




from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

       for param, grad_param in zip(model_a.parameters()				,       model_b.parameters()		):
              if not param.requires_grad:
                     continue
              if not did_step:
                     # Grads should not be in sync
                     assert (
                         torch.allclose(param.grad				,       grad_param.grad		) is False
                     ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
              else:
                     # Grads should be in sync
                     assert (
                         torch.allclose(param.grad				,       grad_param.grad		) is True
                     ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=True		)     -> Union[str, Any]:



       """simple docstring"""

       model.train()
       A__   				=					model(lowercase_		)
       A__   				=					F.mse_loss(lowercase_				,       target.to(output.device		)		)
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(lowercase_		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_=False		)     -> Optional[int]:



       """simple docstring"""

       set_seed(42		)
       A__   				=					RegressionModel()
       A__   				=					deepcopy(lowercase_		)
       A__   				=					RegressionDataset(length=80		)
       A__   				=					DataLoader(lowercase_				,       batch_size=16		)
       model.to(accelerator.device		)
       if sched:
              A__   				=					AdamW(params=model.parameters()				,       lr=1E-3		)
              A__   				=					AdamW(params=ddp_model.parameters()				,       lr=1E-3		)
              A__   				=					LambdaLR(lowercase_				,       lr_lambda=lambda lowercase_		: epoch**0.65		)
              A__   				=					LambdaLR(lowercase_				,       lr_lambda=lambda lowercase_		: epoch**0.65		)
       # Make a copy of `model`
       if sched:
              A__				,							A__				,							A__				,							A__   				=					accelerator.prepare(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
       else:
              A__				,							A__   				=					accelerator.prepare(lowercase_				,       lowercase_		)
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

       A__				,							A__				,							A__   				=					get_training_setup(lowercase_		)
       # Use a single batch
       A__				,							A__   				=					next(iter(lowercase_		)		).values()
       for iteration in range(3		):
              # Gather the distributed inputs and targs for the base model
              A__				,							A__   				=					accelerator.gather((ddp_input, ddp_target)		)
              A__				,							A__   				=					input.to(accelerator.device		), target.to(accelerator.device		)
              # Perform our initial ground truth step in non "DDP"
              step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(lowercase_		):
                            step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              else:
                     # Sync grads
                     step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              for param, ddp_param in zip(model.parameters()				,       ddp_model.parameters()		):
                     if not param.requires_grad:
                            continue
                     assert torch.allclose(
                         param.grad				,       ddp_param.grad		), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration		)
              A__   				=					ddp_input[torch.randperm(len(lowercase_		)		)]







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       A__				,							A__				,							A__   				=					get_training_setup(lowercase_		)
       # Use a single batch
       A__				,							A__   				=					next(iter(lowercase_		)		).values()
       for iteration in range(3		):
              # Gather the distributed inputs and targs for the base model
              A__				,							A__   				=					accelerator.gather((ddp_input, ddp_target)		)
              A__				,							A__   				=					input.to(accelerator.device		), target.to(accelerator.device		)
              # Perform our initial ground truth step in non "DDP"
              step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(lowercase_		):
                            step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              else:
                     # Sync grads
                     step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()				,       ddp_model.parameters()		):
                     if not param.requires_grad:
                            continue
                     if iteration % 2 == 0:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad				,       ddp_param.grad		) is False
                            ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad				,       ddp_param.grad		) is True
                            ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration		)
              A__   				=					ddp_input[torch.randperm(len(lowercase_		)		)]







def SCREAMING_SNAKE_CASE       ( lowercase_=False				,       lowercase_=False		)     -> Tuple:



       """simple docstring"""

       A__   				=					Accelerator(
           split_batches=lowercase_				,       dispatch_batches=lowercase_				,       gradient_accumulation_steps=2		)
       # Test that context manager behaves properly
       A__				,							A__				,							A__   				=					get_training_setup(lowercase_		)
       for iteration, batch in enumerate(lowercase_		):
              A__				,							A__   				=					batch.values()
              # Gather the distributed inputs and targs for the base model
              A__				,							A__   				=					accelerator.gather((ddp_input, ddp_target)		)
              A__				,							A__   				=					input.to(accelerator.device		), target.to(accelerator.device		)
              # Perform our initial ground truth step in non "DDP"
              step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(lowercase_		):
                     step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()				,       ddp_model.parameters()		):
                     if not param.requires_grad:
                            continue
                     if ((iteration + 1) % 2 == 0) or (iteration == len(lowercase_		) - 1):
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad				,       ddp_param.grad		) is True
                            ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad				,       ddp_param.grad		) is False
                            ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration		)
              A__   				=					ddp_input[torch.randperm(len(lowercase_		)		)]
       GradientState._reset_state()







def SCREAMING_SNAKE_CASE       ( lowercase_=False				,       lowercase_=False		)     -> List[Any]:



       """simple docstring"""

       A__   				=					Accelerator(
           split_batches=lowercase_				,       dispatch_batches=lowercase_				,       gradient_accumulation_steps=2		)
       # Test that context manager behaves properly
       A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__   				=					get_training_setup(lowercase_				,       lowercase_		)
       for iteration, batch in enumerate(lowercase_		):
              A__				,							A__   				=					batch.values()
              # Gather the distributed inputs and targs for the base model
              A__				,							A__   				=					accelerator.gather((ddp_input, ddp_target)		)
              A__				,							A__   				=					input.to(accelerator.device		), target.to(accelerator.device		)
              # Perform our initial ground truth step in non "DDP"
              model.train()
              ddp_model.train()
              step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase_		)):
                     if split_batches:
                            sched.step()
                     else:
                            for _ in range(accelerator.num_processes		):
                                   sched.step()
              opt.zero_grad()
              # Perform gradient accumulation under wrapper
              with accelerator.accumulate(lowercase_		):
                     step_model(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
                     ddp_opt.step()
                     ddp_sched.step()
                     ddp_opt.zero_grad()

              # Learning rates should be the same
              assert (
                  opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
              ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
              A__   				=					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase_		))
              if accelerator.num_processes > 1:
                     check_model_parameters(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration		)
       GradientState._reset_state()







def SCREAMING_SNAKE_CASE       ( )     -> Optional[Any]:



       """simple docstring"""

       A__   				=					Accelerator()

       A__   				=					RegressionDataset(length=80		)
       A__   				=					DataLoader(lowercase_				,       batch_size=16		)
       A__   				=					RegressionDataset(length=96		)
       A__   				=					DataLoader(lowercase_				,       batch_size=16		)
       A__				,							A__   				=					accelerator.prepare(lowercase_				,       lowercase_		)
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(lowercase_		):
              assert id(accelerator.gradient_state.active_dataloader		) == id(lowercase_		)
              if iteration < len(lowercase_		) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(lowercase_		):
                                   assert id(accelerator.gradient_state.active_dataloader		) == id(lowercase_		)
                                   if batch_num < len(lowercase_		) - 1:
                                          assert not accelerator.gradient_state.end_of_dataloader
                                   else:
                                          assert accelerator.gradient_state.end_of_dataloader
              else:
                     assert accelerator.gradient_state.end_of_dataloader
       assert accelerator.gradient_state.active_dataloader is None







def SCREAMING_SNAKE_CASE       ( )     -> Optional[Any]:



       """simple docstring"""

       A__   				=					Accelerator()
       A__   				=					accelerator.state
       if state.local_process_index == 0:
              print('''**Test `accumulate` gradient accumulation with dataloader break**'''		)
       test_dataloader_break()
       if state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print('''**Test NOOP `no_sync` context manager**'''		)
              test_noop_sync(lowercase_		)
       if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
              if state.local_process_index == 0:
                     print('''**Test Distributed `no_sync` context manager**'''		)
              test_distributed_sync(lowercase_		)
       if state.distributed_type == DistributedType.MULTI_GPU:
              for split_batch in [True, False]:
                     for dispatch_batches in [True, False]:
                            if state.local_process_index == 0:
                                   print(
                                       '''**Test `accumulate` gradient accumulation, '''				,       f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""				,       )
                            test_gradient_accumulation(lowercase_				,       lowercase_		)

    # Currently will break on torch 2.0 +, need to investigate why
       if is_torch_version('''<'''				,       '''2.0'''		) or state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print(
                         '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''				,       '''`split_batches=False`, `dispatch_batches=False`**'''				,       )
              test_gradient_accumulation_with_opt_and_scheduler()
              if state.distributed_type == DistributedType.MULTI_GPU:
                     for split_batch in [True, False]:
                            for dispatch_batches in [True, False]:
                                   if not split_batch and not dispatch_batches:
                                          continue
                                   if state.local_process_index == 0:
                                          print(
                                              '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''				,       f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""				,       )
                                   test_gradient_accumulation_with_opt_and_scheduler(lowercase_				,       lowercase_		)







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

       main()


if __name__ == "__main__":
  main()








# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       return (-y * np.log(lowercase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.dot(lowercase_				,       lowercase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowercase_		)		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

       for iterations in range(lowercase_		):
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					np.dot(x.T				,       h - y		) / y.size
              A__   				=					theta - alpha * gradient  # updating the weights
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					cost_function(lowercase_				,       lowercase_		)
              if iterations % 100 == 0:
                     print(f"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
       return theta


# In[68]:

if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     datasets.load_iris()
  _lowerCamelCase			:	Any							 =     iris.data[:, :2]
  _lowerCamelCase			:	Dict							 =     (iris.target != 0) * 1

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


