


import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

SCREAMING_SNAKE_CASE      :						List[str]    =		pathlib.Path.cwd().joinpath("project_euler")
SCREAMING_SNAKE_CASE      :						Any    =		pathlib.Path.cwd().joinpath(
    "scripts", "project_euler_answers.json"
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
		SCREAMING_SNAKE_CASE      :						Union[str, Any]    =		json.load(file_handle)






def   __lowerCamelCase	(       lowerCamelCase__		:	pathlib.Path			):





			'''simple docstring'''




			lowerCamelCase								=     importlib.util.spec_from_file_location(file_path.name     ,       str(lowercase__			)			)
			lowerCamelCase								=     importlib.util.module_from_spec(lowercase__			)  # type: ignore
			spec.loader.exec_module(lowercase__			)  # type: ignore
			return module






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     []
			for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
						if problem_dir_path.is_file() or problem_dir_path.name.startswith("""_"""			):
									continue
						for file_path in problem_dir_path.iterdir():
									if file_path.suffix != ".py" or file_path.name.startswith(("""_""", """test""")			):
												continue
									solution_file_paths.append(lowercase__			)
			return solution_file_paths






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			with open(os.environ["""GITHUB_EVENT_PATH"""]			) as file:
						lowerCamelCase								=     json.load(lowercase__			)
			return event["pull_request"]["url"] + "/files"






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     []
			lowerCamelCase								=     {
			    "Accept": "application/vnd.github.v3+json",
			    "Authorization": "token " + os.environ["GITHUB_TOKEN"],
			}
			lowerCamelCase								=     requests.get(get_files_url()     ,       headers=lowercase__			).json()
			for file in files:
						lowerCamelCase								=     pathlib.Path.cwd().joinpath(file["""filename"""]			)
						if (
						    filepath.suffix != ".py"
						    or filepath.name.startswith(("""_""", """test""")			)
						    or not filepath.name.startswith("""sol"""			)
						):
									continue
						solution_file_paths.append(lowercase__			)
			return solution_file_paths






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			if os.environ.get("""CI"""			) and os.environ.get("""GITHUB_EVENT_NAME"""			) == "pull_request":
						# Return only if there are any, otherwise default to all solutions
						if filepaths := added_solution_file_path():
									return filepaths
			return all_solution_file_paths()






@pytest.mark.parametrize(
    """solution_path"""     ,       collect_solution_file_paths()     ,       ids=lambda lowerCamelCase__			: f'{path.parent.name}/{path.name}'     ,       )
def   __lowerCamelCase	(       lowerCamelCase__		:	pathlib.Path			):





			'''simple docstring'''




			lowerCamelCase								=     solution_path.parent.name[8:].zfill(3			)
			lowerCamelCase								=     PROBLEM_ANSWERS[problem_number]
			lowerCamelCase								=     convert_path_to_module(lowercase__			)
			lowerCamelCase								=     str(solution_module.solution()			)  # type: ignore
			lowerCamelCase								=     hashlib.shaaaa(answer.encode()			).hexdigest()
			assert (
			    answer == expected
			), f'Expected solution to {problem_number} to have hash {expected}, got {answer}'





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,
# specifically showcasing the checkpointing capability,
# 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
#
########################################################################

UpperCAmelCase      :						str    =		16
UpperCAmelCase      :						Union[str, Any]    =		32






def   __lowerCamelCase	(       lowerCamelCase__		:	Accelerator     ,       lowerCamelCase__		:	int = 16			):





			'''simple docstring'''




			lowerCamelCase								=     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
			lowerCamelCase								=     load_dataset("""glue"""     ,       """mrpc"""			)

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

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

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

			# Instantiate dataloaders.
			lowerCamelCase								=     DataLoader(
			    tokenized_datasets["""train"""]     ,       shuffle=lowerCamelCase__     ,       collate_fn=lowerCamelCase__     ,       batch_size=lowerCamelCase__			)
			lowerCamelCase								=     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

		UpperCAmelCase      :						Tuple    =		mocked_dataloaders  # noqa: F811






def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	Optional[int]			):





			'''simple docstring'''




			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,       lowerCamelCase__			) == "1":
						lowerCamelCase								=     2
			# Initialize accelerator
			lowerCamelCase								=     Accelerator(cpu=args.cpu     ,       mixed_precision=args.mixed_precision			)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase								=     config["""lr"""]
			lowerCamelCase								=     int(config["""num_epochs"""]			)
			lowerCamelCase								=     int(config["""seed"""]			)
			lowerCamelCase								=     int(config["""batch_size"""]			)

			# New Code #
			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps     ,       """isdigit"""			):
						if args.checkpointing_steps == "epoch":
									lowerCamelCase								=     args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									lowerCamelCase								=     int(args.checkpointing_steps			)
						else:
									raise ValueError(
									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
			else:
						lowerCamelCase								=     None

			set_seed(lowerCamelCase__			)

			lowerCamelCase		,		lowerCamelCase								=     get_dataloaders(lowerCamelCase__     ,       lowerCamelCase__			)
			lowerCamelCase								=     evaluate.load("""glue"""     ,       """mrpc"""			)

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

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

			# Instantiate optimizer
			lowerCamelCase								=     AdamW(params=model.parameters()     ,       lr=lowerCamelCase__			)

			# Instantiate scheduler
			lowerCamelCase								=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(lowerCamelCase__			) * 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.
			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     accelerator.prepare(
			    lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)

			# New Code #
			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase								=     0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase								=     0

			# We need to load the checkpoint back in before training here with `load_state`
			# The total number of epochs is adjusted based on where the state is being loaded from,
			# as we assume continuation of the same training script
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'			)
									accelerator.load_state(args.resume_from_checkpoint			)
									lowerCamelCase								=     os.path.basename(args.resume_from_checkpoint			)
						else:
									# Get the most recent checkpoint
									lowerCamelCase								=     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
									dirs.sort(key=os.path.getctime			)
									lowerCamelCase								=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						lowerCamelCase								=     os.path.splitext(lowerCamelCase__			)[0]

						if "epoch" in training_difference:
									lowerCamelCase								=     int(training_difference.replace("""epoch_"""     ,       """"""			)			) + 1
									lowerCamelCase								=     None
						else:
									lowerCamelCase								=     int(training_difference.replace("""step_"""     ,       """"""			)			)
									lowerCamelCase								=     resume_step // len(lowerCamelCase__			)
									resume_step -= starting_epoch * len(lowerCamelCase__			)

    # Now we train the model
			for epoch in range(lowerCamelCase__     ,       lowerCamelCase__			):
						model.train()
						# New Code #
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									lowerCamelCase								=     accelerator.skip_first_batches(lowerCamelCase__     ,       lowerCamelCase__			)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									lowerCamelCase								=     train_dataloader
						for step, batch in enumerate(lowerCamelCase__			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device			)
									lowerCamelCase								=     model(**lowerCamelCase__			)
									lowerCamelCase								=     outputs.loss
									lowerCamelCase								=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase__			)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
									# New Code #
									overall_step += 1

									# New Code #
									# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
									# These are saved to folders named `step_{overall_step}`
									# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
									# If mixed precision was used, will also save a "scalar.bin" file
									if isinstance(lowerCamelCase__     ,       lowerCamelCase__			):
												lowerCamelCase								=     f'step_{overall_step}'
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
															accelerator.save_state(lowerCamelCase__			)

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

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

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `epoch_{epoch}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if checkpointing_steps == "epoch":
									lowerCamelCase								=     f'epoch_{epoch}'
									if args.output_dir is not None:
												lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
									accelerator.save_state(lowerCamelCase__			)






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     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."""     ,       )
			parser.add_argument("""--cpu"""     ,       action="""store_true"""     ,       help="""If passed, will train on the CPU."""			)
			parser.add_argument(
			    """--checkpointing_steps"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""     ,       )
			parser.add_argument(
			    """--output_dir"""     ,       type=lowerCamelCase__     ,       default="""."""     ,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""     ,       )
			parser.add_argument(
			    """--resume_from_checkpoint"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""If the training should continue from a checkpoint folder."""     ,       )
			lowerCamelCase								=     parser.parse_args()
			lowerCamelCase								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase__     ,       lowerCamelCase__			)


if __name__ == "__main__":
		main()

