


"""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      		=			16
UpperCAmelCase      		=			32






def       lowerCamelCase					(a_						:Accelerator				,				a_						:int = 16)     ->							Optional[int]:
       lowercase		:str       							=				AutoTokenizer.from_pretrained('''bert-base-cased''')
       lowercase		:Optional[int]       							=				load_dataset('''glue'''				,				'''mrpc''')

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

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

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

       # Instantiate dataloaders.
       lowercase		:str       							=				DataLoader(
           tokenized_datasets['''train''']				,				shuffle=a_				,				collate_fn=a_				,				batch_size=a_)
       lowercase		:Any       							=				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

     UpperCAmelCase      		=			mocked_dataloaders  # noqa: F811






def       lowerCamelCase					(a_						:str				,				a_						:Tuple)     ->							List[Any]:
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,				a_) == "1":
              lowercase		:Union[str, Any]       							=				2
       # New Code #
       lowercase		:int       							=				int(args.gradient_accumulation_steps)
       lowercase		:Optional[Any]       							=				int(args.local_sgd_steps)
       # Initialize accelerator
       lowercase		:Optional[Any]       							=				Accelerator(
           cpu=args.cpu				,				mixed_precision=args.mixed_precision				,				gradient_accumulation_steps=a_)
       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
       lowercase		:Dict       							=				config['lr']
       lowercase		:int       							=				int(config['''num_epochs'''])
       lowercase		:List[Any]       							=				int(config['''seed'''])
       lowercase		:Optional[int]       							=				int(config['''batch_size'''])

       lowercase		:str       							=				evaluate.load('''glue'''				,				'''mrpc''')

       set_seed(a_)
       lowercase		:Optional[Any]       							=				get_dataloaders(a_				,				a_)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase		:Union[str, Any]       							=				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).
       lowercase		:Dict       							=				model.to(accelerator.device)

       # Instantiate optimizer
       lowercase		:int       							=				AdamW(params=model.parameters()				,				lr=a_)

       # Instantiate scheduler
       lowercase		:Tuple       							=				get_linear_schedule_with_warmup(
           optimizer=a_				,				num_warmup_steps=100				,				num_training_steps=(len(a_) * 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.
       lowercase		:int       							=				accelerator.prepare(
           a_				,				a_				,				a_				,				a_				,				a_)

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

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

              lowercase		:Optional[int]       							=				metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""				,				a_)






def       lowerCamelCase					()     ->							List[Any]:
       lowercase		:Any       							=				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.'''				,				)
       # New Code #
       parser.add_argument(
           '''--gradient_accumulation_steps'''				,				type=a_				,				default=1				,				help='''The number of minibatches to be ran before gradients are accumulated.'''				,				)
       parser.add_argument(
           '''--local_sgd_steps'''				,				type=a_				,				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.''')
       lowercase		:Any       							=				parser.parse_args()
       lowercase		:List[Any]       							=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(a_				,				a_)


if __name__ == "__main__":
     main()





"""simple docstring"""



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:str				,				a_						:PreTrainedTokenizer				,				a_						:int				,				a_						:Optional[int] = None				,				)     ->							int:
       lowercase		:Dict       							=				{}

       if train_file is not None:
              lowercase		:Union[str, Any]       							=				[train_file]
       if eval_file is not None:
              lowercase		:str       							=				[eval_file]
       if test_file is not None:
              lowercase		:Dict       							=				[test_file]

       lowercase		:Optional[Any]       							=				datasets.load_dataset('''csv'''				,				data_files=a_)
       lowercase		:int       							=				list(ds[list(files.keys())[0]].features.keys())
       lowercase		:Dict       							=				features_name.pop(a_)
       lowercase		:str       							=				list(set(ds[list(files.keys())[0]][label_name]))
       lowercase		:List[str]       							=				{label: i for i, label in enumerate(a_)}
       lowercase		:Any       							=				tokenizer.model_input_names
       lowercase		:Optional[Any]       							=				{}

       if len(a_) == 1:
              for k in files.keys():
                     lowercase		:Dict       							=				ds[k].map(
                         lambda a_: tokenizer.batch_encode_plus(
                             example[features_name[0]]				,				truncation=a_				,				max_length=a_				,				padding='''max_length''')				,				batched=a_				,				)
       elif len(a_) == 2:
              for k in files.keys():
                     lowercase		:Optional[Any]       							=				ds[k].map(
                         lambda a_: tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])				,				truncation=a_				,				max_length=a_				,				padding='''max_length'''				,				)				,				batched=a_				,				)

       def gen_train():
              for ex in transformed_ds[datasets.Split.TRAIN]:
                     lowercase		:Any       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:List[str]       							=				labelaid[ex[label_name]]
                     yield (d, label)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     lowercase		:Optional[Any]       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:Tuple       							=				labelaid[ex[label_name]]
                     yield (d, label)

       def gen_test():
              for ex in transformed_ds[datasets.Split.TEST]:
                     lowercase		:Dict       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:Optional[int]       							=				labelaid[ex[label_name]]
                     yield (d, label)

       lowercase		:List[str]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
           if datasets.Split.TRAIN in transformed_ds
           else None
       )

       if train_ds is not None:
              lowercase		:Union[str, Any]       							=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

       lowercase		:Optional[int]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
           if datasets.Split.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              lowercase		:List[Any]       							=				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

       lowercase		:Optional[Any]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
           if datasets.Split.TEST in transformed_ds
           else None
       )

       if test_ds is not None:
              lowercase		:int       							=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

       return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase      		=			logging.getLogger(__name__)
@dataclass
class 					__magic_name__		:
       __A			:      int        =   field(metadata={"help": "Which column contains the label"}     )
       __A			:      str        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the training file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the development file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the test file"}     )
       __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=__UpperCAmelCase			,						metadata={"help": "Overwrite the cached training and evaluation sets"}     )







@dataclass
class 					__magic_name__		:
       __A			:      str        =   field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
       __A			:      bool        =   field(default=__UpperCAmelCase			,						metadata={"help": "Set this flag to use fast tokenization."}     )
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,						)







def       lowerCamelCase					()     ->							Dict:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       lowercase		:Dict       							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
       lowercase							,  lowercase							,  lowercase		:Union[str, Any]       							=				parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir)
           and os.listdir(training_args.output_dir)
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  ''' --overwrite_output_dir to overcome.''')

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,				datefmt='''%m/%d/%Y %H:%M:%S'''				,				level=logging.INFO				,				)
       logger.info(
           F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, """
           F"""16-bits training: {training_args.fpaa}""")
       logger.info(F"""Training/evaluation parameters {training_args}""")

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       lowercase		:Optional[int]       							=				AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,				cache_dir=model_args.cache_dir				,				)

       lowercase							,  lowercase							,  lowercase							,  lowercase		:int       							=				get_tfds(
           train_file=data_args.train_file				,				eval_file=data_args.dev_file				,				test_file=data_args.test_file				,				tokenizer=a_				,				label_column_id=data_args.label_column_id				,				max_seq_length=data_args.max_seq_length				,				)

       lowercase		:Optional[int]       							=				AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,				num_labels=len(a_)				,				labelaid=a_				,				idalabel={id: label for label, id in labelaid.items()}				,				finetuning_task='''text-classification'''				,				cache_dir=model_args.cache_dir				,				)

       with training_args.strategy.scope():
              lowercase		:int       							=				TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path				,				from_pt=bool('''.bin''' in model_args.model_name_or_path)				,				config=a_				,				cache_dir=model_args.cache_dir				,				)

       def compute_metrics(a_						:EvalPrediction) -> Dict:
              lowercase		:Tuple       							=				np.argmax(p.predictions				,				axis=1)

              return {"acc": (preds == p.label_ids).mean()}

       # Initialize our Trainer
       lowercase		:Any       							=				TFTrainer(
           model=a_				,				args=a_				,				train_dataset=a_				,				eval_dataset=a_				,				compute_metrics=a_				,				)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir)

       # Evaluation
       lowercase		:Union[str, Any]       							=				{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***''')
              lowercase		:str       							=				trainer.evaluate()
              lowercase		:int       							=				os.path.join(training_args.output_dir				,				'''eval_results.txt''')

              with open(a_				,				'''w''') as writer:
                     logger.info('''***** Eval results *****''')

                     for key, value in result.items():
                            logger.info(F"""  {key} = {value}""")
                            writer.write(F"""{key} = {value}\n""")

                     results.update(a_)

       return results


if __name__ == "__main__":
 main()

