
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,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

A     :	List[str]    = 1_6
A     :	Any    = 3_2


def     __lowerCAmelCase			(			a__						,    a__ = 16  )       ->  List[Any]:
				__a										=    AutoTokenizer.from_pretrained('''bert-base-cased'''  )
				__a										=    load_dataset('''glue'''						,    '''mrpc'''  )

				def tokenize_function(a__  ):
								# max_length=None => use the model max length (it's actually the default)
								__a										=    tokenizer(examples['''sentence1''']						,    examples['''sentence2''']						,    truncation=a__						,    max_length=a__  )
								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():
								__a										=    datasets.map(
								    a__						,    batched=a__						,    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
				__a										=    tokenized_datasets.rename_column('''label'''						,    '''labels'''  )

				def collate_fn(a__  ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__a										=    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":
												__a										=    16
								elif accelerator.mixed_precision != "no":
												__a										=    8
								else:
												__a										=    None

								return tokenizer.pad(
								    a__						,    padding='''longest'''						,    max_length=a__						,    pad_to_multiple_of=a__						,    return_tensors='''pt'''						,    )

				# Instantiate dataloaders.
				__a										=    DataLoader(
				    tokenized_datasets['''train''']						,    shuffle=a__						,    collate_fn=a__						,    batch_size=a__  )
				__a										=    DataLoader(
				    tokenized_datasets['''validation''']						,    shuffle=a__						,    collate_fn=a__						,    batch_size=a__  )

				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

					A     :	Any    = mocked_dataloaders  # noqa: F811


def     __lowerCAmelCase			(			a__						,    a__  )       ->  int:





				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,    a__  ) == "1":
								__a										=    2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__a										=    Accelerator(
								    cpu=args.cpu						,    mixed_precision=args.mixed_precision						,    log_with='''all'''						,    project_dir=args.project_dir  )
				else:
								__a										=    Accelerator(cpu=args.cpu						,    mixed_precision=args.mixed_precision  )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__a										=    config["""lr"""]
				__a										=    int(config['''num_epochs''']  )
				__a										=    int(config['''seed''']  )
				__a										=    int(config['''batch_size''']  )
				set_seed(a__  )

				__a										=    get_dataloaders(a__						,    a__  )
				__a										=    evaluate.load('''glue'''						,    '''mrpc'''  )

				# If the batch size is too big we use gradient accumulation
				__a										=    1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__a										=    batch_size // MAX_GPU_BATCH_SIZE
								__a										=    MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__a										=    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,    return_dict=a__  )

				# 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).
				__a										=    model.to(accelerator.device  )

				# Instantiate optimizer
				__a										=    AdamW(params=model.parameters()						,    lr=a__  )

				# Instantiate scheduler
				__a										=    get_linear_schedule_with_warmup(
				    optimizer=a__						,    num_warmup_steps=100						,    num_training_steps=(len(a__  ) * num_epochs) // gradient_accumulation_steps						,    )

				# 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.
				__a										=    accelerator.prepare(
				    a__						,    a__						,    a__						,    a__						,    a__  )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								__a										=    os.path.split(a__  )[-1].split('''.'''  )[0]
								accelerator.init_trackers(a__						,    a__  )

				# Now we train the model
				for epoch in range(a__  ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												__a										=    0
								for step, batch in enumerate(a__  ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												__a										=    model(**a__  )
												__a										=    outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__a										=    loss / gradient_accumulation_steps
												accelerator.backward(a__  )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								__a										=    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""						,    a__  )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': eval_metric['''accuracy'''],
												        '''f1''': eval_metric['''f1'''],
												        '''train_loss''': total_loss.item() / len(a__  ),
												        '''epoch''': epoch,
												    }						,    step=a__						,    )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()


def     __lowerCAmelCase			(			)       ->  int:
				__a										=    argparse.ArgumentParser(description='''Simple example of training script.'''  )
				parser.add_argument(
				    '''--mixed_precision'''						,    type=a__						,    default=a__						,    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.'''						,    )
				parser.add_argument('''--cpu'''						,    action='''store_true'''						,    help='''If passed, will train on the CPU.'''  )
				parser.add_argument(
				    '''--with_tracking'''						,    action='''store_true'''						,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''						,    )
				parser.add_argument(
				    '''--project_dir'''						,    type=a__						,    default='''logs'''						,    help='''Location on where to store experiment tracking logs` and relevent project information'''						,    )
				__a										=    parser.parse_args()
				__a										=    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(a__						,    a__  )


if __name__ == "__main__":
					main()




'''simple docstring'''





from math import sqrt
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (
        number >= 0
    ), "'number' must been an int and positive"

    lowerCAmelCase__ :  int							      =					True

    # 0 and 1 are none primes.
    if number <= 1:
        lowerCAmelCase__ :  Optional[Any]							      =					False

    for divisor in range(2							,    int(round(sqrt(UpperCamelCase			)			)			) + 1			):
        # if 'number' divisible by 'divisor' then sets 'status'
        # of false and break up the loop.
        if number % divisor == 0:
            lowerCAmelCase__ :  Any							      =					False
            break

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'status' must been from type bool"

    return status
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (n > 2), "'N' must been an int and > 2"

    # beginList: contains all natural numbers from 2 up to N
    lowerCAmelCase__ :  List[str]							      =					list(range(2							,    n + 1			)			)

    lowerCAmelCase__ :  str							      =					[]  # this list will be returns.

    # actual sieve of erathostenes
    for i in range(len(UpperCamelCase			)			):
        for j in range(i + 1							,    len(UpperCamelCase			)			):
            if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                lowerCAmelCase__ :  List[Any]							      =					0

    # filters actual prime numbers.
    lowerCAmelCase__ :  List[Any]							      =					[x for x in begin_list if x != 0]

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'ans' must been from type list"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (n > 2), "'N' must been an int and > 2"

    lowerCAmelCase__ :  List[str]							      =					[]

    # iterates over all numbers between 2 up to N+1
    # if a number is prime then appends to list 'ans'
    for number in range(2							,    n + 1			):
        if is_prime(UpperCamelCase			):
            ans.append(UpperCamelCase			)

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'ans' must been from type list"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and number >= 0, "'number' must been an int and >= 0"

    lowerCAmelCase__ :  Optional[Any]							      =					[]  # this list will be returns of the function.

    # potential prime number factors.

    lowerCAmelCase__ :  Dict							      =					2

    lowerCAmelCase__ :  Dict							      =					number

    if number == 0 or number == 1:
        ans.append(UpperCamelCase			)

    # if 'number' not prime then builds the prime factorization of 'number'
    elif not is_prime(UpperCamelCase			):
        while quotient != 1:
            if is_prime(UpperCamelCase			) and (quotient % factor == 0):
                ans.append(UpperCamelCase			)
                quotient /= factor
            else:
                factor += 1

    else:
        ans.append(UpperCamelCase			)

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'ans' must been from type list"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (
        number >= 0
    ), "'number' bust been an int and >= 0"

    lowerCAmelCase__ :  Optional[int]							      =					0

    # prime factorization of 'number'
    lowerCAmelCase__ :  List[str]							      =					prime_factorization(UpperCamelCase			)

    lowerCAmelCase__ :  Any							      =					max(UpperCamelCase			)

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'ans' must been from type int"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (
        number >= 0
    ), "'number' bust been an int and >= 0"

    lowerCAmelCase__ :  List[Any]							      =					0

    # prime factorization of 'number'
    lowerCAmelCase__ :  List[str]							      =					prime_factorization(UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					min(UpperCamelCase			)

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'ans' must been from type int"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'number' must been an int"
    assert isinstance(number % 2 == 0							,    UpperCamelCase			), "compare bust been from type bool"

    return number % 2 == 0
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "'number' must been an int"
    assert isinstance(number % 2 != 0							,    UpperCamelCase			), "compare bust been from type bool"

    return number % 2 != 0
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			) and (number > 2) and is_even(UpperCamelCase			)
    ), "'number' must been an int, even and > 2"

    lowerCAmelCase__ :  Dict							      =					[]  # this list will returned

    # creates a list of prime numbers between 2 up to 'number'
    lowerCAmelCase__ :  Dict							      =					get_prime_numbers(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					len(UpperCamelCase			)

    # run variable for while-loops.
    lowerCAmelCase__ :  List[str]							      =					0
    lowerCAmelCase__ :  List[Any]							      =					None

    # exit variable. for break up the loops
    lowerCAmelCase__ :  Any							      =					True

    while i < len_pn and loop:
        lowerCAmelCase__ :  List[Any]							      =					i + 1

        while j < len_pn and loop:
            if prime_numbers[i] + prime_numbers[j] == number:
                lowerCAmelCase__ :  Optional[Any]							      =					False
                ans.append(prime_numbers[i]			)
                ans.append(prime_numbers[j]			)

            j += 1

        i += 1

    # precondition
    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and (len(UpperCamelCase			) == 2)
        and (ans[0] + ans[1] == number)
        and is_prime(ans[0]			)
        and is_prime(ans[1]			)
    ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and isinstance(UpperCamelCase							,    UpperCamelCase			)
        and (numbera >= 0)
        and (numbera >= 0)
    ), "'number1' and 'number2' must been positive integer."

    lowerCAmelCase__ :  int							      =					0

    while numbera != 0:
        lowerCAmelCase__ :  Any							      =					numbera % numbera
        lowerCAmelCase__ :  str							      =					numbera
        lowerCAmelCase__ :  List[str]							      =					rest

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (
        numbera >= 0
    ), "'number' must been from type int and positive"

    return numbera
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and isinstance(UpperCamelCase							,    UpperCamelCase			)
        and (numbera >= 1)
        and (numbera >= 1)
    ), "'number1' and 'number2' must been positive integer."

    lowerCAmelCase__ :  int							      =					1  # actual answer that will be return.

    # for kgV (x,1)
    if numbera > 1 and numbera > 1:
        # builds the prime factorization of 'number1' and 'number2'
        lowerCAmelCase__ :  int							      =					prime_factorization(UpperCamelCase			)
        lowerCAmelCase__ :  Any							      =					prime_factorization(UpperCamelCase			)

    elif numbera == 1 or numbera == 1:
        lowerCAmelCase__ :  Optional[Any]							      =					[]
        lowerCAmelCase__ :  Dict							      =					[]
        lowerCAmelCase__ :  List[str]							      =					max(UpperCamelCase							,    UpperCamelCase			)

    lowerCAmelCase__ :  Tuple							      =					0
    lowerCAmelCase__ :  str							      =					0

    lowerCAmelCase__ :  List[Any]							      =					[]  # captured numbers int both 'primeFac1' and 'primeFac2'

    # iterates through primeFac1
    for n in prime_fac_a:
        if n not in done:
            if n in prime_fac_a:
                lowerCAmelCase__ :  int							      =					prime_fac_a.count(UpperCamelCase			)
                lowerCAmelCase__ :  Any							      =					prime_fac_a.count(UpperCamelCase			)

                for _ in range(max(UpperCamelCase							,    UpperCamelCase			)			):
                    ans *= n

            else:
                lowerCAmelCase__ :  Any							      =					prime_fac_a.count(UpperCamelCase			)

                for _ in range(UpperCamelCase			):
                    ans *= n

            done.append(UpperCamelCase			)

    # iterates through primeFac2
    for n in prime_fac_a:
        if n not in done:
            lowerCAmelCase__ :  Optional[int]							      =					prime_fac_a.count(UpperCamelCase			)

            for _ in range(UpperCamelCase			):
                ans *= n

            done.append(UpperCamelCase			)

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (
        ans >= 0
    ), "'ans' must been from type int and positive"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (n >= 0), "'number' must been a positive int"

    lowerCAmelCase__ :  Optional[Any]							      =					0
    lowerCAmelCase__ :  Tuple							      =					2  # this variable holds the answer

    while index < n:
        index += 1

        ans += 1  # counts to the next number

        # if ans not prime then
        # runs to the next prime number.
        while not is_prime(UpperCamelCase			):
            ans += 1

    # precondition
    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and is_prime(
        UpperCamelCase			), "'ans' must been a prime number and from type int"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    assert (
        is_prime(UpperCamelCase			) and is_prime(UpperCamelCase			) and (p_number_a < p_number_a)
    ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

    lowerCAmelCase__ :  Dict							      =					p_number_a + 1  # jump to the next number

    lowerCAmelCase__ :  List[Any]							      =					[]  # this list will be returns.

    # if number is not prime then
    # fetch the next prime number.
    while not is_prime(UpperCamelCase			):
        number += 1

    while number < p_number_a:
        ans.append(UpperCamelCase			)

        number += 1

        # fetch the next prime number.
        while not is_prime(UpperCamelCase			):
            number += 1

    # precondition
    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and ans[0] != p_number_a
        and ans[len(UpperCamelCase			) - 1] != p_number_a
    ), "'ans' must been a list without the arguments"

    # 'ans' contains not 'pNumber1' and 'pNumber2' !
    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (n >= 1), "'n' must been int and >= 1"

    lowerCAmelCase__ :  List[Any]							      =					[]  # will be returned.

    for divisor in range(1							,    n + 1			):
        if n % divisor == 0:
            ans.append(UpperCamelCase			)

    # precondition
    assert ans[0] == 1 and ans[len(UpperCamelCase			) - 1] == n, "Error in function getDivisiors(...)"

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (
        number > 1
    ), "'number' must been an int and >= 1"

    lowerCAmelCase__ :  Optional[int]							      =					get_divisors(UpperCamelCase			)

    # precondition
    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and (divisors[0] == 1)
        and (divisors[len(UpperCamelCase			) - 1] == number)
    ), "Error in help-function getDivisiors(...)"

    # summed all divisors up to 'number' (exclusive), hence [:-1]
    return sum(divisors[:-1]			) == number
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and isinstance(UpperCamelCase							,    UpperCamelCase			)
        and (denominator != 0)
    ), "The arguments must been from type int and 'denominator' != 0"

    # build the greatest common divisor of numerator and denominator.
    lowerCAmelCase__ :  int							      =					gcd(abs(UpperCamelCase			)							,    abs(UpperCamelCase			)			)

    # precondition
    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and (numerator % gcd_of_fraction == 0)
        and (denominator % gcd_of_fraction == 0)
    ), "Error in function gcd(...,...)"

    return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (n >= 0), "'n' must been a int and >= 0"

    lowerCAmelCase__ :  str							      =					1  # this will be return.

    for factor in range(1							,    n + 1			):
        ans *= factor

    return ans




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			) and (n >= 0), "'n' must been an int and >= 0"

    lowerCAmelCase__ :  List[Any]							      =					0
    lowerCAmelCase__ :  Any							      =					1
    lowerCAmelCase__ :  Optional[Any]							      =					1  # this will be return

    for _ in range(n - 1			):
        lowerCAmelCase__ :  Dict							      =					ans
        ans += fiba
        lowerCAmelCase__ :  str							      =					tmp

    return ans


