'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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	:       Optional[int]						       =	16
__lowerCAmelCase	:       List[Any]						       =	32





def       lowerCAmelCase					(		UpperCamelCase__				:   Accelerator    ,					UpperCamelCase__				:   int = 1_6       ):


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

	def tokenize_function(UpperCamelCase__				:   Union[str, Any]       ):
		# max_length=None => use the model max length (it's actually the default)
		__UpperCAmelCase							=			tokenizer(examples['''sentence1''']    ,					examples['''sentence2''']    ,					truncation=UpperCamelCase__    ,					max_length=UpperCamelCase__       )
		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(
		    UpperCamelCase__    ,					batched=UpperCamelCase__    ,					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(UpperCamelCase__				:   Union[str, Any]       ):
		# 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(
		    UpperCamelCase__    ,					padding='''longest'''    ,					max_length=UpperCamelCase__    ,					pad_to_multiple_of=UpperCamelCase__    ,					return_tensors='''pt'''    ,					)

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

	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	:       str						       =	mocked_dataloaders  # noqa: F811





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	# For testing only
	if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,					UpperCamelCase__       ) == "1":
		__UpperCAmelCase							=			2
	# New Code #
	__UpperCAmelCase							=			int(args.gradient_accumulation_steps       )
	__UpperCAmelCase							=			int(args.local_sgd_steps       )
	# Initialize accelerator
	__UpperCAmelCase							=			Accelerator(
	    cpu=args.cpu    ,					mixed_precision=args.mixed_precision    ,					gradient_accumulation_steps=UpperCamelCase__       )
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
	# 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(UpperCamelCase__       )
	__UpperCAmelCase				,			__UpperCAmelCase							=			get_dataloaders(UpperCamelCase__    ,					UpperCamelCase__       )
	# 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=UpperCamelCase__       )

	# 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=UpperCamelCase__       )

	# Instantiate scheduler
	__UpperCAmelCase							=			get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__    ,					num_warmup_steps=1_0_0    ,					num_training_steps=(len(UpperCamelCase__       ) * 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(
	    UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )

	# Now we train the model
	for epoch in range(UpperCamelCase__       ):
		model.train()
		with LocalSGD(
		    accelerator=UpperCamelCase__    ,					model=UpperCamelCase__    ,					local_sgd_steps=UpperCamelCase__    ,					enabled=local_sgd_steps is not None       ) as local_sgd:
			for step, batch in enumerate(UpperCamelCase__       ):
				# 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(UpperCamelCase__       ):
					__UpperCAmelCase							=			model(**UpperCamelCase__       )
					__UpperCAmelCase							=			output.loss
					accelerator.backward(UpperCamelCase__       )
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

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

		__UpperCAmelCase							=			metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""    ,					UpperCamelCase__       )





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			argparse.ArgumentParser(description='''Simple example of training script.'''       )
	parser.add_argument(
	    '''--mixed_precision'''    ,					type=UpperCamelCase__    ,					default=UpperCamelCase__    ,					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=UpperCamelCase__    ,					default=1    ,					help='''The number of minibatches to be ran before gradients are accumulated.'''    ,					)
	parser.add_argument(
	    '''--local_sgd_steps'''    ,					type=UpperCamelCase__    ,					default=8    ,					help='''Number of local SGD steps or None to disable local SGD'''       )
	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(UpperCamelCase__    ,					UpperCamelCase__       )


if __name__ == "__main__":
					main()

'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__lowerCAmelCase	:       str						       =	299_792_458

# Symbols
__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase	:       Any						       =	symbols("ct x y z")





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	if velocity > c:
		raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''       )
	elif velocity < 1:
		# Usually the speed should be much higher than 1 (c order of magnitude)
		raise ValueError('''Speed must be greater than or equal to 1!'''       )

	return velocity / c





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return 1 / sqrt(1 - beta(UpperCamelCase__       ) ** 2       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return np.array(
	    [
	        [gamma(UpperCamelCase__       ), -gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), 0, 0],
	        [-gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), gamma(UpperCamelCase__       ), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   np.ndarray | None = None       ):


	"""simple docstring"""
	# Ensure event is not empty
	if event is None:
		__UpperCAmelCase							=			np.array([ct, x, y, z]       )  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(UpperCamelCase__       ) @ event


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Example of symbolic vector:
					__lowerCAmelCase	:       Dict						       =	transform(29_979_245)
					print("Example of four vector: ")
					print(F"""ct' = {four_vector[0]}""")
					print(F"""x' = {four_vector[1]}""")
					print(F"""y' = {four_vector[2]}""")
					print(F"""z' = {four_vector[3]}""")

					# Substitute symbols with numerical values
					__lowerCAmelCase	:       Union[str, Any]						       =	{ct: c, x: 1, y: 1, z: 1}
					__lowerCAmelCase	:       Optional[int]						       =	[four_vector[i].subs(sub_dict) for i in range(4)]

					print(F"""\n{numerical_vector}""")
