


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    (      )					->							Dict:
     '''simple docstring'''





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

     return model, optimizer, scheduler, train_dl, valid_dl




def     UpperCamelCase    (      _a		)					->							Optional[Any]:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a		)					->							Dict:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       torch.nn.Linear(*tuple(model.weight.T.shape		)		).state_dict()
     model.load_state_dict(_a		)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_cuda
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       Accelerator()
        assert PartialState._shared_state["_cpu"] is False
        assert PartialState._shared_state["device"].type == "cuda"
        with self.assertRaises(UpperCamelCase_		):
             lowercase_       :Union[str, Any]     =       Accelerator(cpu=UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       Accelerator()
        lowercase_       :Any     =       GradientState()
        assert state.num_steps == 1
        lowercase_       :Optional[int]     =       4
        assert state.num_steps == 4

        assert state.sync_gradients is True
        lowercase_       :int     =       False
        assert state.sync_gradients is False
        GradientState._reset_state()
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       Accelerator()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Any     =       create_components()

        (
            (
            lowercase_
        )    ,      (
            lowercase_
        )    ,      (
            lowercase_
        )    ,      (
            lowercase_
        )    ,      (
            lowercase_
        )    ,      
        )       :Union[str, Any]     =       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		):
        lowercase_       :Union[str, Any]     =       Accelerator()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :str     =       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		):
        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'''		):
             lowercase_       :Optional[int]     =       Accelerator()
             self.assertEqual(str(accelerator.state.device		)   ,							'''cuda:64'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       Accelerator()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Dict     =       create_components()
        accelerator.prepare(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

        lowercase_       :Any     =       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		):
        lowercase_       :Any     =       Accelerator()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Dict     =       create_components()
        accelerator.prepare(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

        lowercase_       :List[Any]     =       get_signature(UpperCamelCase_		)

        # saving hook
        def save_config(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :int     =       {'''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:
                  lowercase_       :Union[str, Any]     =       json.load(UpperCamelCase_		)

             lowercase_       :Tuple     =       config['''class_name''']

        lowercase_       :Union[str, Any]     =       accelerator.register_save_state_pre_hook(UpperCamelCase_		)
        lowercase_       :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
             lowercase_       :Any     =       '''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
             lowercase_       :List[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 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       Accelerator()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Any     =       create_components()
        lowercase_       :Dict     =       None

        # This should work
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Union[str, Any]     =       accelerator.prepare(
            UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
        self.assertTrue(dummy_obj is None		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       Accelerator()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Tuple     =       create_components()
        lowercase_       :Tuple     =       [1, 2, 3]

        # This should work
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :List[str]     =       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		):
        from transformers import AutoModelForCausalLM

        lowercase_       :Optional[int]     =       AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''   ,							load_in_abit=UpperCamelCase_   ,							device_map={'''''': 0}   ,							)
        lowercase_       :Any     =       Accelerator()

        # This should work
        lowercase_       :str     =       accelerator.prepare(UpperCamelCase_		)
   @slow
   @require_bnb
   def 		UpperCamelCase  (     self		):
        from transformers import AutoModelForCausalLM

        lowercase_       :str     =       Accelerator()

        with init_empty_weights():
             lowercase_       :List[Any]     =       AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''   ,							)
             model.tie_weights()
             lowercase_       :Dict     =       infer_auto_device_map(UpperCamelCase_		)
             lowercase_       :Tuple     =       '''cpu'''

        lowercase_       :Tuple     =       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_		):
             lowercase_       :List[str]     =       accelerator.prepare(UpperCamelCase_		)
   @slow
   @require_bnb
   @require_multi_gpu
   def 		UpperCamelCase  (     self		):
        from transformers import AutoModelForCausalLM

        lowercase_       :Optional[int]     =       {'''distributed_type''': DistributedType.MULTI_GPU}

        with init_empty_weights():
             lowercase_       :List[Any]     =       AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''   ,							)
             model.tie_weights()
             lowercase_       :Dict     =       infer_auto_device_map(UpperCamelCase_		)
             lowercase_       :Dict     =       1

        lowercase_       :List[Any]     =       AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''   ,							load_in_abit=UpperCamelCase_   ,							device_map=UpperCamelCase_   ,							)
        lowercase_       :str     =       Accelerator()

        # This should not work and get value error
        with self.assertRaises(UpperCamelCase_		):
             lowercase_       :Optional[int]     =       accelerator.prepare(UpperCamelCase_		)

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

        with init_empty_weights():
             lowercase_       :str     =       AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''   ,							)
             lowercase_       :List[Any]     =       infer_auto_device_map(UpperCamelCase_		)
             lowercase_       :int     =       1

        lowercase_       :Optional[Any]     =       AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''   ,							load_in_abit=UpperCamelCase_   ,							device_map=UpperCamelCase_   ,							)
        lowercase_       :Dict     =       Accelerator()

        # This should work
        lowercase_       :List[Any]     =       accelerator.prepare(UpperCamelCase_		)




   @require_cuda
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       torch.nn.Linear(10   ,							10		)
        lowercase_       :Optional[int]     =       torch.optim.SGD(model.parameters()   ,							lr=0.01		)
        lowercase_       :str     =       Accelerator(cpu=UpperCamelCase_		)
        lowercase_       :Dict     =       accelerator.prepare(UpperCamelCase_		)





import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return np.dot(_a       ,		_a		)






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							*,
       UpperCamelCase_ = np.inf   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = 0.0   ,							):
        lowercase_       :Dict     =       regularization
        lowercase_       :Optional[Any]     =       gamma
        if kernel == "linear":
             lowercase_       :Optional[Any]     =       self.__linear
        elif kernel == "rbf":
             if self.gamma == 0:
                  raise ValueError('''rbf kernel requires gamma'''		)
             if not isinstance(self.gamma   ,							(float, int)		):
                  raise ValueError('''gamma must be float or int'''		)
             if not self.gamma > 0:
                  raise ValueError('''gamma must be > 0'''		)
             lowercase_       :Union[str, Any]     =       self.__rbf
             # in the future, there could be a default value like in sklearn
             # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
             # previously it was 1/(n_features)
        else:
             lowercase_       :str     =       f"Unknown kernel: {kernel}"
             raise ValueError(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.dot(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       observations
        lowercase_       :Union[str, Any]     =       classes

        # using Wolfe's Dual to calculate w.
        # Primal problem: minimize 1/2*norm_squared(w)
        #   constraint: yn(w . xn + b) >= 1
        #
        # With l a vector
        # Dual problem: maximize sum_n(ln) -
        #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
        #   constraint: self.C >= ln >= 0
        #           and sum_n(ln*yn) = 0
        # Then we get w using w = sum_n(ln*yn*xn)
        # At the end we can get b ~= mean(yn - w . xn)
        #
        # Since we use kernels, we only need l_star to calculate b
        # and to classify observations

        ((lowercase_)    ,      )       :Optional[Any]     =       np.shape(UpperCamelCase_		)

        def to_minimize(UpperCamelCase_		) -> float:
             lowercase_       :Dict     =       0
             ((lowercase_)    ,      )       :Tuple     =       np.shape(UpperCamelCase_		)
             for i in range(UpperCamelCase_		):
                  for j in range(UpperCamelCase_		):
                       s += (
                           candidate[i]
                           * candidate[j]
                           * classes[i]
                           * classes[j]
                           * self.kernel(observations[i]   ,							observations[j]		)
                       )
             return 1 / 2 * s - sum(UpperCamelCase_		)

        lowercase_       :Dict     =       LinearConstraint(UpperCamelCase_   ,							0   ,							0		)
        lowercase_       :Optional[Any]     =       Bounds(0   ,							self.regularization		)

        lowercase_       :Any     =       minimize(
            UpperCamelCase_   ,							np.ones(UpperCamelCase_		)   ,							bounds=UpperCamelCase_   ,							constraints=[ly_contraint]		).x
        lowercase_       :Tuple     =       l_star

        # calculating mean offset of separation plane to points
        lowercase_       :str     =       0
        for i in range(UpperCamelCase_		):
             for j in range(UpperCamelCase_		):
                  s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                      observations[i]   ,							observations[j]		)
        lowercase_       :List[str]     =       s / n




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Union[str, Any]     =       sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]   ,							UpperCamelCase_		)
            for n in range(len(self.classes		)		)		)
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

