


"""simple docstring"""



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		int:


    """simple docstring"""





    snake_case					:   Tuple         =					AutoTokenizer.from_pretrained("bert-base-cased"				)
    snake_case					:   Union[str, Any]         =					load_dataset("glue"		,							"mrpc"				)

    def tokenize_function(lowercase						:						Optional[int]				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   Dict         =					tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=lowercase		,							max_length=lowercase				)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case					:   Tuple         =					datasets.map(
            lowercase		,							batched=lowercase		,							remove_columns=["idx", "sentence1", "sentence2"]		,							)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case					:   Union[str, Any]         =					tokenized_datasets.rename_column("label"		,							"labels"				)

    def collate_fn(lowercase						:						List[Any]				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case					:   Dict         =					128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case					:   List[str]         =					16
        elif accelerator.mixed_precision != "no":
            snake_case					:   Any         =					8
        else:
            snake_case					:   Optional[Any]         =					None

        return tokenizer.pad(
            lowercase		,							padding="longest"		,							max_length=lowercase		,							pad_to_multiple_of=lowercase		,							return_tensors="pt"		,							)

    # Instantiate dataloaders.
    snake_case					:   Optional[int]         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   Tuple         =					DataLoader(
        tokenized_datasets["validation"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __snake_case         =					mocked_dataloaders  # noqa: F811







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


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
        snake_case					:   Tuple         =					2
    # New Code #
    snake_case					:   List[Any]         =					int(args.gradient_accumulation_steps				)
    # Initialize accelerator
    snake_case					:   Optional[Any]         =					Accelerator(
        cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							gradient_accumulation_steps=lowercase				)
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"				)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case					:   Union[str, Any]         =					config["lr"]
    snake_case					:   Optional[int]         =					int(config["num_epochs"]				)
    snake_case					:   Union[str, Any]         =					int(config["seed"]				)
    snake_case					:   List[Any]         =					int(config["batch_size"]				)

    snake_case					:   List[str]         =					evaluate.load("glue"		,							"mrpc"				)

    set_seed(lowercase				)
    snake_case      ,snake_case					:   List[str]         =					get_dataloaders(lowercase		,							lowercase				)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case					:   Dict         =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=lowercase				)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    snake_case					:   Optional[Any]         =					model.to(accelerator.device				)

    # Instantiate optimizer
    snake_case					:   Any         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Dict         =					get_linear_schedule_with_warmup(
        optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * num_epochs)		,							)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Optional[int]         =					accelerator.prepare(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

    # Now we train the model
    for epoch in range(lowercase				):
        model.train()
        for step, batch in enumerate(lowercase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(lowercase				):
                snake_case					:   List[str]         =					model(**lowercase				)
                snake_case					:   Dict         =					output.loss
                accelerator.backward(lowercase				)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(lowercase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                snake_case					:   Any         =					model(**lowercase				)
            snake_case					:   Union[str, Any]         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   List[str]         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

        snake_case					:   Tuple         =					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,							lowercase				)







def 						__lowerCAmelCase		(		)			->		Optional[Any]:


    """simple docstring"""





    snake_case					:   Any         =					argparse.ArgumentParser(description="Simple example of training script."				)
    parser.add_argument(
        "--mixed_precision"		,							type=lowercase		,							default=lowercase		,							choices=["no", "fp16", "bf16", "fp8"]		,							help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU."		,							)
    # New Code #
    parser.add_argument(
        "--gradient_accumulation_steps"		,							type=lowercase		,							default=1		,							help="The number of minibatches to be ran before gradients are accumulated."		,							)
    parser.add_argument("--cpu"		,							action="store_true"		,							help="If passed, will train on the CPU."				)
    snake_case					:   int         =					parser.parse_args()
    snake_case					:   Dict         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
  main()









"""simple docstring"""



import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


__snake_case         =					NewType("""DataClass""", Any)
__snake_case         =					NewType("""DataClassType""", Any)







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


    """simple docstring"""





    if isinstance(lowercase		,							lowercase				):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise ArgumentTypeError(
            F'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'				)







def 						__lowerCAmelCase		(		lowercase						:						list				)			->		Callable[[str], Any]:


    """simple docstring"""





    snake_case					:   Any         =					{str(lowercase				): choice for choice in choices}
    return lambda lowercase				: str_to_choice.get(lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		*,
    lowercase						:						Union[str, List[str]] = None		,							lowercase						:						str = None		,							lowercase						:						Any = dataclasses.MISSING		,							lowercase						:						Callable[[], Any] = dataclasses.MISSING		,							lowercase						:						dict = None		,							**lowercase						:						Tuple		,							)			->		dataclasses.Field:


    """simple docstring"""





    if metadata is None:
        # Important, don't use as default param in function signature because dict is mutable and shared across function calls
        snake_case					:   Any         =					{}
    if aliases is not None:
        snake_case					:   List[str]         =					aliases
    if help is not None:
        snake_case					:   Union[str, Any]         =					help

    return dataclasses.field(metadata=lowercase		,							default=lowercase		,							default_factory=lowercase		,							**lowercase				)







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Iterable[DataClassType]







   def __init__(			self		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       if "formatter_class" not in kwargs:
           snake_case					:   List[Any]         =					ArgumentDefaultsHelpFormatter
       super().__init__(**UpperCamelCase__		)
       if dataclasses.is_dataclass(UpperCamelCase__		):
           snake_case					:   str         =					[dataclass_types]
       snake_case					:   Any         =					list(UpperCamelCase__		)
       for dtype in self.dataclass_types:
           self._add_dataclass_arguments(UpperCamelCase__		)







   @staticmethod
   def 				lowerCamelCase	(			UpperCamelCase__		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       snake_case					:   Tuple         =					F'--{field.name}'
       snake_case					:   Optional[Any]         =					field.metadata.copy()
       # field.metadata is not used at all by Data Classes,
       # it is provided as a third-party extension mechanism.
       if isinstance(field.type		,			UpperCamelCase__		):
           raise RuntimeError(
               "Unresolved type detected, which should have been done with the help of "
               "`typing.get_type_hints` method by default"		)

       snake_case					:   int         =					kwargs.pop("aliases"		,			[]		)
       if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   Tuple         =					[aliases]

       snake_case					:   Tuple         =					getattr(field.type		,			"__origin__"		,			field.type		)
       if origin_type is Union or (hasattr(UpperCamelCase__		,			"UnionType"		) and isinstance(UpperCamelCase__		,			types.UnionType		)):
           if str not in field.type.__args__ and (
               len(field.type.__args__		) != 2 or type(UpperCamelCase__		) not in field.type.__args__
           ):
               raise ValueError(
                   "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
                   " the argument parser only supports one type per argument."
                   F' Problem encountered in field \'{field.name}\'.'		)
           if type(UpperCamelCase__		) not in field.type.__args__:
               # filter `str` in Union
               snake_case					:   Union[str, Any]         =					field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
               snake_case					:   str         =					getattr(field.type		,			"__origin__"		,			field.type		)
           elif bool not in field.type.__args__:
               # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
               snake_case					:   List[Any]         =					(
                   field.type.__args__[0] if isinstance(UpperCamelCase__		,			field.type.__args__[1]		) else field.type.__args__[1]
               )
               snake_case					:   Tuple         =					getattr(field.type		,			"__origin__"		,			field.type		)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
       snake_case					:   Optional[int]         =					{}
       if origin_type is Literal or (isinstance(field.type		,			UpperCamelCase__		) and issubclass(field.type		,			UpperCamelCase__		)):
           if origin_type is Literal:
               snake_case					:   Any         =					field.type.__args__
           else:
               snake_case					:   Dict         =					[x.value for x in field.type]

           snake_case					:   List[str]         =					make_choice_type_function(kwargs["choices"]		)

           if field.default is not dataclasses.MISSING:
               snake_case					:   Any         =					field.default
           else:
               snake_case					:   List[Any]         =					True
       elif field.type is bool or field.type == Optional[bool]:
           # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
           # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
           snake_case					:   Optional[Any]         =					copy(UpperCamelCase__		)

           # Hack because type=bool in argparse does not behave as we want.
           snake_case					:   Optional[Any]         =					string_to_bool
           if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
               # Default value is False if we have no default when of type bool.
               snake_case					:   Union[str, Any]         =					False if field.default is dataclasses.MISSING else field.default
               # This is the value that will get picked if we don't include --field_name in any way
               snake_case					:   Any         =					default
               # This tells argparse we accept 0 or 1 value after --field_name
               snake_case					:   Optional[int]         =					"?"
               # This is the value that will get picked if we do --field_name (without value)
               snake_case					:   Any         =					True
       elif isclass(UpperCamelCase__		) and issubclass(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   List[Any]         =					field.type.__args__[0]
           snake_case					:   Tuple         =					"+"
           if field.default_factory is not dataclasses.MISSING:
               snake_case					:   Union[str, Any]         =					field.default_factory()
           elif field.default is dataclasses.MISSING:
               snake_case					:   Dict         =					True
       else:
           snake_case					:   List[str]         =					field.type
           if field.default is not dataclasses.MISSING:
               snake_case					:   List[Any]         =					field.default
           elif field.default_factory is not dataclasses.MISSING:
               snake_case					:   Optional[Any]         =					field.default_factory()
           else:
               snake_case					:   Any         =					True
       parser.add_argument(UpperCamelCase__		,			*UpperCamelCase__		,			**UpperCamelCase__		)

       # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
       # Order is important for arguments with the same destination!
       # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
       # here and we do not need those changes/additional keys.
       if field.default is True and (field.type is bool or field.type == Optional[bool]):
           snake_case					:   Optional[Any]         =					False
           parser.add_argument(F'--no_{field.name}'		,			action="store_false"		,			dest=field.name		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	int:

       '''simple docstring'''




       if hasattr(UpperCamelCase__		,			"_argument_group_name"		):
           snake_case					:   Optional[Any]         =					self.add_argument_group(dtype._argument_group_name		)
       else:
           snake_case					:   int         =					self

       try:
           snake_case					:   Dict[str, type]         =					get_type_hints(UpperCamelCase__		)
       except NameError:
           raise RuntimeError(
               F'Type resolution failed for {dtype}. Try declaring the class in global scope or '
               "removing line of `from __future__ import annotations` which opts in Postponed "
               "Evaluation of Annotations (PEP 563)"		)
       except TypeError as ex:
           # Remove this block when we drop Python 3.9 support
           if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(UpperCamelCase__		):
               snake_case					:   Dict         =					".".join(map(UpperCamelCase__		,			sys.version_info[:3]		)		)
               raise RuntimeError(
                   F'Type resolution failed for {dtype} on Python {python_version}. Try removing '
                   "line of `from __future__ import annotations` which opts in union types as "
                   "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
                   "support Python versions that lower than 3.10, you need to use "
                   "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
                   "`X | None`."		) from ex
           raise

       for field in dataclasses.fields(UpperCamelCase__		):
           if not field.init:
               continue
           snake_case					:   Optional[int]         =					type_hints[field.name]
           self._parse_dataclass_field(UpperCamelCase__		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=None		,			)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
           snake_case					:   Optional[int]         =					[]

           if args_filename:
               args_files.append(Path(UpperCamelCase__		)		)
           elif look_for_args_file and len(sys.argv		):
               args_files.append(Path(sys.argv[0]		).with_suffix(".args"		)		)

           # args files specified via command line flag should overwrite default args files so we add them last
           if args_file_flag:
               # Create special parser just to extract the args_file_flag values
               snake_case					:   str         =					ArgumentParser()
               args_file_parser.add_argument(UpperCamelCase__		,			type=UpperCamelCase__		,			action="append"		)

               # Use only remaining args for further parsing (remove the args_file_flag)
               snake_case      ,snake_case					:   Dict         =					args_file_parser.parse_known_args(args=UpperCamelCase__		)
               snake_case					:   int         =					vars(UpperCamelCase__		).get(args_file_flag.lstrip("-"		)		,			UpperCamelCase__		)

               if cmd_args_file_paths:
                   args_files.extend([Path(UpperCamelCase__		) for p in cmd_args_file_paths]		)

           snake_case					:   Optional[Any]         =					[]
           for args_file in args_files:
               if args_file.exists():
                   file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
           snake_case					:   Dict         =					file_args + args if args is not None else file_args + sys.argv[1:]
       snake_case      ,snake_case					:   Any         =					self.parse_known_args(args=UpperCamelCase__		)
       snake_case					:   List[str]         =					[]
       for dtype in self.dataclass_types:
           snake_case					:   Optional[int]         =					{f.name for f in dataclasses.fields(UpperCamelCase__		) if f.init}
           snake_case					:   Optional[int]         =					{k: v for k, v in vars(UpperCamelCase__		).items() if k in keys}
           for k in keys:
               delattr(UpperCamelCase__		,			UpperCamelCase__		)
           snake_case					:   List[str]         =					dtype(**UpperCamelCase__		)
           outputs.append(UpperCamelCase__		)
       if len(namespace.__dict__		) > 0:
           # additional namespace.
           outputs.append(UpperCamelCase__		)
       if return_remaining_strings:
           return (*outputs, remaining_args)
       else:
           if remaining_args:
               raise ValueError(F'Some specified arguments are not used by the HfArgumentParser: {remaining_args}'		)

           return (*outputs,)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       snake_case					:   int         =					set(args.keys()		)
       snake_case					:   int         =					[]
       for dtype in self.dataclass_types:
           snake_case					:   Tuple         =					{f.name for f in dataclasses.fields(UpperCamelCase__		) if f.init}
           snake_case					:   Dict         =					{k: v for k, v in args.items() if k in keys}
           unused_keys.difference_update(inputs.keys()		)
           snake_case					:   Optional[Any]         =					dtype(**UpperCamelCase__		)
           outputs.append(UpperCamelCase__		)
       if not allow_extra_keys and unused_keys:
           raise ValueError(F'Some keys are not used by the HfArgumentParser: {sorted(UpperCamelCase__		)}'		)
       return tuple(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       with open(Path(UpperCamelCase__		)		,			encoding="utf-8"		) as open_json_file:
           snake_case					:   Optional[Any]         =					json.loads(open_json_file.read()		)
       snake_case					:   int         =					self.parse_dict(UpperCamelCase__		,			allow_extra_keys=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__ = False		)			->	Tuple[DataClass, ...]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.parse_dict(yaml.safe_load(Path(UpperCamelCase__		).read_text()		)		,			allow_extra_keys=UpperCamelCase__		)
       return tuple(UpperCamelCase__		)





