






'''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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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		:		int								=			32
def __UpperCAmelCase (			A			:	Accelerator	,   A			:	int = 1_6						)  -> Tuple:
			UpperCAmelCase_     :   Optional[int]       =   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
			UpperCAmelCase_     :   int       =   load_dataset('''glue'''	,   '''mrpc'''						)

			def tokenize_function(A			:	Dict						):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_     :   Optional[int]       =   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_     :   Any       =   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_     :   Union[str, Any]       =   tokenized_datasets.rename_column('''label'''	,   '''labels'''						)

			def collate_fn(A			:	Tuple						):
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									UpperCAmelCase_     :   Any       =   1_6
						elif accelerator.mixed_precision != "no":
									UpperCAmelCase_     :   int       =   8
						else:
									UpperCAmelCase_     :   int       =   None

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

			# Instantiate dataloaders.
			UpperCAmelCase_     :   int       =   DataLoader(
			    tokenized_datasets['''train''']	,   shuffle=A	,   collate_fn=A	,   batch_size=A						)
			UpperCAmelCase_     :   Optional[Any]       =   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

						_UpperCamelCase		:		Dict								=			mocked_dataloaders  # noqa: F811
def __UpperCAmelCase (			A			:	Optional[int]	,   A			:	Tuple						)  -> Any:
			# For testing only
			if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,   A						) == "1":
						UpperCAmelCase_     :   Tuple       =   2
			# Initialize accelerator
			UpperCAmelCase_     :   Optional[int]       =   Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision						)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_     :   int       =   config['''lr''']
			UpperCAmelCase_     :   List[Any]       =   int(config['''num_epochs''']						)
			UpperCAmelCase_     :   Optional[Any]       =   int(config['''seed''']						)
			UpperCAmelCase_     :   Tuple       =   int(config['''batch_size''']						)

			UpperCAmelCase_     :   Union[str, Any]       =   evaluate.load('''glue'''	,   '''mrpc'''						)

			# New Code #
			# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
			# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
			# half each time. From this, we can calculate the number of gradient accumulation steps needed
			# and modify the Accelerator object as a result
			@find_executable_batch_size(starting_batch_size=int(A						)						)
			def inner_training_loop(A			:	List[str]						):
						# Since we need to modify the outside accelerator object, we need to bring it
						# to the local scope
						nonlocal accelerator

						# We can calculate the number of gradient accumulation steps based on the current
						# batch size vs the starting batch size
						UpperCAmelCase_     :   Tuple       =   observed_batch_size // batch_size

						# And then set it in the Accelerator directly:
						UpperCAmelCase_     :   int       =   num_gradient_accumulation_steps

						# Next we need to free all of the stored model references in the Accelerator each time
						accelerator.free_memory()

						# And set the seed so our results are reproducable each reset
						set_seed(A						)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_     :   Any       =   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_     :   Optional[int]       =   model.to(accelerator.device						)

						# Instantiate optimizer
						UpperCAmelCase_     :   int       =   AdamW(params=model.parameters()	,   lr=A						)
						UpperCAmelCase_  ,     UpperCAmelCase_     :   Dict       =   get_dataloaders(A	,   A						)

						# Instantiate scheduler
						UpperCAmelCase_     :   str       =   get_linear_schedule_with_warmup(
						    optimizer=A	,   num_warmup_steps=1_0_0	,   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_     :   Optional[int]       =   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						):
												# And perform gradient accumulation
												with accelerator.accumulate(A						):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device						)
															UpperCAmelCase_     :   List[Any]       =   model(**A						)
															UpperCAmelCase_     :   Optional[Any]       =   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_     :   List[str]       =   model(**A						)
												UpperCAmelCase_     :   List[str]       =   outputs.logits.argmax(dim=-1						)
												UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
												metric.add_batch(
												    predictions=A	,   references=A	,   )

									UpperCAmelCase_     :   Tuple       =   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 __UpperCAmelCase (			)  -> Union[str, Any]:
			UpperCAmelCase_     :   int       =   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_     :   Any       =   parser.parse_args()
			# New Code #
			# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
			UpperCAmelCase_     :   int       =   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
			training_function(A	,   A						)


if __name__ == "__main__":
						main()










'''simple docstring'''





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize


class 				snake_case__     (			UpperCamelCase):
					a_       =			1
					a_       =			2
					a_       =			3




					def __repr__(       self       :						str     )  ->    Optional[int]:
								return F"{self.__class__.__name__}.{self.name}"






def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	int	,   A			:	int						)  -> float:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   point
			return degrees(atana(y - miny	,   x - minx						)						)
def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	tuple[int, int]	,   A			:	tuple[int, int]						)  -> Direction:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   starting
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[Any]       =   via
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   target
			UpperCAmelCase_     :   Optional[int]       =   degrees(atana(ya - ya	,   xa - xa						)						)
			via_angle %= 3_6_0
			UpperCAmelCase_     :   Dict       =   degrees(atana(ya - ya	,   xa - xa						)						)
			target_angle %= 3_6_0
			# t-
			#  \ \
			#   \ v
			#    \|
			#     s
			# via_angle is always lower than target_angle, if direction is left.
			# If they are same, it means they are on a same line of convex hull.
			if target_angle > via_angle:
						return Direction.left
			elif target_angle == via_angle:
						return Direction.straight
			else:
						return Direction.right







def __UpperCAmelCase (			A			:	list[tuple[int, int]]						)  -> list[tuple[int, int]]:

			if len(A						) <= 2:
						# There is no convex hull
						raise ValueError('''graham_scan: argument must contain more than 3 points.'''						)
			if len(A						) == 3:
						return points
			# find the lowest and the most left point
			UpperCAmelCase_     :   List[str]       =   0
			UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   maxsize, maxsize
			for i, point in enumerate(A						):
						UpperCAmelCase_     :   str       =   point[0]
						UpperCAmelCase_     :   List[Any]       =   point[1]
						if y < miny:
									UpperCAmelCase_     :   int       =   y
									UpperCAmelCase_     :   Dict       =   x
									UpperCAmelCase_     :   Any       =   i
						if y == miny and x < minx:
									UpperCAmelCase_     :   str       =   x
									UpperCAmelCase_     :   Optional[int]       =   i

    # remove the lowest and the most left point from points for preparing for sort
			points.pop(A						)

			UpperCAmelCase_     :   Optional[Any]       =   sorted(A	,   key=lambda A						: angle_comparer(A	,   A	,   A						)						)
			# This insert actually costs complexity,
			# and you should instead add (minx, miny) into stack later.
			# I'm using insert just for easy understanding.
			sorted_points.insert(0	,   (minx, miny)						)

			UpperCAmelCase_     :   deque[tuple[int, int]]       =   deque()
			stack.append(sorted_points[0]						)
			stack.append(sorted_points[1]						)
			stack.append(sorted_points[2]						)
			# In any ways, the first 3 points line are towards left.
			# Because we sort them the angle from minx, miny.
			UpperCAmelCase_     :   Any       =   Direction.left

			for i in range(3	,   len(A						)						):
						while True:
									UpperCAmelCase_     :   Any       =   stack[-2]
									UpperCAmelCase_     :   List[Any]       =   stack[-1]
									UpperCAmelCase_     :   Any       =   sorted_points[i]
									UpperCAmelCase_     :   Tuple       =   check_direction(A	,   A	,   A						)

									if next_direction == Direction.left:
												UpperCAmelCase_     :   Optional[Any]       =   Direction.left
												break
									if next_direction == Direction.straight:
												if current_direction == Direction.left:
															# We keep current_direction as left.
															# Because if the straight line keeps as straight,
															# we want to know if this straight line is towards left.
															break
												elif current_direction == Direction.right:
															# If the straight line is towards right,
															# every previous points on those straigh line is not convex hull.
															stack.pop()
									if next_direction == Direction.right:
												stack.pop()
						stack.append(sorted_points[i]						)
			return list(A						)


