






"""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
#
########################################################################


lowerCAmelCase__    						=			16
lowerCAmelCase__    						=			32





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase = 16       ):
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    AutoTokenizer.from_pretrained('bert-base-cased'       )
				_SCREAMING_SNAKE_CASE   :		int       		=    load_dataset('glue',						'mrpc'       )

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

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

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

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE   :		Any       		=    DataLoader(
				    tokenized_datasets['train'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		int       		=    DataLoader(
				    tokenized_datasets['validation'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )

				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

			lowerCAmelCase__    						=			mocked_dataloaders  # noqa: F811





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

				_SCREAMING_SNAKE_CASE   :		Any       		=    evaluate.load('glue',						'mrpc'       )

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

				# 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).
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model.to(accelerator.device       )

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

				# Instantiate scheduler
				_SCREAMING_SNAKE_CASE   :		str       		=    get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase,						num_warmup_steps=100,						num_training_steps=(len(lowerCamelCase       ) * 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.
				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Tuple       		=    accelerator.prepare(
				    lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )

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

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

								_SCREAMING_SNAKE_CASE   :		int       		=    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:""",						lowerCamelCase       )





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		str       		=    argparse.ArgumentParser(description='Simple example of training script.'       )
				parser.add_argument(
				    '--mixed_precision',						type=lowerCamelCase,						default=lowerCamelCase,						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=lowerCamelCase,						default=1,						help='The number of minibatches to be ran before gradients are accumulated.',						)
				parser.add_argument(
				    '--local_sgd_steps',						type=lowerCamelCase,						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.'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    parser.parse_args()
				_SCREAMING_SNAKE_CASE   :		Dict       		=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowerCamelCase,						lowerCamelCase       )


if __name__ == "__main__":
			main()









"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

lowerCAmelCase__    						=			logging.getLogger(__name__)


@dataclass
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				field(
						    default=128   ,  metadata={
						        'help': (
						            'The maximum total input sequence length after tokenization. Sequences longer '
						            'than this will be truncated, sequences shorter will be padded.'
						        )
						    }   ,  )
						SCREAMING_SNAKE_CASE_:					bool 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}       )
						SCREAMING_SNAKE_CASE_:					bool 					=				field(
						    default=__UpperCAmelCase   ,  metadata={
						        'help': (
						            'Whether to pad all samples to `max_seq_length`. '
						            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
						        )
						    }   ,  )
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={
						        'help': (
						            'For debugging purposes or quicker training, truncate the number of training examples to this '
						            'value if set.'
						        )
						    }   ,  )
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={
						        'help': (
						            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
						            'value if set.'
						        )
						    }   ,  )
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={
						        'help': (
						            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
						            'value if set.'
						        )
						    }   ,  )







@dataclass
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					str 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
						SCREAMING_SNAKE_CASE_:					str 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'}       )
						SCREAMING_SNAKE_CASE_:					Optional[str] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Train language if it is different from the evaluation language.'}       )
						SCREAMING_SNAKE_CASE_:					Optional[str] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
						SCREAMING_SNAKE_CASE_:					Optional[str] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
						SCREAMING_SNAKE_CASE_:					Optional[str] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,  )
						SCREAMING_SNAKE_CASE_:					Optional[bool] 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}   ,  )
						SCREAMING_SNAKE_CASE_:					bool 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}   ,  )
						SCREAMING_SNAKE_CASE_:					str 					=				field(
						    default='main'   ,  metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,  )
						SCREAMING_SNAKE_CASE_:					bool 					=				field(
						    default=__UpperCAmelCase   ,  metadata={
						        'help': (
						            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
						            'with private models).'
						        )
						    }   ,  )
						SCREAMING_SNAKE_CASE_:					bool 					=				field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}   ,  )







def 				lowercase__		(					):
				# 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.

				_SCREAMING_SNAKE_CASE   :		str       		=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
				_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('run_xnli',						lowerCamelCase       )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',						datefmt='%m/%d/%Y %H:%M:%S',						handlers=[logging.StreamHandler(sys.stdout       )],						)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				_SCREAMING_SNAKE_CASE   :		Any       		=    training_args.get_process_log_level()
				logger.setLevel(lowerCamelCase       )
				datasets.utils.logging.set_verbosity(lowerCamelCase       )
				transformers.utils.logging.set_verbosity(lowerCamelCase       )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
				    + f"""distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}"""       )
				logger.info(f"""Training/evaluation parameters {training_args}"""       )

				# Detecting last checkpoint.
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    None
				if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
								_SCREAMING_SNAKE_CASE   :		str       		=    get_last_checkpoint(training_args.output_dir       )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
												raise ValueError(
												    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
												    'Use --overwrite_output_dir to overcome.'       )
								elif last_checkpoint is not None:
												logger.info(
												    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
												    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'       )

    # Set seed before initializing model.
				set_seed(training_args.seed       )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    load_dataset(
												    'xnli',						model_args.language,						split='train',						cache_dir=model_args.cache_dir,						use_auth_token=True if model_args.use_auth_token else None,						)
								else:
												_SCREAMING_SNAKE_CASE   :		int       		=    load_dataset(
												    'xnli',						model_args.train_language,						split='train',						cache_dir=model_args.cache_dir,						use_auth_token=True if model_args.use_auth_token else None,						)
								_SCREAMING_SNAKE_CASE   :		Dict       		=    train_dataset.features['label'].names

				if training_args.do_eval:
								_SCREAMING_SNAKE_CASE   :		Any       		=    load_dataset(
								    'xnli',						model_args.language,						split='validation',						cache_dir=model_args.cache_dir,						use_auth_token=True if model_args.use_auth_token else None,						)
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    eval_dataset.features['label'].names

				if training_args.do_predict:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    load_dataset(
								    'xnli',						model_args.language,						split='test',						cache_dir=model_args.cache_dir,						use_auth_token=True if model_args.use_auth_token else None,						)
								_SCREAMING_SNAKE_CASE   :		int       		=    predict_dataset.features['label'].names

				# Labels
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    len(lowerCamelCase       )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				_SCREAMING_SNAKE_CASE   :		Dict       		=    AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,						num_labels=lowerCamelCase,						idalabel={str(lowerCamelCase       ): label for i, label in enumerate(lowerCamelCase       )},						labelaid={label: i for i, label in enumerate(lowerCamelCase       )},						finetuning_task='xnli',						cache_dir=model_args.cache_dir,						revision=model_args.model_revision,						use_auth_token=True if model_args.use_auth_token else None,						)
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,						do_lower_case=model_args.do_lower_case,						cache_dir=model_args.cache_dir,						use_fast=model_args.use_fast_tokenizer,						revision=model_args.model_revision,						use_auth_token=True if model_args.use_auth_token else None,						)
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path,						from_tf=bool('.ckpt' in model_args.model_name_or_path       ),						config=lowerCamelCase,						cache_dir=model_args.cache_dir,						revision=model_args.model_revision,						use_auth_token=True if model_args.use_auth_token else None,						ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,						)

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    'max_length'
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    False

				def preprocess_function(lowerCamelCase       ):
								# Tokenize the texts
								return tokenizer(
								    examples['premise'],						examples['hypothesis'],						padding=lowerCamelCase,						max_length=data_args.max_seq_length,						truncation=lowerCamelCase,						)

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    min(len(lowerCamelCase       ),						data_args.max_train_samples       )
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    train_dataset.select(range(lowerCamelCase       )       )
								with training_args.main_process_first(desc='train dataset map pre-processing'       ):
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    train_dataset.map(
												    lowerCamelCase,						batched=lowerCamelCase,						load_from_cache_file=not data_args.overwrite_cache,						desc='Running tokenizer on train dataset',						)
								# Log a few random samples from the training set:
								for index in random.sample(range(len(lowerCamelCase       )       ),						3       ):
												logger.info(f"""Sample {index} of the training set: {train_dataset[index]}."""       )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												_SCREAMING_SNAKE_CASE   :		Dict       		=    min(len(lowerCamelCase       ),						data_args.max_eval_samples       )
												_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    eval_dataset.select(range(lowerCamelCase       )       )
								with training_args.main_process_first(desc='validation dataset map pre-processing'       ):
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    eval_dataset.map(
												    lowerCamelCase,						batched=lowerCamelCase,						load_from_cache_file=not data_args.overwrite_cache,						desc='Running tokenizer on validation dataset',						)

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    min(len(lowerCamelCase       ),						data_args.max_predict_samples       )
												_SCREAMING_SNAKE_CASE   :		Dict       		=    predict_dataset.select(range(lowerCamelCase       )       )
								with training_args.main_process_first(desc='prediction dataset map pre-processing'       ):
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    predict_dataset.map(
												    lowerCamelCase,						batched=lowerCamelCase,						load_from_cache_file=not data_args.overwrite_cache,						desc='Running tokenizer on prediction dataset',						)

    # Get the metric function
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    evaluate.load('xnli'       )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Dict       		=    p.predictions[0] if isinstance(p.predictions,						lowerCamelCase       ) else p.predictions
								_SCREAMING_SNAKE_CASE   :		int       		=    np.argmax(lowerCamelCase,						axis=1       )
								return metric.compute(predictions=lowerCamelCase,						references=p.label_ids       )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    default_data_collator
				elif training_args.fpaa:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    DataCollatorWithPadding(lowerCamelCase,						pad_to_multiple_of=8       )
				else:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    None

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Trainer(
				    model=lowerCamelCase,						args=lowerCamelCase,						train_dataset=train_dataset if training_args.do_train else None,						eval_dataset=eval_dataset if training_args.do_eval else None,						compute_metrics=lowerCamelCase,						tokenizer=lowerCamelCase,						data_collator=lowerCamelCase,						)

				# Training
				if training_args.do_train:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None
								if training_args.resume_from_checkpoint is not None:
												_SCREAMING_SNAKE_CASE   :		List[str]       		=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    last_checkpoint
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    trainer.train(resume_from_checkpoint=lowerCamelCase       )
								_SCREAMING_SNAKE_CASE   :		Dict       		=    train_result.metrics
								_SCREAMING_SNAKE_CASE   :		str       		=    (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase       )
								)
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    min(lowerCamelCase,						len(lowerCamelCase       )       )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics('train',						lowerCamelCase       )
								trainer.save_metrics('train',						lowerCamelCase       )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info('*** Evaluate ***'       )
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    trainer.evaluate(eval_dataset=lowerCamelCase       )

								_SCREAMING_SNAKE_CASE   :		Tuple       		=    data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase       )
								_SCREAMING_SNAKE_CASE   :		Any       		=    min(lowerCamelCase,						len(lowerCamelCase       )       )

								trainer.log_metrics('eval',						lowerCamelCase       )
								trainer.save_metrics('eval',						lowerCamelCase       )

				# Prediction
				if training_args.do_predict:
								logger.info('*** Predict ***'       )
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Any       		=    trainer.predict(lowerCamelCase,						metric_key_prefix='predict'       )

								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase       )
								)
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    min(lowerCamelCase,						len(lowerCamelCase       )       )

								trainer.log_metrics('predict',						lowerCamelCase       )
								trainer.save_metrics('predict',						lowerCamelCase       )

								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    np.argmax(lowerCamelCase,						axis=1       )
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    os.path.join(training_args.output_dir,						'predictions.txt'       )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase,						'w'       ) as writer:
																writer.write('index\tprediction\n'       )
																for index, item in enumerate(lowerCamelCase       ):
																				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    label_list[item]
																				writer.write(f"""{index}\t{item}\n"""       )


if __name__ == "__main__":
			main()

