


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"""






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

     return model, optimizer, scheduler, train_dl, valid_dl
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    torch.nn.Linear(*tuple(model.weight.T.shape    )    ).state_dict()
     model.load_state_dict(UpperCamelCase_    )

class   A__ (   snake_case__     ):







    """simple docstring"""

    @require_cuda
    def 		a_      (	self   ):
         snake_case					=    Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(__snake_case   ):
              snake_case					=    Accelerator(cpu=__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    Accelerator()
         snake_case					=    GradientState()
         assert state.num_steps == 1
         snake_case					=    4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         snake_case					=    False
         assert state.sync_gradients is False
         GradientState._reset_state()
    def 		a_      (	self   ):
         snake_case					=    Accelerator()
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    create_components()

         (
             (
             snake_case
         )			,  (
             snake_case
         )			,  (
             snake_case
         )			,  (
             snake_case
         )			,  (
             snake_case
         )			,  
         )					=    accelerator.prepare(__snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   )

         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 		a_      (	self   ):
         snake_case					=    Accelerator()
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    create_components()
         accelerator.prepare(__snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   )
         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 		a_      (	self   ):
         PartialState._reset_state()

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

         with patch('''torch.cuda.set_device'''      , __snake_case   ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''   ):
              snake_case					=    Accelerator()
              self.assertEqual(str(accelerator.state.device   )      , '''cuda:64'''   )
    def 		a_      (	self   ):
         snake_case					=    Accelerator()
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    create_components()
         accelerator.prepare(__snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   )

         snake_case					=    get_signature(__snake_case   )

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

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

              # make sure loaded weights match
              accelerator.load_state(__snake_case   )
              self.assertTrue(abs(model_signature - get_signature(__snake_case   )   ) < 1E-3   )
    def 		a_      (	self   ):
         snake_case					=    Accelerator()
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    create_components()
         accelerator.prepare(__snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   )

         snake_case					=    get_signature(__snake_case   )

         # saving hook
         def save_config(__snake_case      , __snake_case      , __snake_case   ):
              snake_case					=    {'''class_name''': models[0].__class__.__name__}

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

        # loading hook
         def load_config(__snake_case      , __snake_case   ):
              with open(os.path.join(__snake_case      , '''data.json'''   )      , '''r'''   ) as f:
                   snake_case					=    json.load(__snake_case   )

              snake_case					=    config['''class_name''']

         snake_case					=    accelerator.register_save_state_pre_hook(__snake_case   )
         snake_case					=    accelerator.register_load_state_pre_hook(__snake_case   )

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

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

              # random class name to verify correct one is loaded
              snake_case					=    '''random'''

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

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

              # random class name to verify correct one is loaded
              snake_case					=    '''random'''

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

              # mode.class_name is NOT loaded from config
              self.assertTrue(model.class_name != model.__class__.__name__   )
    def 		a_      (	self   ):
         snake_case					=    Accelerator()
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    create_components()
         snake_case					=    None

         # This should work
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    accelerator.prepare(
             __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   )
         self.assertTrue(dummy_obj is None   )
    def 		a_      (	self   ):
         snake_case					=    Accelerator()
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    create_components()
         snake_case					=    [1, 2, 3]

         # This should work
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    accelerator.prepare(
             __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   )
         self.assertEqual(
             getattr(__snake_case      , '''_is_accelerate_prepared'''      , __snake_case   )      , __snake_case      , '''Dummy object should have `_is_accelerate_prepared` set to `True`'''      , )
         self.assertEqual(
             getattr(__snake_case      , '''_is_accelerate_prepared'''      , __snake_case   )      , __snake_case      , '''Model is missing `_is_accelerator_prepared` or is set to `False`'''      , )
         self.assertEqual(
             getattr(__snake_case      , '''_is_accelerate_prepared'''      , __snake_case   )      , __snake_case      , '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`'''      , )
         self.assertEqual(
             getattr(__snake_case      , '''_is_accelerate_prepared'''      , __snake_case   )      , __snake_case      , '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`'''      , )
         self.assertEqual(
             getattr(__snake_case      , '''_is_accelerate_prepared'''      , __snake_case   )      , __snake_case      , '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''      , )
         self.assertEqual(
             getattr(__snake_case      , '''_is_accelerate_prepared'''      , __snake_case   )      , __snake_case      , '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''      , )
    @slow
    @require_bnb
    def 		a_      (	self   ):
         from transformers import AutoModelForCausalLM

         snake_case					=    AutoModelForCausalLM.from_pretrained(
             '''EleutherAI/gpt-neo-125m'''      , load_in_abit=__snake_case      , device_map={'''''': 0}      , )
         snake_case					=    Accelerator()

         # This should work
         snake_case					=    accelerator.prepare(__snake_case   )
    @slow
    @require_bnb
    def 		a_      (	self   ):
         from transformers import AutoModelForCausalLM

         snake_case					=    Accelerator()

         with init_empty_weights():
              snake_case					=    AutoModelForCausalLM.from_pretrained(
                  '''EleutherAI/gpt-neo-125m'''      , )
              model.tie_weights()
              snake_case					=    infer_auto_device_map(__snake_case   )
              snake_case					=    '''cpu'''

         snake_case					=    AutoModelForCausalLM.from_pretrained(
             '''EleutherAI/gpt-neo-125m'''      , device_map=__snake_case      , load_in_abit=__snake_case      , llm_inta_enable_fpaa_cpu_offload=__snake_case   )

         # This should not work and get value error
         with self.assertRaises(__snake_case   ):
              snake_case					=    accelerator.prepare(__snake_case   )
    @slow
    @require_bnb
    @require_multi_gpu
    def 		a_      (	self   ):
         from transformers import AutoModelForCausalLM

         snake_case					=    {'''distributed_type''': DistributedType.MULTI_GPU}

         with init_empty_weights():
              snake_case					=    AutoModelForCausalLM.from_pretrained(
                  '''EleutherAI/gpt-neo-125m'''      , )
              model.tie_weights()
              snake_case					=    infer_auto_device_map(__snake_case   )
              snake_case					=    1

         snake_case					=    AutoModelForCausalLM.from_pretrained(
             '''EleutherAI/gpt-neo-125m'''      , load_in_abit=__snake_case      , device_map=__snake_case      , )
         snake_case					=    Accelerator()

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

         PartialState._reset_state()
    @slow
    @require_bnb
    @require_multi_gpu
    def 		a_      (	self   ):
         from transformers import AutoModelForCausalLM

         with init_empty_weights():
              snake_case					=    AutoModelForCausalLM.from_pretrained(
                  '''EleutherAI/gpt-neo-125m'''      , )
              snake_case					=    infer_auto_device_map(__snake_case   )
              snake_case					=    1

         snake_case					=    AutoModelForCausalLM.from_pretrained(
             '''EleutherAI/gpt-neo-125m'''      , load_in_abit=__snake_case      , device_map=__snake_case      , )
         snake_case					=    Accelerator()

         # This should work
         snake_case					=    accelerator.prepare(__snake_case   )




    @require_cuda
    def 		a_      (	self   ):
         snake_case					=    torch.nn.Linear(1_0      , 1_0   )
         snake_case					=    torch.optim.SGD(model.parameters()      , lr=0.01   )
         snake_case					=    Accelerator(cpu=__snake_case   )
         snake_case					=    accelerator.prepare(__snake_case   )








def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )
     snake_case					=    [[0] * n for i in range(UpperCamelCase_    )]
     for i in range(UpperCamelCase_    ):
          snake_case					=    y_points[i]

     for i in range(2							,UpperCamelCase_    ):
          for j in range(UpperCamelCase_							,UpperCamelCase_    ):
               snake_case					=    (
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




