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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


A_      =      16
A_      =      32






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase = 16       )->       str:



			"""simple docstring"""







			lowercase							=		AutoTokenizer.from_pretrained('''bert-base-cased'''       )
			lowercase							=		load_dataset('''glue''',		'''mrpc'''       )

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

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

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

			# Instantiate dataloaders.
			lowercase							=		DataLoader(
			    tokenized_datasets['''train'''],		shuffle=UpperCAmelCase,		collate_fn=UpperCAmelCase,		batch_size=UpperCAmelCase       )
			lowercase							=		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

						A_      =      mocked_dataloaders  # noqa: F811






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			if os.environ.get('''TESTING_MOCKED_DATALOADERS''',		UpperCAmelCase       ) == "1":
						lowercase							=		2
			# Initialize accelerator
			lowercase							=		Accelerator(cpu=args.cpu,		mixed_precision=args.mixed_precision       )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowercase							=		config['''lr''']
			lowercase							=		int(config['''num_epochs''']       )
			lowercase							=		int(config['''seed''']       )
			lowercase							=		int(config['''batch_size''']       )

			lowercase							=		evaluate.load('''glue''',		'''mrpc'''       )

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

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

			# Instantiate optimizer
			lowercase							=		AdamW(params=model.parameters(),		lr=UpperCAmelCase       )

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

			# Now we train the model
			for epoch in range(UpperCAmelCase       ):
						model.train()
						for step, batch in enumerate(UpperCAmelCase       ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device       )
									lowercase							=		model(**UpperCAmelCase       )
									lowercase							=		outputs.loss
									lowercase							=		loss / gradient_accumulation_steps
									accelerator.backward(UpperCAmelCase       )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						lowercase							=		0
						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():
												lowercase							=		model(**UpperCAmelCase       )
									lowercase							=		outputs.logits.argmax(dim=-1       )
									lowercase		,lowercase							=		accelerator.gather((predictions, batch['''labels'''])       )
									# New Code #
									# First we check if it's a distributed system
									if accelerator.use_distributed:
												# Then see if we're on the last batch of our eval dataloader
												if step == len(UpperCAmelCase       ) - 1:
															# Last batch needs to be truncated on distributed systems as it contains additional samples
															lowercase							=		predictions[: len(eval_dataloader.dataset       ) - samples_seen]
															lowercase							=		references[: len(eval_dataloader.dataset       ) - samples_seen]
												else:
															# Otherwise we add the number of samples seen
															samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
									metric.add_batch(
									    predictions=UpperCAmelCase,		references=UpperCAmelCase,		)

						lowercase							=		metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'epoch {epoch}:',		UpperCAmelCase       )






def        __UpperCAmelCase    (       )->       Any:



			"""simple docstring"""







			lowercase							=		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.''',		)
			parser.add_argument('''--cpu''',		action='''store_true''',		help='''If passed, will train on the CPU.'''       )
			lowercase							=		parser.parse_args()
			lowercase							=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(UpperCAmelCase,		UpperCAmelCase       )


if __name__ == "__main__":
						main()






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





