





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
#
########################################################################


_UpperCAmelCase				:       Optional[Any]	   =				16
_UpperCAmelCase				:       Union[str, Any]	   =				32


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase = 16 )			->     Union[str, Any]:
    lowerCamelCase__    :      Union[str, Any]					 =		AutoTokenizer.from_pretrained('bert-base-cased' )
    lowerCamelCase__    :      Union[str, Any]					 =		load_dataset('glue'						,	'mrpc' )

    def tokenize_function(_UpperCAmelCase ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCamelCase__    :      Tuple					 =		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():
        lowerCamelCase__    :      List[Any]					 =		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
    lowerCamelCase__    :      int					 =		tokenized_datasets.rename_column('label'						,	'labels' )

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

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

    # Instantiate dataloaders.
    lowerCamelCase__    :      Tuple					 =		DataLoader(
        tokenized_datasets['train']						,	shuffle=lowercase__						,	collate_fn=lowercase__						,	batch_size=lowercase__ )
    lowerCamelCase__    :      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

    _UpperCAmelCase				:       Optional[int]	   =				mocked_dataloaders  # noqa: F811


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'						,	lowercase__ ) == "1":
        lowerCamelCase__    :      Optional[Any]					 =		2
    # New Code #
    lowerCamelCase__    :      Optional[int]					 =		int(args.gradient_accumulation_steps )
    # Initialize accelerator
    lowerCamelCase__    :      Dict					 =		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
    lowerCamelCase__    :      Tuple					 =		config['''lr''']
    lowerCamelCase__    :      Optional[Any]					 =		int(config['num_epochs'] )
    lowerCamelCase__    :      Union[str, Any]					 =		int(config['seed'] )
    lowerCamelCase__    :      Optional[Any]					 =		int(config['batch_size'] )

    lowerCamelCase__    :      Dict					 =		evaluate.load('glue'						,	'mrpc' )

    set_seed(lowercase__ )
    lowerCamelCase__    :      Optional[int]					 =		get_dataloaders(lowercase__						,	lowercase__ )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__    :      Optional[int]					 =		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).
    lowerCamelCase__    :      int					 =		model.to(accelerator.device )

    # Instantiate optimizer
    lowerCamelCase__    :      Union[str, Any]					 =		AdamW(params=model.parameters()						,	lr=lowercase__ )

    # Instantiate scheduler
    lowerCamelCase__    :      Tuple					 =		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.
    lowerCamelCase__    :      str					 =		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__ ):
                lowerCamelCase__    :      Tuple					 =		model(**lowercase__ )
                lowerCamelCase__    :      str					 =		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():
                lowerCamelCase__    :      Dict					 =		model(**lowercase__ )
            lowerCamelCase__    :      List[str]					 =		outputs.logits.argmax(dim=-1 )
            lowerCamelCase__    :      Optional[Any]					 =		accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=lowercase__						,	references=lowercase__						,	)

        lowerCamelCase__    :      Union[str, Any]					 =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""						,	lowercase__ )


def        SCREAMING_SNAKE_CASE      (       )			->     List[str]:
    lowerCamelCase__    :      str					 =		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.' )
    lowerCamelCase__    :      Any					 =		parser.parse_args()
    lowerCamelCase__    :      Union[str, Any]					 =		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(lowercase__						,	lowercase__ )


if __name__ == "__main__":
    main()









import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__lowerCAmelCase	: Optional[int]  				=logging.get_logger(__name__)

__lowerCAmelCase	: Optional[Any]  				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	: List[str]  				={
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase	: Optional[int]  				={
    'gpt-neox-20b': 2_0_4_8,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		['''input_ids''', '''attention_mask''']


				def __init__(       self			:int			,			lowerCAmelCase__			:Any=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:Dict="<|endoftext|>"			,			lowerCAmelCase__			:Union[str, Any]=False			,			**lowerCAmelCase__			:List[str]			,			)		->			Any:
										super().__init__(
										    lowerCAmelCase__			,			lowerCAmelCase__			,			tokenizer_file=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			bos_token=lowerCAmelCase__			,			eos_token=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase__	) != add_prefix_space:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(lowerCAmelCase__			,			pre_tok_state.pop('''type'''	)	)
																__SCREAMING_SNAKE_CASE						:     str					=		add_prefix_space
																__SCREAMING_SNAKE_CASE						:     Any					=		pre_tok_class(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		add_prefix_space


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._tokenizer.model.save(lowerCAmelCase__			,			name=lowerCAmelCase__	)
										return tuple(lowerCAmelCase__	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"Conversation"	)		->			List[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

										if len(lowerCAmelCase__	) > self.model_max_length:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		input_ids[-self.model_max_length :]
										return input_ids
