


"""simple docstring"""



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   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Dict						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]			)     ->						List[Any]:


    """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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  List[Any]						,							lowercase  :  Union[str, Any]						,							lowercase  :  Union[str, Any]=True			)     ->						str:


    """simple docstring"""





    model.train()
    snake_case   :				Dict				 =   model(lowercase			)
    snake_case   :				List[str]				 =   F.mse_loss(lowercase						,							target.to(output.device			)			)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(lowercase			)







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Union[str, Any]=False			)     ->						Any:


    """simple docstring"""





    set_seed(42			)
    snake_case   :				Tuple				 =   RegressionModel()
    snake_case   :				Tuple				 =   deepcopy(lowercase			)
    snake_case   :				Any				 =   RegressionDataset(length=80			)
    snake_case   :				str				 =   DataLoader(lowercase						,							batch_size=16			)
    model.to(accelerator.device			)
    if sched:
        snake_case   :				Union[str, Any]				 =   AdamW(params=model.parameters()						,							lr=1e-3			)
        snake_case   :				Union[str, Any]				 =   AdamW(params=ddp_model.parameters()						,							lr=1e-3			)
        snake_case   :				str				 =   LambdaLR(lowercase						,							lr_lambda=lambda lowercase			: epoch**0.65			)
        snake_case   :				int				 =   LambdaLR(lowercase						,							lr_lambda=lambda lowercase			: epoch**0.65			)
    # Make a copy of `model`
    if sched:
        snake_case ,snake_case ,snake_case ,snake_case   :				str				 =   accelerator.prepare(lowercase						,							lowercase						,							lowercase						,							lowercase			)
    else:
        snake_case ,snake_case   :				int				 =   accelerator.prepare(lowercase						,							lowercase			)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]			)     ->						Dict:


    """simple docstring"""





    snake_case ,snake_case ,snake_case   :				str				 =   get_training_setup(lowercase			)
    # Use a single batch
    snake_case ,snake_case   :				Optional[int]				 =   next(iter(lowercase			)			).values()
    for iteration in range(3			):
        # Gather the distributed inputs and targs for the base model
        snake_case ,snake_case   :				str				 =   accelerator.gather((ddp_input, ddp_target)			)
        snake_case ,snake_case   :				Any				 =   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(1337 + iteration			)
        snake_case   :				Union[str, Any]				 =   ddp_input[torch.randperm(len(lowercase			)			)]







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case ,snake_case ,snake_case   :				Dict				 =   get_training_setup(lowercase			)
    # Use a single batch
    snake_case ,snake_case   :				Optional[Any]				 =   next(iter(lowercase			)			).values()
    for iteration in range(3			):
        # Gather the distributed inputs and targs for the base model
        snake_case ,snake_case   :				Optional[Any]				 =   accelerator.gather((ddp_input, ddp_target)			)
        snake_case ,snake_case   :				Optional[Any]				 =   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(1337 + iteration			)
        snake_case   :				List[str]				 =   ddp_input[torch.randperm(len(lowercase			)			)]







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]=False						,							lowercase  :  Optional[Any]=False			)     ->						Tuple:


    """simple docstring"""





    snake_case   :				Union[str, Any]				 =   Accelerator(
        split_batches=lowercase						,							dispatch_batches=lowercase						,							gradient_accumulation_steps=2			)
    # Test that context manager behaves properly
    snake_case ,snake_case ,snake_case   :				str				 =   get_training_setup(lowercase			)
    for iteration, batch in enumerate(lowercase			):
        snake_case ,snake_case   :				List[str]				 =   batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case ,snake_case   :				str				 =   accelerator.gather((ddp_input, ddp_target)			)
        snake_case ,snake_case   :				Dict				 =   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(1337 + iteration			)
        snake_case   :				str				 =   ddp_input[torch.randperm(len(lowercase			)			)]
    GradientState._reset_state()







def   __lowerCAmelCase						(						lowercase  :  Any=False						,							lowercase  :  Tuple=False			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[int]				 =   Accelerator(
        split_batches=lowercase						,							dispatch_batches=lowercase						,							gradient_accumulation_steps=2			)
    # Test that context manager behaves properly
    snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case   :				Tuple				 =   get_training_setup(lowercase						,							lowercase			)
    for iteration, batch in enumerate(lowercase			):
        snake_case ,snake_case   :				Optional[Any]				 =   batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case ,snake_case   :				str				 =   accelerator.gather((ddp_input, ddp_target)			)
        snake_case ,snake_case   :				List[Any]				 =   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'
        snake_case   :				str				 =   (((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(1337 + iteration			)
    GradientState._reset_state()







def   __lowerCAmelCase						(						)     ->						Dict:


    """simple docstring"""





    snake_case   :				Union[str, Any]				 =   Accelerator()

    snake_case   :				List[Any]				 =   RegressionDataset(length=80			)
    snake_case   :				Tuple				 =   DataLoader(lowercase						,							batch_size=16			)
    snake_case   :				Dict				 =   RegressionDataset(length=96			)
    snake_case   :				Dict				 =   DataLoader(lowercase						,							batch_size=16			)
    snake_case ,snake_case   :				List[str]				 =   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   __lowerCAmelCase						(						)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				Optional[int]				 =   Accelerator()
    snake_case   :				Optional[int]				 =   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   __lowerCAmelCase						(						lowercase  :  Tuple			)     ->						str:


    """simple docstring"""





    main()


if __name__ == "__main__":
  main()









"""simple docstring"""



import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__snake_case             =   logging.getLogger()







@unittest.skip('''Temporarily disable the doc tests.'''     )
@require_torch
@require_tf
@slow
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     )     ->		Tuple:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   [file for file in os.listdir(UpperCamelCase__						) if os.path.isfile(os.path.join(UpperCamelCase__						,     UpperCamelCase__						)						)]

       if identifier is not None:
           snake_case   :				Optional[Any]				 =   [file for file in files if identifier in file]

       if n_identifier is not None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               for n_ in n_identifier:
                   snake_case   :				str				 =   [file for file in files if n_ not in file]
           else:
               snake_case   :				str				 =   [file for file in files if n_identifier not in file]

       snake_case   :				Tuple				 =   ignore_files or []
       ignore_files.append("__init__.py"						)
       snake_case   :				Optional[Any]				 =   [file for file in files if file not in ignore_files]

       for file in files:
           # Open all files
           print("Testing"						,     UpperCamelCase__						)

           if only_modules:
               snake_case   :				str				 =   file.split("."						)[0]
               try:
                   snake_case   :				Optional[int]				 =   getattr(UpperCamelCase__						,     UpperCamelCase__						)
                   snake_case   :				str				 =   doctest.DocTestSuite(UpperCamelCase__						)
                   snake_case   :				Optional[Any]				 =   unittest.TextTestRunner().run(UpperCamelCase__						)
                   self.assertIs(len(result.failures						)						,     0						)
               except AttributeError:
                   logger.info(F'{module_identifier} is not a module.'						)
           else:
               snake_case   :				Tuple				 =   doctest.testfile(str(".." / directory / file						)						,     optionflags=doctest.ELLIPSIS						)
               self.assertIs(result.failed						,     0						)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Tuple				 =   Path("src/transformers"						)
       snake_case   :				List[Any]				 =   "modeling"
       snake_case   :				Dict				 =   [
           "modeling_ctrl.py",
           "modeling_tf_ctrl.py",
       ]
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						,     ignore_files=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   Path("src/transformers"						)
       snake_case   :				Optional[Any]				 =   "tokenization"
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   Path("src/transformers"						)
       snake_case   :				Optional[Any]				 =   "configuration"
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   Path("src/transformers"						)
       snake_case   :				List[str]				 =   ["configuration", "modeling", "tokenization"]
       self.analyze_directory(UpperCamelCase__						,     n_identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Any				 =   Path("docs/source"						)
       snake_case   :				Tuple				 =   ["favicon.ico"]
       self.analyze_directory(UpperCamelCase__						,     ignore_files=UpperCamelCase__						,     only_modules=UpperCamelCase__						)





