





'''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      __A						(							):
  lowerCAmelCase     :    Any						=   torch.nn.Linear(2      ,4	)
  lowerCAmelCase     :    Optional[Any]						=   torch.optim.AdamW(model.parameters()      ,lr=1.0	)
  lowerCAmelCase     :    Optional[int]						=   torch.optim.lr_scheduler.OneCycleLR(_lowerCAmelCase      ,max_lr=0.0_1      ,steps_per_epoch=2      ,epochs=1	)
  lowerCAmelCase     :    str						=   DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
  lowerCAmelCase     :    Union[str, Any]						=   DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

  return model, optimizer, scheduler, train_dl, valid_dl






def      __A						(							a_						:  Union[str, Any]	):
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def      __A						(							a_						:  List[Any]	):
  lowerCAmelCase     :    List[Any]						=   torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
  model.load_state_dict(_lowerCAmelCase	)

class 		lowerCamelCase  (      __snake_case       ):



 @require_cuda
 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    str						=   Accelerator()
   assert PartialState._shared_state["_cpu"] is False
   assert PartialState._shared_state["device"].type == "cuda"
   with self.assertRaises(A_	):
     lowerCAmelCase     :    Union[str, Any]						=   Accelerator(cpu=A_	)



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Optional[int]						=   Accelerator()
   lowerCAmelCase     :    Dict						=   GradientState()
   assert state.num_steps == 1
   lowerCAmelCase     :    Tuple						=   4
   assert state.num_steps == 4

   assert state.sync_gradients is True
   lowerCAmelCase     :    Dict						=   False
   assert state.sync_gradients is False
   GradientState._reset_state()



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    str						=   Accelerator()
   lowerCAmelCase     :    Tuple						=   create_components()

   (
       lowerCAmelCase					
   )     :    Dict						=   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   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    str						=   Accelerator()
   lowerCAmelCase     :    Optional[Any]						=   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   _lowerCamelCase    (   self	):
   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"	):
     lowerCAmelCase     :    Dict						=   Accelerator()
     self.assertEqual(str(accelerator.state.device	)		,      "cuda:64"	)



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    List[Any]						=   Accelerator()
   lowerCAmelCase     :    int						=   create_components()
   accelerator.prepare(A_		,      A_		,      A_		,      A_		,      A_	)

   lowerCAmelCase     :    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   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    Tuple						=   Accelerator()
   lowerCAmelCase     :    Optional[int]						=   create_components()
   accelerator.prepare(A_		,      A_		,      A_		,      A_		,      A_	)

   lowerCAmelCase     :    Any						=   get_signature(A_	)

   # saving hook
   def save_config(a_		,      a_		,      a_	):
     lowerCAmelCase     :    Dict						=   {"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:
       lowerCAmelCase     :    int						=   json.load(A_	)

     lowerCAmelCase     :    List[str]						=   config["class_name"]

   lowerCAmelCase     :    List[Any]						=   accelerator.register_save_state_pre_hook(A_	)
   lowerCAmelCase     :    List[Any]						=   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
     lowerCAmelCase     :    Optional[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
     lowerCAmelCase     :    int						=   "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   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    str						=   Accelerator()
   lowerCAmelCase     :    Optional[Any]						=   create_components()
   lowerCAmelCase     :    Dict						=   None

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



 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    List[str]						=   Accelerator()
   lowerCAmelCase     :    Any						=   create_components()
   lowerCAmelCase     :    int						=   [1, 2, 3]

   # This should work
   lowerCAmelCase     :    List[Any]						=   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   _lowerCamelCase    (   self	):
   from transformers import AutoModelForCausalLM

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

   # This should work
   lowerCAmelCase     :    str						=   accelerator.prepare(A_	)



 @slow
 @require_bnb
 def   _lowerCamelCase    (   self	):
   from transformers import AutoModelForCausalLM

   lowerCAmelCase     :    Optional[Any]						=   Accelerator()

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

   lowerCAmelCase     :    Tuple						=   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_	):
     lowerCAmelCase     :    Any						=   accelerator.prepare(A_	)



 @slow
 @require_bnb
 @require_multi_gpu
 def   _lowerCamelCase    (   self	):
   from transformers import AutoModelForCausalLM

   lowerCAmelCase     :    str						=   {"distributed_type": DistributedType.MULTI_GPU}

   with init_empty_weights():
     lowerCAmelCase     :    Union[str, Any]						=   AutoModelForCausalLM.from_pretrained(
         "EleutherAI/gpt-neo-125m"		,      )
     model.tie_weights()
     lowerCAmelCase     :    Union[str, Any]						=   infer_auto_device_map(A_	)
     lowerCAmelCase     :    int						=   1

   lowerCAmelCase     :    Any						=   AutoModelForCausalLM.from_pretrained(
       "EleutherAI/gpt-neo-125m"		,      load_in_abit=A_		,      device_map=A_		,      )
   lowerCAmelCase     :    List[Any]						=   Accelerator()

   # This should not work and get value error
   with self.assertRaises(A_	):
     lowerCAmelCase     :    int						=   accelerator.prepare(A_	)

   PartialState._reset_state()



 @slow
 @require_bnb
 @require_multi_gpu
 def   _lowerCamelCase    (   self	):
   from transformers import AutoModelForCausalLM

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

   lowerCAmelCase     :    Optional[Any]						=   AutoModelForCausalLM.from_pretrained(
       "EleutherAI/gpt-neo-125m"		,      load_in_abit=A_		,      device_map=A_		,      )
   lowerCAmelCase     :    List[str]						=   Accelerator()

   # This should work
   lowerCAmelCase     :    Optional[Any]						=   accelerator.prepare(A_	)





 @require_cuda
 def   _lowerCamelCase    (   self	):
   lowerCAmelCase     :    int						=   torch.nn.Linear(10		,      10	)
   lowerCAmelCase     :    Tuple						=   torch.optim.SGD(model.parameters()		,      lr=0.01	)
   lowerCAmelCase     :    Tuple						=   Accelerator(cpu=A_	)
   lowerCAmelCase     :    str						=   accelerator.prepare(A_	)










'''simple docstring'''






import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


lowerCAmelCase       		=      """\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


lowerCAmelCase       		=      """\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


lowerCAmelCase       		=      R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION      ,   _KWARGS_DESCRIPTION       )
class 		lowerCamelCase  (      datasets.Metric       ):



	def   _lowerCamelCase    (   self	):
			return datasets.MetricInfo(
			    description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(
			        {
			            "predictions": datasets.Value("string"	),
			            "references": datasets.Value("string"	),
			        }	)		,      homepage="https://github.com/hendrycks/math"		,      codebase_urls=["https://github.com/hendrycks/math"]		,      )





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   0.0
			for i, j in zip(a_		,      a_	):
					n_correct += 1.0 if math_equivalence.is_equiv(a_		,      a_	) else 0.0
			lowerCAmelCase     :    List[str]						=   n_correct / len(a_	)
			return {
			    "accuracy": accuracy,
			}



