'''simple docstring'''


import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


lowercase_      :				List[str]   					=    16
lowercase_      :				Optional[Any]   					=    32







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Accelerator ,  lowercase_    :      DatasetDict ,  lowercase_    :      List[int] ,  lowercase_    :      List[int] ,  lowercase_    :      int = 16						):
						lowercase        =							AutoTokenizer.from_pretrained("""bert-base-cased"""						)
						lowercase        =							DatasetDict(
						    {
						        """train""": dataset["""train"""].select(lowercase_						),
						        """validation""": dataset["""train"""].select(lowercase_						),
						        """test""": dataset["""validation"""],
						    }						)

						def tokenize_function(lowercase_    :      Dict						):
												# max_length=None => use the model max length (it's actually the default)
												lowercase        =							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():
												lowercase        =							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
						lowercase        =							tokenized_datasets.rename_column("""label""" ,  """labels"""						)

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

												return tokenizer.pad(
												    lowercase_ ,  padding="""longest""" ,  max_length=lowercase_ ,  pad_to_multiple_of=lowercase_ ,  return_tensors="""pt""" ,  )

						# Instantiate dataloaders.
						lowercase        =							DataLoader(
						    tokenized_datasets["""train"""] ,  shuffle=lowercase_ ,  collate_fn=lowercase_ ,  batch_size=lowercase_						)
						lowercase        =							DataLoader(
						    tokenized_datasets["""validation"""] ,  shuffle=lowercase_ ,  collate_fn=lowercase_ ,  batch_size=lowercase_						)

						lowercase        =							DataLoader(
						    tokenized_datasets["""test"""] ,  shuffle=lowercase_ ,  collate_fn=lowercase_ ,  batch_size=lowercase_						)

						return train_dataloader, eval_dataloader, test_dataloader







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      Any						):
						# New Code #
						lowercase        =							[]
						# Download the dataset
						lowercase        =							load_dataset("""glue""" ,  """mrpc"""						)
						# Create our splits
						lowercase        =							StratifiedKFold(n_splits=int(args.num_folds						)						)
						# Initialize accelerator
						lowercase        =							Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase        =							config["""lr"""]
						lowercase        =							int(config["""num_epochs"""]						)
						lowercase        =							int(config["""seed"""]						)
						lowercase        =							int(config["""batch_size"""]						)

						lowercase        =							evaluate.load("""glue""" ,  """mrpc"""						)

						# If the batch size is too big we use gradient accumulation
						lowercase        =							1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowercase        =							batch_size // MAX_GPU_BATCH_SIZE
												lowercase        =							MAX_GPU_BATCH_SIZE

						set_seed(lowercase_						)

						# New Code #
						# Create our folds:
						lowercase        =							kfold.split(np.zeros(datasets["""train"""].num_rows						) ,  datasets["""train"""]["""label"""]						)
						lowercase        =							[]
						# Iterate over them
						for i, (train_idxs, valid_idxs) in enumerate(lowercase_						):
												lowercase     ,   lowercase     ,   lowercase        =							get_fold_dataloaders(
												    lowercase_ ,  lowercase_ ,  lowercase_ ,  lowercase_ ,  )
												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowercase        =							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).
												lowercase        =							model.to(accelerator.device						)

												# Instantiate optimizer
												lowercase        =							AdamW(params=model.parameters() ,  lr=lowercase_						)

												# Instantiate scheduler
												lowercase        =							get_linear_schedule_with_warmup(
												    optimizer=lowercase_ ,  num_warmup_steps=100 ,  num_training_steps=(len(lowercase_						) * num_epochs) // gradient_accumulation_steps ,  )

												# 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     ,   lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							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						)
																								lowercase        =							model(**lowercase_						)
																								lowercase        =							outputs.loss
																								lowercase        =							loss / gradient_accumulation_steps
																								accelerator.backward(lowercase_						)
																								if step % gradient_accumulation_steps == 0:
																														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():
																														lowercase        =							model(**lowercase_						)
																								lowercase        =							outputs.logits.argmax(dim=-1						)
																								lowercase     ,   lowercase        =							accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
																								metric.add_batch(
																								    predictions=lowercase_ ,  references=lowercase_ ,  )

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

												# New Code #
												# We also run predictions on the test set at the very end
												lowercase        =							[]
												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():
																								lowercase        =							model(**lowercase_						)
																		lowercase        =							outputs.logits
																		lowercase     ,   lowercase        =							accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
																		fold_predictions.append(predictions.cpu()						)
																		if i == 0:
																								# We need all of the test predictions
																								test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
												test_predictions.append(torch.cat(lowercase_ ,  dim=0						)						)
												# We now need to release all our memory and get rid of the current model, optimizer, etc
												accelerator.free_memory()
						# New Code #
						# Finally we check the accuracy of our folded results:
						lowercase        =							torch.cat(lowercase_ ,  dim=0						)
						lowercase        =							torch.stack(lowercase_ ,  dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
						lowercase        =							metric.compute(predictions=lowercase_ ,  references=lowercase_						)
						accelerator.print("""Average test metrics from all folds:""" ,  lowercase_						)







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							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.""" ,  )
						parser.add_argument("""--cpu""" ,  action="""store_true""" ,  help="""If passed, will train on the CPU."""						)
						# New Code #
						parser.add_argument("""--num_folds""" ,  type=lowercase_ ,  default=3 ,  help="""The number of splits to perform across the dataset"""						)
						lowercase        =							parser.parse_args()
						lowercase        =							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(lowercase_ ,  lowercase_						)


if __name__ == "__main__":
						main()







'''simple docstring'''


from __future__ import annotations

import os
from typing import Any

import requests

lowercase_      :				List[str]   					=    '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_      :				Any   					=    BASE_URL + '''/user'''

# https://github.com/settings/tokens
lowercase_      :				Union[str, Any]   					=    os.environ.get('''USER_TOKEN''', '''''')







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							{
						    """Authorization""": F"""token {auth_token}""",
						    """Accept""": """application/vnd.github.v3+json""",
						}
						return requests.get(lowercase_ ,  headers=lowercase_						).json()


if __name__ == "__main__":  # pragma: no cover
						if USER_TOKEN:
												for key, value in fetch_github_info(USER_TOKEN).items():
																		print(f'''{key}: {value}''')
						else:
												raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')


