


import argparse

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
#
# 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 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_			       =      1_6
lowercase_			       =      3_2
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__ = 16		):
 __lowerCamelCase						:				List[str]						   =		AutoTokenizer.from_pretrained('bert-base-cased'		)
 __lowerCamelCase						:				Optional[Any]						   =		load_dataset('glue'     ,			'mrpc'		)

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

 def collate_fn(SCREAMING_SNAKE_CASE__		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __lowerCamelCase						:				Union[str, Any]						   =		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						:				str						   =		16
  elif accelerator.mixed_precision != "no":
   __lowerCamelCase						:				Tuple						   =		8
  else:
   __lowerCamelCase						:				int						   =		None

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

 # Instantiate dataloaders.
 __lowerCamelCase						:				Any						   =		DataLoader(
     tokenized_datasets['train']     ,			shuffle=__a     ,			collate_fn=__a     ,			batch_size=__a     ,			drop_last=__a		)
 __lowerCamelCase						:				Any						   =		DataLoader(
     tokenized_datasets['validation']     ,			shuffle=__a     ,			collate_fn=__a     ,			batch_size=__a     ,			drop_last=(accelerator.mixed_precision == 'fp8')     ,			)

 return train_dataloader, eval_dataloader
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				List[Any]						   =		Accelerator(cpu=args.cpu     ,			mixed_precision=args.mixed_precision		)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCamelCase						:				List[Any]						   =		config['''lr''']
 __lowerCamelCase						:				List[str]						   =		int(config['num_epochs']		)
 __lowerCamelCase						:				int						   =		int(config['seed']		)
 __lowerCamelCase						:				str						   =		int(config['batch_size']		)

 __lowerCamelCase						:				List[str]						   =		evaluate.load('glue'     ,			'mrpc'		)

 # If the batch size is too big we use gradient accumulation
 __lowerCamelCase						:				Tuple						   =		1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  __lowerCamelCase						:				Optional[Any]						   =		batch_size // MAX_GPU_BATCH_SIZE
  __lowerCamelCase						:				Dict						   =		MAX_GPU_BATCH_SIZE

 set_seed(__a		)
 __lowerCamelCase						:				Tuple						   =		get_dataloaders(__a     ,			__a		)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __lowerCamelCase						:				List[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).
 __lowerCamelCase						:				Optional[int]						   =		model.to(accelerator.device		)
 # Instantiate optimizer
 __lowerCamelCase						:				Union[str, Any]						   =		AdamW(params=model.parameters()     ,			lr=__a		)

 # Instantiate scheduler
 __lowerCamelCase						:				Optional[Any]						   =		get_linear_schedule_with_warmup(
     optimizer=__a     ,			num_warmup_steps=100     ,			num_training_steps=(len(__a		) * 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						:				str						   =		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		)
   __lowerCamelCase						:				Dict						   =		model(**__a		)
   __lowerCamelCase						:				Union[str, Any]						   =		outputs.loss
   __lowerCamelCase						:				Tuple						   =		loss / gradient_accumulation_steps
   accelerator.backward(__a		)
   if step % gradient_accumulation_steps == 0:
    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():
    __lowerCamelCase						:				Optional[int]						   =		model(**__a		)
   __lowerCamelCase						:				int						   =		outputs.logits.argmax(dim=-1		)
   __lowerCamelCase						:				List[Any]						   =		accelerator.gather_for_metrics((predictions, batch['labels'])		)
   metric.add_batch(
       predictions=__a     ,			references=__a     ,			)

  __lowerCamelCase						:				List[str]						   =		metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f'epoch {epoch}:'     ,			__a		)
def  UpperCamelCase__	(    ):
 __lowerCamelCase						:				Union[str, Any]						   =		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.'		)
 __lowerCamelCase						:				int						   =		parser.parse_args()
 __lowerCamelCase						:				int						   =		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__a     ,			__a		)


if __name__ == "__main__":
       main()





'''simple docstring'''
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							if components is None:
												UpperCamelCase__  :List[Any]				   =					[]
							UpperCamelCase__  :Optional[Any]				   =					list(UpperCamelCase_				)




		def __len__(						self				):

							'''simple docstring'''


							return len(self.__components				)




		def __str__(						self				):

							'''simple docstring'''


							return "(" + ",".join(map(UpperCamelCase_       ,  self.__components				)				) + ")"




		def __add__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					len(self				)
							if size == len(UpperCamelCase_				):
												UpperCamelCase__  :Dict				   =					[self.__components[i] + other.component(UpperCamelCase_				) for i in range(UpperCamelCase_				)]
												return Vector(UpperCamelCase_				)
							else:
												raise Exception('''must have the same size'''				)




		def __sub__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					len(self				)
							if size == len(UpperCamelCase_				):
												UpperCamelCase__  :List[str]				   =					[self.__components[i] - other.component(UpperCamelCase_				) for i in range(UpperCamelCase_				)]
												return Vector(UpperCamelCase_				)
							else:  # error case
												raise Exception('''must have the same size'''				)




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if isinstance(UpperCamelCase_       ,  (float, int)				):
												UpperCamelCase__  :Optional[Any]				   =					[c * other for c in self.__components]
												return Vector(UpperCamelCase_				)
							elif isinstance(UpperCamelCase_       ,  UpperCamelCase_				) and len(self				) == len(UpperCamelCase_				):
												UpperCamelCase__  :Optional[int]				   =					len(self				)
												UpperCamelCase__  :List[Any]				   =					[self.__components[i] * other.component(UpperCamelCase_				) for i in range(UpperCamelCase_				)]
												return sum(UpperCamelCase_				)
							else:  # error case
												raise Exception('''invalid operand!'''				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return Vector(self.__components				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if isinstance(UpperCamelCase_       ,  UpperCamelCase_				) and -len(self.__components				) <= i < len(self.__components				):
												return self.__components[i]
							else:
												raise Exception('''index out of range'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							assert -len(self.__components				) <= pos < len(self.__components				)
							UpperCamelCase__  :Optional[int]				   =					value




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if len(self.__components				) == 0:
												raise Exception('''Vector is empty'''				)
							UpperCamelCase__  :List[str]				   =					[c**2 for c in self.__components]
							return math.sqrt(sum(UpperCamelCase_				)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = False				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					self * other
							UpperCamelCase__  :Optional[int]				   =					self.euclidean_length() * other.euclidean_length()
							if deg:
												return math.degrees(math.acos(num / den				)				)
							else:
												return math.acos(num / den				)



def      a (     __a  )    ->      Vector:



					'''simple docstring'''






					assert isinstance(__a					,   __a  )
					return Vector([0] * dimension  )


def      a (     __a					,   __a  )    ->      Vector:



					'''simple docstring'''






					assert isinstance(__a					,   __a  ) and (isinstance(__a					,   __a  ))
					UpperCamelCase__  :str				   =					[0] * dimension
					UpperCamelCase__  :Tuple				   =					1
					return Vector(__a  )


def      a (     __a					,   __a					,   __a  )    ->      Vector:



					'''simple docstring'''






					assert (
					    isinstance(__a					,   __a  )
					    and isinstance(__a					,   __a  )
					    and (isinstance(__a					,   (int, float)  ))
					)
					return x * scalar + y


def      a (     __a					,   __a					,   __a  )    ->      Vector:



					'''simple docstring'''






					random.seed(__a  )
					UpperCamelCase__  :Optional[Any]				   =					[random.randint(__a					,   __a  ) for _ in range(__a  )]
					return Vector(__a  )
class   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					matrix
							UpperCamelCase__  :Union[str, Any]				   =					w
							UpperCamelCase__  :Optional[int]				   =					h




		def __str__(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					''''''
							for i in range(self.__height				):
												ans += "|"
												for j in range(self.__width				):
																	if j < self.__width - 1:
																						ans += str(self.__matrix[i][j]				) + ","
																	else:
																						ans += str(self.__matrix[i][j]				) + "|\n"
							return ans




		def __add__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__width == other.width() and self.__height == other.height():
												UpperCamelCase__  :int				   =					[]
												for i in range(self.__height				):
																	UpperCamelCase__  :int				   =					[
																	    self.__matrix[i][j] + other.component(UpperCamelCase_       ,  UpperCamelCase_				)
																	    for j in range(self.__width				)
																	]
																	matrix.append(UpperCamelCase_				)
												return Matrix(UpperCamelCase_       ,  self.__width       ,  self.__height				)
							else:
												raise Exception('''matrix must have the same dimension!'''				)




		def __sub__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__width == other.width() and self.__height == other.height():
												UpperCamelCase__  :Optional[Any]				   =					[]
												for i in range(self.__height				):
																	UpperCamelCase__  :Optional[int]				   =					[
																	    self.__matrix[i][j] - other.component(UpperCamelCase_       ,  UpperCamelCase_				)
																	    for j in range(self.__width				)
																	]
																	matrix.append(UpperCamelCase_				)
												return Matrix(UpperCamelCase_       ,  self.__width       ,  self.__height				)
							else:
												raise Exception('''matrices must have the same dimension!'''				)




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		@overload
		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							...




		def __mul__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							if isinstance(UpperCamelCase_       ,  UpperCamelCase_				):  # matrix-vector
												if len(UpperCamelCase_				) == self.__width:
																	UpperCamelCase__  :Any				   =					zero_vector(self.__height				)
																	for i in range(self.__height				):
																						UpperCamelCase__  :Any				   =					[
																						    self.__matrix[i][j] * other.component(UpperCamelCase_				)
																						    for j in range(self.__width				)
																						]
																						ans.change_component(UpperCamelCase_       ,  sum(UpperCamelCase_				)				)
																	return ans
												else:
																	raise Exception(
																	    '''vector must have the same size as the '''
																	    '''number of columns of the matrix!'''				)
							elif isinstance(UpperCamelCase_       ,  (int, float)				):  # matrix-scalar
												UpperCamelCase__  :Union[str, Any]				   =					[
												    [self.__matrix[i][j] * other for j in range(self.__width				)]
												    for i in range(self.__height				)
												]
												return Matrix(UpperCamelCase_       ,  self.__width       ,  self.__height				)
							return None




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return self.__height




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return self.__width




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if 0 <= x < self.__height and 0 <= y < self.__width:
												return self.__matrix[x][y]
							else:
												raise Exception('''change_component: indices out of bounds'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if 0 <= x < self.__height and 0 <= y < self.__width:
												UpperCamelCase__  :Tuple				   =					value
							else:
												raise Exception('''change_component: indices out of bounds'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__height != self.__width:
												raise Exception('''Matrix is not square'''				)
							UpperCamelCase__  :str				   =					self.__matrix[:x] + self.__matrix[x + 1 :]
							for i in range(len(UpperCamelCase_				)				):
												UpperCamelCase__  :Union[str, Any]				   =					minor[i][:y] + minor[i][y + 1 :]
							return Matrix(UpperCamelCase_       ,  self.__width - 1       ,  self.__height - 1				).determinant()




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if self.__height != self.__width:
												raise Exception('''Matrix is not square'''				)
							if 0 <= x < self.__height and 0 <= y < self.__width:
												return (-1) ** (x + y) * self.minor(UpperCamelCase_       ,  UpperCamelCase_				)
							else:
												raise Exception('''Indices out of bounds'''				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.__height != self.__width:
												raise Exception('''Matrix is not square'''				)
							if self.__height < 1:
												raise Exception('''Matrix has no element'''				)
							elif self.__height == 1:
												return self.__matrix[0][0]
							elif self.__height == 2:
												return (
												    self.__matrix[0][0] * self.__matrix[1][1]
												    - self.__matrix[0][1] * self.__matrix[1][0]
												)
							else:
												UpperCamelCase__  :Optional[Any]				   =					[
												    self.__matrix[0][y] * self.cofactor(0       ,  UpperCamelCase_				) for y in range(self.__width				)
												]
												return sum(UpperCamelCase_				)



def      a (     __a  )    ->      Matrix:



					'''simple docstring'''






					UpperCamelCase__  :list[list[float]]				   =					[[0] * n for _ in range(__a  )]
					return Matrix(__a					,   __a					,   __a  )







def      a (     __a					,   __a					,   __a					,   __a  )    ->      Matrix:



					'''simple docstring'''






					random.seed(__a  )
					UpperCamelCase__  :list[list[float]]				   =					[
					    [random.randint(__a					,   __a  ) for _ in range(__a  )] for _ in range(__a  )
					]
					return Matrix(__a					,   __a					,   __a  )