
'''simple docstring'''
import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowerCAmelCase					:    Dict           = 16
lowerCAmelCase					:    List[str]           = 32







def 		A_(     A						:		Accelerator		,     A						:		int = 16):
							UpperCamelCase   			=     AutoTokenizer.from_pretrained('bert-base-cased')
							UpperCamelCase   			=     load_dataset('glue'		,     'mrpc')

							def tokenize_function(A						:		str):
														# max_length=None => use the model max length (it's actually the default)
														UpperCamelCase   			=     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():
														UpperCamelCase   			=     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
							UpperCamelCase   			=     tokenized_datasets.rename_column('label'		,     'labels')

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

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

							# Instantiate dataloaders.
							UpperCamelCase   			=     DataLoader(
							    tokenized_datasets['train']		,     shuffle=A		,     collate_fn=A		,     batch_size=A)
							UpperCamelCase   			=     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

						lowerCAmelCase					:    Dict           = mocked_dataloaders  # noqa: F811







def 		A_(     A						:		Tuple		,     A						:		Union[str, Any]):
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'		,     A) == "1":
														UpperCamelCase   			=     2
							# Initialize accelerator
							UpperCamelCase   			=     Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							UpperCamelCase   			=     config['lr']
							UpperCamelCase   			=     int(config['num_epochs'])
							UpperCamelCase   			=     int(config['seed'])
							UpperCamelCase   			=     int(config['batch_size'])

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

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=A)
							def inner_training_loop(A						:		Any):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(A)

														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														UpperCamelCase   			=     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).
														UpperCamelCase   			=     model.to(accelerator.device)

														# Instantiate optimizer
														UpperCamelCase   			=     AdamW(params=model.parameters()		,     lr=A)
														UpperCamelCase			,	UpperCamelCase   			=     get_dataloaders(A		,     A)

														# Instantiate scheduler
														UpperCamelCase   			=     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.
														UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     accelerator.prepare(
														    A		,     A		,     A		,     A		,     A)

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

																					UpperCamelCase   			=     metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(f'''epoch {epoch}:'''		,     A)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
							inner_training_loop()







def 		A_(     ):
							UpperCamelCase   			=     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.')
							UpperCamelCase   			=     parser.parse_args()
							UpperCamelCase   			=     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(A		,     A)


if __name__ == "__main__":
						main()









import pytest


lowercase_													=      '''__dummy_dataset1__'''

lowercase_													=      '''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Any:
					return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Union[str, Any]:
					return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str						)	->    List[str]:
					__lowerCAmelCase						 =dataset_loading_script_name
					__lowerCAmelCase						 =tmp_path / """datasets""" / script_name
					script_dir.mkdir(parents=__lowerCamelCase						)
					__lowerCAmelCase						 =script_dir / f"""{script_name}.py"""
					with open(__lowerCamelCase					, """w"""						) as f:
										f.write(__lowerCamelCase						)
					return str(__lowerCamelCase						)




