

'''simple docstring'''





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__ (					):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.nn.Linear(2							,			4)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.optim.AdamW(model.parameters()							,			lr=1.0)
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.optim.lr_scheduler.OneCycleLR(_a							,			max_lr=0.01							,			steps_per_epoch=2							,			epochs=1)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DataLoader(TensorDataset(torch.tensor([1, 2, 3])))
	SCREAMING_SNAKE_CASE   :      Dict				     =       DataLoader(TensorDataset(torch.tensor([4, 5, 6])))

	return model, optimizer, scheduler, train_dl, valid_dl

def  lowerCamelCase__ (					_a):
	return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
	model.load_state_dict(_a)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			@require_cuda
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       Accelerator()
				assert PartialState._shared_state["_cpu"] is False
				assert PartialState._shared_state["device"].type == "cuda"
				with self.assertRaises(a	):
					SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator(cpu=a	)
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      int				     =       GradientState()
				assert state.num_steps == 1
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       4
				assert state.num_steps == 4

				assert state.sync_gradients is True
				SCREAMING_SNAKE_CASE   :      Any				     =       False
				assert state.sync_gradients is False
				GradientState._reset_state()
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      str				     =       create_components()

				(
				    SCREAMING_SNAKE_CASE
				)   :      Union[str, Any]				     =       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							: Tuple	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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							: Tuple	)							->       Dict:

				"""simple docstring"""

				PartialState._reset_state()

				# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
				def noop(*a							: Tuple      ,      **a							: Optional[Any]	):
					pass

				with patch("torch.cuda.set_device"      ,      a	), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Accelerator()
					self.assertEqual(str(accelerator.state.device	)      ,      "cuda:64"	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       create_components()
				accelerator.prepare(a      ,      a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       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							: int	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      int				     =       create_components()
				accelerator.prepare(a      ,      a      ,      a      ,      a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       get_signature(a	)

				# saving hook
				def save_config(a							: Union[str, Any]      ,      a							: str      ,      a							: Optional[int]	):
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {"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							: Tuple      ,      a							: Optional[int]	):
					with open(os.path.join(a      ,      "data.json"	)      ,      "r"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       json.load(a	)

					SCREAMING_SNAKE_CASE   :      Dict				     =       config["class_name"]

				SCREAMING_SNAKE_CASE   :      List[str]				     =       accelerator.register_save_state_pre_hook(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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
					SCREAMING_SNAKE_CASE   :      Any				     =       "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
					SCREAMING_SNAKE_CASE   :      str				     =       "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							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       create_components()
				SCREAMING_SNAKE_CASE   :      Any				     =       None

				# This should work
				SCREAMING_SNAKE_CASE   :      Tuple				     =       accelerator.prepare(
				    a      ,      a      ,      a      ,      a      ,      a      ,      a	)
				self.assertTrue(dummy_obj is None	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       Accelerator()
				SCREAMING_SNAKE_CASE   :      str				     =       create_components()
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [1, 2, 3]

				# This should work
				SCREAMING_SNAKE_CASE   :      Tuple				     =       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[Any]	)							->       Any:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoModelForCausalLM.from_pretrained(
				    "EleutherAI/gpt-neo-125m"      ,      load_in_abit=a      ,      device_map={"": 0}      ,      )
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Accelerator()

				# This should work
				SCREAMING_SNAKE_CASE   :      Dict				     =       accelerator.prepare(a	)
			@slow
			@require_bnb
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       Accelerator()

				with init_empty_weights():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"      ,      )
					model.tie_weights()
					SCREAMING_SNAKE_CASE   :      Tuple				     =       infer_auto_device_map(a	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       "cpu"

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       accelerator.prepare(a	)
			@slow
			@require_bnb
			@require_multi_gpu
			def 							__UpperCamelCase (			self							: List[str]	)							->       List[str]:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {"distributed_type": DistributedType.MULTI_GPU}

				with init_empty_weights():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"      ,      )
					model.tie_weights()
					SCREAMING_SNAKE_CASE   :      List[str]				     =       infer_auto_device_map(a	)
					SCREAMING_SNAKE_CASE   :      str				     =       1

				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoModelForCausalLM.from_pretrained(
				    "EleutherAI/gpt-neo-125m"      ,      load_in_abit=a      ,      device_map=a      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       Accelerator()

				# This should not work and get value error
				with self.assertRaises(a	):
					SCREAMING_SNAKE_CASE   :      str				     =       accelerator.prepare(a	)

				PartialState._reset_state()
			@slow
			@require_bnb
			@require_multi_gpu
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[Any]:

				"""simple docstring"""

				from transformers import AutoModelForCausalLM

				with init_empty_weights():
					SCREAMING_SNAKE_CASE   :      Any				     =       AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"      ,      )
					SCREAMING_SNAKE_CASE   :      List[str]				     =       infer_auto_device_map(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       1

				SCREAMING_SNAKE_CASE   :      Dict				     =       AutoModelForCausalLM.from_pretrained(
				    "EleutherAI/gpt-neo-125m"      ,      load_in_abit=a      ,      device_map=a      ,      )
				SCREAMING_SNAKE_CASE   :      Dict				     =       Accelerator()

				# This should work
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       accelerator.prepare(a	)






			@require_cuda
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.nn.Linear(10      ,      10	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.optim.SGD(model.parameters()      ,      lr=0.01	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       Accelerator(cpu=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       accelerator.prepare(a	)


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(__A   )							,	'Tatoeba directory does not exist.'   )
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tempfile.mkdtemp()
				return TatoebaConverter(save_dir=a	)
			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				self.resolver.convert_models(["heb-eng"]	)






			@slow
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.resolver.write_model_card("opus-mt-he-en"      ,      dry_run=a	)
				assert mmeta["long_pair"] == "heb-eng"