
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
# and perform 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)
#
# 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 							__A(lowerCAmelCase						,  lowerCAmelCase = 1_6 )		->				Dict:

		"""simple docstring"""






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

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

		def collate_fn(lowerCAmelCase ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				_UpperCamelCase						    =	1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						_UpperCamelCase						    =	1_6
				elif accelerator.mixed_precision != "no":
						_UpperCamelCase						    =	8
				else:
						_UpperCamelCase						    =	None

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

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

		lowerCamelCase__	=							mocked_dataloaders  # noqa: F811


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Any:

		"""simple docstring"""






		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,  lowerCAmelCase ) == "1":
				_UpperCamelCase						    =	2
		# New Code #
		_UpperCamelCase						    =	int(args.gradient_accumulation_steps )
		# Initialize accelerator
		_UpperCamelCase						    =	Accelerator(
		    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  gradient_accumulation_steps=lowerCAmelCase )
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`""" )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_UpperCamelCase						    =	config["""lr"""]
		_UpperCamelCase						    =	int(config["""num_epochs"""] )
		_UpperCamelCase						    =	int(config["""seed"""] )
		_UpperCamelCase						    =	int(config["""batch_size"""] )

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

		set_seed(lowerCAmelCase )
		_UpperCamelCase    ,	_UpperCamelCase						    =	get_dataloaders(lowerCAmelCase						,  lowerCAmelCase )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCamelCase						    =	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).
		_UpperCamelCase						    =	model.to(accelerator.device )

		# Instantiate optimizer
		_UpperCamelCase						    =	AdamW(params=model.parameters()						,  lr=lowerCAmelCase )

		# Instantiate scheduler
		_UpperCamelCase						    =	get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase						,  num_warmup_steps=1_0_0						,  num_training_steps=(len(lowerCAmelCase ) * 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						    =	accelerator.prepare(
		    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )

		# Now we train the model
		for epoch in range(lowerCAmelCase ):
				model.train()
				for step, batch in enumerate(lowerCAmelCase ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device )
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(lowerCAmelCase ):
								_UpperCamelCase						    =	model(**lowerCAmelCase )
								_UpperCamelCase						    =	output.loss
								accelerator.backward(lowerCAmelCase )
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

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


def 							__A()		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	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."""						,  )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""						,  type=lowerCAmelCase						,  default=1						,  help="""The number of minibatches to be ran before gradients are accumulated."""						,  )
		parser.add_argument("""--cpu"""						,  action="""store_true"""						,  help="""If passed, will train on the CPU.""" )
		_UpperCamelCase						    =	parser.parse_args()
		_UpperCamelCase						    =	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
		training_function(lowerCAmelCase						,  lowerCAmelCase )


if __name__ == "__main__":
		main()





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = "x"						,  lowerCAmelCase = 1_0**-1_0						,  lowerCAmelCase = 1						,  )		->				complex:

		"""simple docstring"""






		_UpperCamelCase						    =	symbols(lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  diff(lowerCAmelCase						,  lowerCAmelCase ) )

		_UpperCamelCase						    =	starting_point

		while True:
				if diff_function(lowerCAmelCase ) != 0:
						_UpperCamelCase						    =	prev_guess - multiplicity * func(lowerCAmelCase ) / diff_function(
						    lowerCAmelCase )
				else:
						raise ZeroDivisionError("""Could not find root""" ) from None

				# Precision is checked by comparing the difference of consecutive guesses
				if abs(next_guess - prev_guess ) < precision:
						return next_guess

				_UpperCamelCase						    =	next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"""{newton_raphson("exp(x) - 1", 10, precision=0.0_05)}""",
		)

		# Find root of cos(x)
		print(F"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")



