example)						)

	return dummy_data





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     str=None						) ->   Optional[int]:





	"""simple docstring"""







	A								=				generate_examples(UpperCamelCase__							,					num_examples=UpperCamelCase__							,					seq_shapes=UpperCamelCase__						)

	with ArrowWriter(features=UpperCamelCase__							,					path=UpperCamelCase__						) as writer:
		for key, record in dummy_data:
			A								=				features.encode_example(UpperCamelCase__						)
			writer.write(UpperCamelCase__						)

		A					,			A								=				writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'						)

	A								=				datasets.Dataset.from_file(filename=UpperCamelCase__							,					info=datasets.DatasetInfo(features=UpperCamelCase__						)						)

	return dataset



