

'''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


########################################################################
# 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
#
########################################################################

_lowerCAmelCase    			=						16
_lowerCAmelCase    			=						32




def 						UpperCamelCase       ( a		,						a = 16 )      ->	Optional[Any]:






     '''simple docstring'''
     __magic_name__						   =				AutoTokenizer.from_pretrained('''bert-base-cased''' )
     __magic_name__						   =				load_dataset('''glue'''		,						'''mrpc''' )

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

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

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

     # Instantiate dataloaders.
     __magic_name__						   =				DataLoader(
         tokenized_datasets['''train''']		,						shuffle=_SCREAMING_SNAKE_CASE		,						collate_fn=_SCREAMING_SNAKE_CASE		,						batch_size=_SCREAMING_SNAKE_CASE )
     __magic_name__						   =				DataLoader(
         tokenized_datasets['''validation''']		,						shuffle=_SCREAMING_SNAKE_CASE		,						collate_fn=_SCREAMING_SNAKE_CASE		,						batch_size=_SCREAMING_SNAKE_CASE )

     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 						UpperCamelCase       ( a		,						a )      ->	List[str]:






     '''simple docstring'''
     # For testing only
     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,						_SCREAMING_SNAKE_CASE ) == "1":
          __magic_name__						   =				2
     # Initialize accelerator
     __magic_name__						   =				Accelerator(cpu=args.cpu		,						mixed_precision=args.mixed_precision )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __magic_name__						   =				config['''lr''']
     __magic_name__						   =				int(config['''num_epochs'''] )
     __magic_name__						   =				int(config['''seed'''] )
     __magic_name__						   =				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":
               __magic_name__						   =				args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __magic_name__						   =				int(args.checkpointing_steps )
          else:
               raise ValueError(
                   F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
     else:
          __magic_name__						   =				None

     set_seed(_SCREAMING_SNAKE_CASE )

     __magic_name__   ,  __magic_name__						   =				get_dataloaders(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE )
     __magic_name__						   =				evaluate.load('''glue'''		,						'''mrpc''' )

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

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

     # 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).
     __magic_name__						   =				model.to(accelerator.device )

     # Instantiate optimizer
     __magic_name__						   =				AdamW(params=model.parameters()		,						lr=_SCREAMING_SNAKE_CASE )

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

     # New Code #
     # We need to keep track of how many total steps we have iterated over
     __magic_name__						   =				0
     # We also need to keep track of the stating epoch so files are named properly
     __magic_name__						   =				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 )
               __magic_name__						   =				os.path.basename(args.resume_from_checkpoint )
          else:
               # Get the most recent checkpoint
               __magic_name__						   =				[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
               dirs.sort(key=os.path.getctime )
               __magic_name__						   =				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __magic_name__						   =				os.path.splitext(_SCREAMING_SNAKE_CASE )[0]

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

    # Now we train the model
     for epoch in range(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE ):
          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
               __magic_name__						   =				accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               __magic_name__						   =				train_dataloader
          for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device )
               __magic_name__						   =				model(**_SCREAMING_SNAKE_CASE )
               __magic_name__						   =				outputs.loss
               __magic_name__						   =				loss / gradient_accumulation_steps
               accelerator.backward(_SCREAMING_SNAKE_CASE )
               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(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE ):
                    __magic_name__						   =				F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __magic_name__						   =				os.path.join(args.output_dir		,						_SCREAMING_SNAKE_CASE )
                         accelerator.save_state(_SCREAMING_SNAKE_CASE )

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

          __magic_name__						   =				metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''		,						_SCREAMING_SNAKE_CASE )

          # 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":
               __magic_name__						   =				F'''epoch_{epoch}'''
               if args.output_dir is not None:
                    __magic_name__						   =				os.path.join(args.output_dir		,						_SCREAMING_SNAKE_CASE )
               accelerator.save_state(_SCREAMING_SNAKE_CASE )




def 						UpperCamelCase       ( )      ->	Optional[Any]:






     '''simple docstring'''
     __magic_name__						   =				argparse.ArgumentParser(description='''Simple example of training script.''' )
     parser.add_argument(
         '''--mixed_precision'''		,						type=_SCREAMING_SNAKE_CASE		,						default=_SCREAMING_SNAKE_CASE		,						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=_SCREAMING_SNAKE_CASE		,						default=_SCREAMING_SNAKE_CASE		,						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=_SCREAMING_SNAKE_CASE		,						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=_SCREAMING_SNAKE_CASE		,						default=_SCREAMING_SNAKE_CASE		,						help='''If the training should continue from a checkpoint folder.'''		,						)
     __magic_name__						   =				parser.parse_args()
     __magic_name__						   =				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
      main()











'''simple docstring'''

from __future__ import annotations

from pathlib import Path






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						tuple[int, int]							,		_SCREAMING_SNAKE_CASE     :						tuple[int, int]							)			->						int:







	"""simple docstring"""
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							)			->						bool:







	"""simple docstring"""
	lowerCAmelCase    =	(xa, ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	-vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	+vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	return a > 0 and b > 0 and a + b < 1






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str = "p102_triangles.txt"							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	Path(_SCREAMING_SNAKE_CASE							).parent.joinpath(_SCREAMING_SNAKE_CASE							).read_text(encoding="""utf-8"""							)

	lowerCAmelCase    =	[]
	for line in data.strip().split("""\n"""							):
		triangles.append([int(_SCREAMING_SNAKE_CASE							) for number in line.split(""","""							)]							)

	lowerCAmelCase    =	0
	lowerCAmelCase    =	42

	for triangle in triangles:
		ret += contains_origin(*_SCREAMING_SNAKE_CASE							)

	return ret


if __name__ == "__main__":
				print(F'''{solution() = }''')