

'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


_UpperCamelCase						:  str							       =				16
_UpperCamelCase						:  Dict							       =				32


def       snake_case   (	snake_case      : Accelerator					,		snake_case      : int = 16			) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							AutoTokenizer.from_pretrained('bert-base-cased'			)
    lowerCAmelCase 						=							load_dataset('glue'					,		'mrpc'			)

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

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

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

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

    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

     _UpperCamelCase						:  str							       =				mocked_dataloaders  # noqa: F811


def       snake_case   (	snake_case      : Any					,		snake_case      : Optional[int]			) ->  Union[str, Any]:


    """simple docstring"""


    if os.environ.get('TESTING_MOCKED_DATALOADERS'					,		snake_case			) == "1":
        lowerCAmelCase 						=							2
    # New Code #
    lowerCAmelCase 						=							int(args.gradient_accumulation_steps			)
    lowerCAmelCase 						=							int(args.local_sgd_steps			)
    # Initialize accelerator
    lowerCAmelCase 						=							Accelerator(
        cpu=args.cpu					,		mixed_precision=args.mixed_precision					,		gradient_accumulation_steps=snake_case			)
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'			)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCAmelCase 						=							config['''lr''']
    lowerCAmelCase 						=							int(config['num_epochs']			)
    lowerCAmelCase 						=							int(config['seed']			)
    lowerCAmelCase 						=							int(config['batch_size']			)

    lowerCAmelCase 						=							evaluate.load('glue'					,		'mrpc'			)

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

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

    # Instantiate optimizer
    lowerCAmelCase 						=							AdamW(params=model.parameters()					,		lr=snake_case			)

    # Instantiate scheduler
    lowerCAmelCase 						=							get_linear_schedule_with_warmup(
        optimizer=snake_case					,		num_warmup_steps=100					,		num_training_steps=(len(snake_case			) * 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.
    lowerCAmelCase 						=							accelerator.prepare(
        snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)

    # Now we train the model
    for epoch in range(snake_case			):
        model.train()
        with LocalSGD(
            accelerator=snake_case					,		model=snake_case					,		local_sgd_steps=snake_case					,		enabled=local_sgd_steps is not None			) as local_sgd:
            for step, batch in enumerate(snake_case			):
                # 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(snake_case			):
                    lowerCAmelCase 						=							model(**snake_case			)
                    lowerCAmelCase 						=							output.loss
                    accelerator.backward(snake_case			)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

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


def       snake_case   (	) ->  Optional[int]:


    """simple docstring"""


    lowerCAmelCase 						=							argparse.ArgumentParser(description='Simple example of training script.'			)
    parser.add_argument(
        '--mixed_precision'					,		type=snake_case					,		default=snake_case					,		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=snake_case					,		default=1					,		help='The number of minibatches to be ran before gradients are accumulated.'					,		)
    parser.add_argument(
        '--local_sgd_steps'					,		type=snake_case					,		default=8					,		help='Number of local SGD steps or None to disable local SGD'			)
    parser.add_argument('--cpu'					,		action='store_true'					,		help='If passed, will train on the CPU.'			)
    lowerCAmelCase 						=							parser.parse_args()
    lowerCAmelCase 						=							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(snake_case					,		snake_case			)


if __name__ == "__main__":
     main()












"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							__lowerCamelCase   :   List[Any]       ='''f32le'''
							__lowerCamelCase   :   Optional[int]       =[
							    '''ffmpeg''',
							    '''-i''',
							    '''pipe:0''',
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdin=subprocess.PIPE     ,    stdout=subprocess.PIPE ) as ffmpeg_process:
																					__lowerCamelCase   :   Union[str, Any]       =ffmpeg_process.communicate(SCREAMING_SNAKE_CASE )
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
							__lowerCamelCase   :   Any       =output_stream[0]
							__lowerCamelCase   :   Tuple       =np.frombuffer(SCREAMING_SNAKE_CASE     ,    np.floataa )
							if audio.shape[0] == 0:
														raise ValueError('''Malformed soundfile''' )
							return audio



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   List[Any]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   List[Any]       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							__lowerCamelCase   :   Union[str, Any]       =platform.system()
							if system == "Linux":
														__lowerCamelCase   :   Tuple       ='''alsa'''
														__lowerCamelCase   :   int       ='''default'''
							elif system == "Darwin":
														__lowerCamelCase   :   Any       ='''avfoundation'''
														__lowerCamelCase   :   List[str]       =''':0'''
							elif system == "Windows":
														__lowerCamelCase   :   Union[str, Any]       ='''dshow'''
														__lowerCamelCase   :   Dict       ='''default'''

							__lowerCamelCase   :   Any       =[
							    '''ffmpeg''',
							    '''-f''',
							    format_,
							    '''-i''',
							    input_,
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-fflags''',
							    '''nobuffer''',
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]
							__lowerCamelCase   :   Tuple       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							__lowerCamelCase   :   int       =_ffmpeg_stream(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							for item in iterator:
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

							__lowerCamelCase   :   Union[str, Any]       =ffmpeg_microphone(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    format_for_conversion=SCREAMING_SNAKE_CASE )
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   int       =np.intaa
														__lowerCamelCase   :   Optional[int]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   Dict       =np.floataa
														__lowerCamelCase   :   Tuple       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

							__lowerCamelCase   :   List[str]       =int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
							__lowerCamelCase   :   int       =int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
							__lowerCamelCase   :   Optional[int]       =datetime.datetime.now()
							__lowerCamelCase   :   List[Any]       =datetime.timedelta(seconds=SCREAMING_SNAKE_CASE )
							for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    stride=(stride_left, stride_right)     ,    stream=SCREAMING_SNAKE_CASE ):
														# Put everything back in numpy scale
														__lowerCamelCase   :   Optional[int]       =np.frombuffer(item['''raw''']     ,    dtype=SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   Dict       =(
														    item['''stride'''][0] // size_of_sample,
														    item['''stride'''][1] // size_of_sample,
														)
														__lowerCamelCase   :   Optional[Any]       =sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 10 * delta:
																					# We're late !! SKIP
																					continue
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =B''''''
							__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
							__lowerCamelCase   :   List[str]       =0
							for raw in iterator:
														acc += raw
														if stream and len(SCREAMING_SNAKE_CASE ) < chunk_len:
																					__lowerCamelCase   :   List[str]       =(_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(SCREAMING_SNAKE_CASE ) >= chunk_len:
																												# We are flushing the accumulator
																												__lowerCamelCase   :   Optional[Any]       =(_stride_left, stride_right)
																												__lowerCamelCase   :   str       ={'''raw''': acc[:chunk_len], '''stride''': stride}
																												if stream:
																																			__lowerCamelCase   :   Optional[Any]       =False
																												yield item
																												__lowerCamelCase   :   Dict       =stride_left
																												__lowerCamelCase   :   str       =acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(SCREAMING_SNAKE_CASE ) > stride_left:
														__lowerCamelCase   :   List[str]       ={'''raw''': acc, '''stride''': (_stride_left, 0)}
														if stream:
																					__lowerCamelCase   :   Tuple       =False
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdout=subprocess.PIPE     ,    bufsize=SCREAMING_SNAKE_CASE ) as ffmpeg_process:
																					while True:
																												__lowerCamelCase   :   Dict       =ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE )
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error




