


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 the experiment tracking capability,
# 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
#
########################################################################

snake_case__			 =						16
snake_case__			 =						32


def 				lowerCamelCase__	(      a	:   Accelerator	,  a	:   int = 16				)    ->	Tuple:





			"""simple docstring"""
			a__ :int						  =    AutoTokenizer.from_pretrained("bert-base-cased"				)
			a__ :Union[str, Any]						  =    load_dataset("glue"	,  "mrpc"				)

			def tokenize_function(a	:   Any				):
						# max_length=None => use the model max length (it's actually the default)
						a__ :List[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():
						a__ :Optional[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
			a__ :List[Any]						  =    tokenized_datasets.rename_column("label"	,  "labels"				)

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

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

			# Instantiate dataloaders.
			a__ :Dict						  =    DataLoader(
			    tokenized_datasets["train"]	,  shuffle=a	,  collate_fn=a	,  batch_size=a				)
			a__ :Union[str, 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

		snake_case__			 =						mocked_dataloaders  # noqa: F811


def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Any				)    ->	Dict:





			"""simple docstring"""
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS"	,  a				) == "1":
						a__ :int						  =    2
			# Initialize Accelerator

			# New Code #
			# We pass in "all" to `log_with` to grab all available trackers in the environment
			# Note: If using a custom `Tracker` class, should be passed in here such as:
			# >>> log_with = ["all", MyCustomTrackerClassInstance()]
			if args.with_tracking:
						a__ :Optional[int]						  =    Accelerator(
						    cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  log_with="all"	,  project_dir=args.project_dir				)
			else:
						a__ :Dict						  =    Accelerator(cpu=args.cpu	,  mixed_precision=args.mixed_precision				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a__ :Tuple						  =    config["lr"]
			a__ :Optional[int]						  =    int(config["num_epochs"]				)
			a__ :Dict						  =    int(config["seed"]				)
			a__ :int						  =    int(config["batch_size"]				)
			set_seed(a				)

			a__  ,  a__ :str						  =    get_dataloaders(a	,  a				)
			a__ :str						  =    evaluate.load("glue"	,  "mrpc"				)

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

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__ :List[str]						  =    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).
			a__ :List[str]						  =    model.to(accelerator.device				)

			# Instantiate optimizer
			a__ :Union[str, Any]						  =    AdamW(params=model.parameters()	,  lr=a				)

			# Instantiate scheduler
			a__ :List[str]						  =    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.
			a__  ,  a__  ,  a__  ,  a__  ,  a__ :List[Any]						  =    accelerator.prepare(
			    a	,  a	,  a	,  a	,  a				)

			# New Code #
			# We need to initialize the trackers we use. Overall configurations can also be stored
			if args.with_tracking:
						a__ :List[str]						  =    os.path.split(a				)[-1].split("."				)[0]
						accelerator.init_trackers(a	,  a				)

			# Now we train the model
			for epoch in range(a				):
						model.train()
						# New Code #
						# For our tracking example, we will log the total loss of each epoch
						if args.with_tracking:
									a__ :Dict						  =    0
						for step, batch in enumerate(a				):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device				)
									a__ :Union[str, Any]						  =    model(**a				)
									a__ :int						  =    outputs.loss
									# New Code #
									if args.with_tracking:
												total_loss += loss.detach().float()
									a__ :str						  =    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` (the default).
									batch.to(accelerator.device				)
									with torch.no_grad():
												a__ :Optional[Any]						  =    model(**a				)
									a__ :Optional[int]						  =    outputs.logits.argmax(dim=-1				)
									a__  ,  a__ :int						  =    accelerator.gather_for_metrics((predictions, batch["labels"])				)
									metric.add_batch(
									    predictions=a	,  references=a	,  )

						a__ :List[Any]						  =    metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''	,  a				)

						# New Code #
						# To actually log, we call `Accelerator.log`
						# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
						if args.with_tracking:
									accelerator.log(
									    {
									        "accuracy": eval_metric["accuracy"],
									        "f1": eval_metric["f1"],
									        "train_loss": total_loss.item() / len(a				),
									        "epoch": epoch,
									    }	,  step=a	,  )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
			if args.with_tracking:
						accelerator.end_training()


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





			"""simple docstring"""
			a__ :Optional[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."				)
			parser.add_argument(
			    "--with_tracking"	,  action="store_true"	,  help="Whether to load in all available experiment trackers from the environment and use them for logging."	,  )
			parser.add_argument(
			    "--project_dir"	,  type=a	,  default="logs"	,  help="Location on where to store experiment tracking logs` and relevent project information"	,  )
			a__ :Tuple						  =    parser.parse_args()
			a__ :str						  =    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(a	,  a				)


if __name__ == "__main__":
		main()






import numpy as np


def 				lowerCamelCase__	(      a	:   np.ndarray	,  a	:   np.ndarray	,  a	:   float = 1e-12	,  a	:   int = 100	,  )    ->	tuple[float, np.ndarray]:





			"""simple docstring"""
			assert np.shape(a				)[0] == np.shape(a				)[1]
			# Ensure proper dimensionality.
			assert np.shape(a				)[0] == np.shape(a				)[0]
			# Ensure inputs are either both complex or both real
			assert np.iscomplexobj(a				) == np.iscomplexobj(a				)
			a__ :List[str]						  =    np.iscomplexobj(a				)
			if is_complex:
						# Ensure complex input_matrix is Hermitian
						assert np.array_equal(a	,  input_matrix.conj().T				)

			# Set convergence to False. Will define convergence when we exceed max_iterations
			# or when we have small changes from one iteration to next.

			a__ :List[str]						  =    False
			a__ :str						  =    0
			a__ :Dict						  =    0
			a__ :Tuple						  =    1e12

			while not convergence:
						# Multiple matrix by the vector.
						a__ :Tuple						  =    np.dot(a	,  a				)
						# Normalize the resulting output vector.
						a__ :List[Any]						  =    w / np.linalg.norm(a				)
						# Find rayleigh quotient
						# (faster than usual b/c we know vector is normalized already)
						a__ :List[str]						  =    vector.conj().T if is_complex else vector.T
						a__ :Any						  =    np.dot(a	,  np.dot(a	,  a				)				)

						# Check convergence.
						a__ :Optional[Any]						  =    np.abs(lambda_ - lambda_previous				) / lambda_
						iterations += 1

						if error <= error_tol or iterations >= max_iterations:
									a__ :List[str]						  =    True

						a__ :Optional[int]						  =    lambda_

			if is_complex:
						a__ :Union[str, Any]						  =    np.real(lambda_				)

			return lambda_, vector


def 				lowerCamelCase__	(      )    ->	None:





			"""simple docstring"""
			a__ :List[str]						  =    np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
			a__ :Dict						  =    np.array([41, 4, 20]				)
			a__ :Union[str, Any]						  =    real_input_matrix.astype(np.complexaaa				)
			a__ :Optional[Any]						  =    np.triu(1j * complex_input_matrix	,  1				)
			complex_input_matrix += imag_matrix
			complex_input_matrix += -1 * imag_matrix.T
			a__ :Union[str, Any]						  =    np.array([41, 4, 20]				).astype(np.complexaaa				)

			for problem_type in ["real", "complex"]:
						if problem_type == "real":
									a__ :Union[str, Any]						  =    real_input_matrix
									a__ :int						  =    real_vector
						elif problem_type == "complex":
									a__ :Optional[Any]						  =    complex_input_matrix
									a__ :List[Any]						  =    complex_vector

						# Our implementation.
						a__  ,  a__ :Any						  =    power_iteration(a	,  a				)

						# Numpy implementation.

						# Get eigenvalues and eigenvectors using built-in numpy
						# eigh (eigh used for symmetric or hermetian matrices).
						a__  ,  a__ :int						  =    np.linalg.eigh(a				)
						# Last eigenvalue is the maximum one.
						a__ :int						  =    eigen_values[-1]
						# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
						a__ :Optional[Any]						  =    eigen_vectors[:, -1]

						# Check our implementation and numpy gives close answers.
						assert np.abs(eigen_value - eigen_value_max				) <= 1e-6
						# Take absolute values element wise of each eigenvector.
						# as they are only unique to a minus sign.
						assert np.linalg.norm(np.abs(a				) - np.abs(a				)				) <= 1e-6


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		test_power_iteration()


