


"""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		(		)			->		Dict:


    """simple docstring"""





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

    return model, optimizer, scheduler, train_dl, valid_dl







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]				)			->		Optional[int]:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]				)			->		int:


    """simple docstring"""





    snake_case					:   List[Any]         =					torch.nn.Linear(*tuple(model.weight.T.shape				)				).state_dict()
    model.load_state_dict(lowercase				)







class _lowerCAmelCase       (   snake_case_			):







   @require_cuda
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					Accelerator()
       assert PartialState._shared_state["_cpu"] is False
       assert PartialState._shared_state["device"].type == "cuda"
       with self.assertRaises(UpperCamelCase__		):
           snake_case					:   List[str]         =					Accelerator(cpu=UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Tuple         =					Accelerator()
       snake_case					:   Optional[Any]         =					GradientState()
       assert state.num_steps == 1
       snake_case					:   Any         =					4
       assert state.num_steps == 4

       assert state.sync_gradients is True
       snake_case					:   int         =					False
       assert state.sync_gradients is False
       GradientState._reset_state()







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   int         =					Accelerator()
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					create_components()

       (
           (
           snake_case
       )      ,(
           snake_case
       )      ,(
           snake_case
       )      ,(
           snake_case
       )      ,(
           snake_case
       )      ,
       )					:   List[str]         =					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 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   str         =					Accelerator()
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Optional[int]         =					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 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       PartialState._reset_state()

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

       with patch("torch.cuda.set_device"		,			UpperCamelCase__		), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"		):
           snake_case					:   List[Any]         =					Accelerator()
           self.assertEqual(str(accelerator.state.device		)		,			"cuda:64"		)







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					Accelerator()
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Optional[int]         =					create_components()
       accelerator.prepare(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)

       snake_case					:   Dict         =					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 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   List[str]         =					Accelerator()
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[str]         =					create_components()
       accelerator.prepare(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)

       snake_case					:   Tuple         =					get_signature(UpperCamelCase__		)

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

           snake_case					:   Dict         =					config["class_name"]

       snake_case					:   Tuple         =					accelerator.register_save_state_pre_hook(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					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
           snake_case					:   int         =					"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
           snake_case					:   Optional[Any]         =					"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 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					Accelerator()
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Tuple         =					create_components()
       snake_case					:   Any         =					None

       # This should work
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Optional[Any]         =					accelerator.prepare(
           UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)
       self.assertTrue(dummy_obj is None		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					Accelerator()
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Dict         =					create_components()
       snake_case					:   Union[str, Any]         =					[1, 2, 3]

       # This should work
       snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Any         =					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 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       from transformers import AutoModelForCausalLM

       snake_case					:   Tuple         =					AutoModelForCausalLM.from_pretrained(
           "EleutherAI/gpt-neo-125m"		,			load_in_abit=UpperCamelCase__		,			device_map={"": 0}		,			)
       snake_case					:   Union[str, Any]         =					Accelerator()

       # This should work
       snake_case					:   List[str]         =					accelerator.prepare(UpperCamelCase__		)







   @slow
   @require_bnb
   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       from transformers import AutoModelForCausalLM

       snake_case					:   int         =					Accelerator()

       with init_empty_weights():
           snake_case					:   int         =					AutoModelForCausalLM.from_pretrained(
               "EleutherAI/gpt-neo-125m"		,			)
           model.tie_weights()
           snake_case					:   Tuple         =					infer_auto_device_map(UpperCamelCase__		)
           snake_case					:   Any         =					"cpu"

       snake_case					:   Optional[int]         =					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__		):
           snake_case					:   str         =					accelerator.prepare(UpperCamelCase__		)







   @slow
   @require_bnb
   @require_multi_gpu
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       from transformers import AutoModelForCausalLM

       snake_case					:   Tuple         =					{"distributed_type": DistributedType.MULTI_GPU}

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

       snake_case					:   Any         =					AutoModelForCausalLM.from_pretrained(
           "EleutherAI/gpt-neo-125m"		,			load_in_abit=UpperCamelCase__		,			device_map=UpperCamelCase__		,			)
       snake_case					:   Any         =					Accelerator()

       # This should not work and get value error
       with self.assertRaises(UpperCamelCase__		):
           snake_case					:   Union[str, Any]         =					accelerator.prepare(UpperCamelCase__		)

       PartialState._reset_state()







   @slow
   @require_bnb
   @require_multi_gpu
   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       from transformers import AutoModelForCausalLM

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

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

       # This should work
       snake_case					:   str         =					accelerator.prepare(UpperCamelCase__		)







   @require_cuda
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					torch.nn.Linear(10		,			10		)
       snake_case					:   Tuple         =					torch.optim.SGD(model.parameters()		,			lr=0.01		)
       snake_case					:   int         =					Accelerator(cpu=UpperCamelCase__		)
       snake_case					:   str         =					accelerator.prepare(UpperCamelCase__		)









"""simple docstring"""



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__snake_case         =					subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__snake_case         =					(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

__snake_case         =					"""|""".join(sys.argv[1:])
__snake_case         =					re.compile(RF'''^({joined_dirs}).*?\.py$''')

__snake_case         =					[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





