


"""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        UpperCAmelCase__	():




					'''simple docstring'''


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

					return model, optimizer, scheduler, train_dl, valid_dl




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    torch.nn.Linear(*tuple(model.weight.T.shape       )       ).state_dict()
					model.load_state_dict(lowerCAmelCase_       )




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	@require_cuda
	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    Accelerator()
						assert PartialState._shared_state["_cpu"] is False
						assert PartialState._shared_state["device"].type == "cuda"
						with self.assertRaises(UpperCAmelCase__       ):
											__SCREAMING_SNAKE_CASE						       =    Accelerator(cpu=UpperCAmelCase__       )




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

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




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    Accelerator()
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    create_components()

						(
						    (
						    __SCREAMING_SNAKE_CASE
						)       ,  (
						    __SCREAMING_SNAKE_CASE
						)       ,  (
						    __SCREAMING_SNAKE_CASE
						)       ,  (
						    __SCREAMING_SNAKE_CASE
						)       ,  (
						    __SCREAMING_SNAKE_CASE
						)       ,  
						)						       =    accelerator.prepare(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )

						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       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    Accelerator()
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    create_components()
						accelerator.prepare(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )
						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      :    Any       )	->     List[Any]:
						PartialState._reset_state()

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

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




	def   UpperCAmelCase_				(			self      :    Any       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    Accelerator()
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    create_components()
						accelerator.prepare(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    get_signature(UpperCAmelCase__       )

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

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

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




	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    Accelerator()
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    create_components()
						accelerator.prepare(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    get_signature(UpperCAmelCase__       )

						# saving hook
						def save_config(UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Optional[Any]       ):
											__SCREAMING_SNAKE_CASE						       =    {"class_name": models[0].__class__.__name__}

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

        # loading hook
						def load_config(UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    Tuple       ):
											with open(os.path.join(UpperCAmelCase__      ,      "data.json"       )      ,      "r"       ) as f:
																__SCREAMING_SNAKE_CASE						       =    json.load(UpperCAmelCase__       )

											__SCREAMING_SNAKE_CASE						       =    config["class_name"]

						__SCREAMING_SNAKE_CASE						       =    accelerator.register_save_state_pre_hook(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    accelerator.register_load_state_pre_hook(UpperCAmelCase__       )

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

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

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

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

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

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

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

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




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    Accelerator()
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    create_components()
						__SCREAMING_SNAKE_CASE						       =    None

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




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    Accelerator()
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    create_components()
						__SCREAMING_SNAKE_CASE						       =    [1, 2, 3]

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




	@slow
	@require_bnb
	def   UpperCAmelCase_				(			self      :    Any       )	->     int:
						from transformers import AutoModelForCausalLM

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

						# This should work
						__SCREAMING_SNAKE_CASE						       =    accelerator.prepare(UpperCAmelCase__       )




	@slow
	@require_bnb
	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     List[Any]:
						from transformers import AutoModelForCausalLM

						__SCREAMING_SNAKE_CASE						       =    Accelerator()

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

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

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




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

						__SCREAMING_SNAKE_CASE						       =    {"distributed_type": DistributedType.MULTI_GPU}

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

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

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

						PartialState._reset_state()




	@slow
	@require_bnb
	@require_multi_gpu
	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[int]:
						from transformers import AutoModelForCausalLM

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

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

						# This should work
						__SCREAMING_SNAKE_CASE						       =    accelerator.prepare(UpperCAmelCase__       )




	@require_cuda
	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    torch.nn.Linear(1_0      ,      1_0       )
						__SCREAMING_SNAKE_CASE						       =    torch.optim.SGD(model.parameters()      ,      lr=0.01       )
						__SCREAMING_SNAKE_CASE						       =    Accelerator(cpu=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    accelerator.prepare(UpperCAmelCase__       )




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
