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

  return model, optimizer, scheduler, train_dl, valid_dl






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   torch.nn.Linear(*tuple(model.weight.T.shape		)		).state_dict()
  model.load_state_dict(lowercase		)


class 							a					(		lowerCAmelCase_ ):


    @require_cuda
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   Accelerator()
      assert PartialState._shared_state["_cpu"] is False
      assert PartialState._shared_state["device"].type == "cuda"
      with self.assertRaises(__lowerCAmelCase       ):
        _UpperCAmelCase    			=   Accelerator(cpu=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   Accelerator()
      _UpperCAmelCase    			=   GradientState()
      assert state.num_steps == 1
      _UpperCAmelCase    			=   4
      assert state.num_steps == 4

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


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   Accelerator()
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   create_components()

      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   accelerator.prepare(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

      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						:	List[Any]       ):
      _UpperCAmelCase    			=   Accelerator()
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   create_components()
      accelerator.prepare(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )
      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						:	List[str]       ):
      PartialState._reset_state()

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

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


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   Accelerator()
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   create_components()
      accelerator.prepare(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   get_signature(__lowerCAmelCase       )

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

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

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


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   Accelerator()
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   create_components()
      accelerator.prepare(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   get_signature(__lowerCAmelCase       )

      # saving hook
      def save_config(__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict       ):
        _UpperCAmelCase    			=   {"""class_name""": models[0].__class__.__name__}

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

        # loading hook
      def load_config(__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Optional[Any]       ):
        with open(os.path.join(__lowerCAmelCase ,					"""data.json"""       ) ,					"""r"""       ) as f:
          _UpperCAmelCase    			=   json.load(__lowerCAmelCase       )

        _UpperCAmelCase    			=   config["""class_name"""]

      _UpperCAmelCase    			=   accelerator.register_save_state_pre_hook(__lowerCAmelCase       )
      _UpperCAmelCase    			=   accelerator.register_load_state_pre_hook(__lowerCAmelCase       )

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

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

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

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

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

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

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

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


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   Accelerator()
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   create_components()
      _UpperCAmelCase    			=   None

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


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   Accelerator()
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   create_components()
      _UpperCAmelCase    			=   [1, 2, 3]

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


    @slow
    @require_bnb
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      from transformers import AutoModelForCausalLM

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

      # This should work
      _UpperCAmelCase    			=   accelerator.prepare(__lowerCAmelCase       )


    @slow
    @require_bnb
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      from transformers import AutoModelForCausalLM

      _UpperCAmelCase    			=   Accelerator()

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

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

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


    @slow
    @require_bnb
    @require_multi_gpu
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      from transformers import AutoModelForCausalLM

      _UpperCAmelCase    			=   {"""distributed_type""": DistributedType.MULTI_GPU}

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

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

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

      PartialState._reset_state()


    @slow
    @require_bnb
    @require_multi_gpu
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      from transformers import AutoModelForCausalLM

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

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

      # This should work
      _UpperCAmelCase    			=   accelerator.prepare(__lowerCAmelCase       )


    @require_cuda
    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   torch.nn.Linear(10 ,					10       )
      _UpperCAmelCase    			=   torch.optim.SGD(model.parameters() ,					lr=0.01       )
      _UpperCAmelCase    			=   Accelerator(cpu=__lowerCAmelCase       )
      _UpperCAmelCase    			=   accelerator.prepare(__lowerCAmelCase       )

"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase = False		):







  """simple docstring"""
  if radian_mode:
    return [magnitude * cos(lowercase		), magnitude * sin(lowercase		)]
  return [magnitude * cos(radians(lowercase		)		), magnitude * sin(radians(lowercase		)		)]






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase = 10**-1		):







  """simple docstring"""
  _UpperCAmelCase    			=   cross(lowercase ,lowercase		)
  _UpperCAmelCase    			=   sum(lowercase		)
  return abs(lowercase		) < eps


if __name__ == "__main__":
    # Test to check if it works
    UpperCAmelCase__			=    array(
        [
            polar_force(718.4, 1_8_0 - 3_0),
            polar_force(879.54, 4_5),
            polar_force(1_0_0, -9_0),
        ]
    )

    UpperCAmelCase__			=    array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem 1 in image_data/2D_problems.jpg
    UpperCAmelCase__			=    array(
        [
            polar_force(3_0 * 9.81, 1_5),
            polar_force(2_1_5, 1_8_0 - 4_5),
            polar_force(2_6_4, 9_0 - 3_0),
        ]
    )

    UpperCAmelCase__			=    array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem in image_data/2D_problems_1.jpg
    UpperCAmelCase__			=    array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])

    UpperCAmelCase__			=    array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])

    assert in_static_equilibrium(forces, location)

    import doctest

    doctest.testmod()
