


"""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_   (					UpperCamelCase__						:			Optional[Any]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Optional[int]			):






 """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_   (					UpperCamelCase__						:			Optional[Any]					, UpperCamelCase__						:			Optional[Any]					, UpperCamelCase__						:			str					, UpperCamelCase__						:			Dict					, UpperCamelCase__						:			Optional[int]=True			):






 """simple docstring"""







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


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			Optional[Any]=False			):






 """simple docstring"""







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


def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







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

  # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
  check_model_parameters(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
  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			)
  __lowercase						=						ddp_input[torch.randperm(len(UpperCamelCase__			)			)]


def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







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

  # 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			)
  __lowercase						=						ddp_input[torch.randperm(len(UpperCamelCase__			)			)]


def lowerCAmelCase_   (					UpperCamelCase__						:			str=False					, UpperCamelCase__						:			Optional[int]=False			):






 """simple docstring"""







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

  # 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(UpperCamelCase__			) - 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			)
  __lowercase						=						ddp_input[torch.randperm(len(UpperCamelCase__			)			)]
 GradientState._reset_state()


def lowerCAmelCase_   (					UpperCamelCase__						:			str=False					, UpperCamelCase__						:			Union[str, Any]=False			):






 """simple docstring"""







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

  if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__			)):
   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(UpperCamelCase__			):
   step_model(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
   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'''
  __lowercase						=						(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__			))
  if accelerator.num_processes > 1:
   check_model_parameters(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
  # Shuffle ddp_input on each iteration
  torch.manual_seed(1337 + iteration			)
 GradientState._reset_state()


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						Accelerator()

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






 """simple docstring"""







 __lowercase						=						Accelerator()
 __lowercase						=						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(UpperCamelCase__			)
 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(UpperCamelCase__			)
 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(UpperCamelCase__					, UpperCamelCase__			)

    # 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(UpperCamelCase__					, UpperCamelCase__			)


def lowerCAmelCase_   (					UpperCamelCase__						:			Tuple			):






 """simple docstring"""







 main()


if __name__ == "__main__":
       main()




"""simple docstring"""

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase__					=logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__    :
      a			:  str           =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
      a			:  str           =		field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
      a			:  int           =		field(
          default=1_28      , metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }      , )
      a			:  bool           =		field(
          default=_a      , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.task_name.lower()






class  lowerCamelCase__    (		_a     ):
      a			:  List[Any]           =		"""train"""
      a			:  List[str]           =		"""dev"""
      a			:  Optional[int]           =		"""test"""






class  lowerCamelCase__    (		_a     ):
      a			:  GlueDataTrainingArguments
      a			:  str
      a			:  List[InputFeatures]
      def __init__(		self						:						Optional[int]   ,							A_						:						GlueDataTrainingArguments   ,							A_						:						PreTrainedTokenizerBase   ,							A_						:						Optional[int] = None   ,							A_						:						Union[str, Split] = Split.train   ,							A_						:						Optional[str] = None   ,							):






       '''simple docstring'''


       warnings.warn(
           """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
           """library. You can have a look at this example script for pointers: """
           """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""   ,							A_   ,							)
       __lowercase						=						args
       __lowercase						=						glue_processors[args.task_name]()
       __lowercase						=						glue_output_modes[args.task_name]
       if isinstance(A_   ,							A_				):
        try:
         __lowercase						=						Split[mode]
        except KeyError:
         raise KeyError("""mode is not a valid split name"""				)
        # Load data features from cache or dataset file
       __lowercase						=						os.path.join(
           cache_dir if cache_dir is not None else args.data_dir   ,							F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''   ,							)
       __lowercase						=						self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        __lowercase				,		__lowercase						=						label_list[2], label_list[1]
       __lowercase						=						label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       __lowercase						=						cached_features_file + """.lock"""
       with FileLock(A_				):
        if os.path.exists(A_				) and not args.overwrite_cache:
         __lowercase						=						time.time()
         __lowercase						=						torch.load(A_				)
         logger.info(
             F'''Loading features from cached file {cached_features_file} [took %.3f s]'''   ,							time.time() - start				)
        else:
         logger.info(F'''Creating features from dataset file at {args.data_dir}'''				)

         if mode == Split.dev:
          __lowercase						=						self.processor.get_dev_examples(args.data_dir				)
         elif mode == Split.test:
          __lowercase						=						self.processor.get_test_examples(args.data_dir				)
         else:
          __lowercase						=						self.processor.get_train_examples(args.data_dir				)
         if limit_length is not None:
          __lowercase						=						examples[:limit_length]
         __lowercase						=						glue_convert_examples_to_features(
             A_   ,							A_   ,							max_length=args.max_seq_length   ,							label_list=A_   ,							output_mode=self.output_mode   ,							)
         __lowercase						=						time.time()
         torch.save(self.features   ,							A_				)
         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
         logger.info(
             F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''				)
      def __len__(		self						:						int				):






       '''simple docstring'''


       return len(self.features				)
      def __getitem__(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       return self.features[i]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self.label_list
