






'''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


########################################################################
# 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
#
########################################################################

_A     :      Dict   					=16
_A     :      Dict   					=32





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = 16				)				->	Union[str, Any]:
							lowerCamelCase__ :     int	  =	AutoTokenizer.from_pretrained("""bert-base-cased"""				)
							lowerCamelCase__ :     Any	  =	load_dataset("""glue"""    ,      """mrpc"""				)

							def tokenize_function(UpperCamelCase				):
														# max_length=None => use the model max length (it's actually the default)
														lowerCamelCase__ :     Dict	  =	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():
														lowerCamelCase__ :     List[Any]	  =	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
							lowerCamelCase__ :     int	  =	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.
														lowerCamelCase__ :     Optional[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__ :     Dict	  =	16
														elif accelerator.mixed_precision != "no":
																					lowerCamelCase__ :     Union[str, Any]	  =	8
														else:
																					lowerCamelCase__ :     int	  =	None

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

							# Instantiate dataloaders.
							lowerCamelCase__ :     Optional[Any]	  =	DataLoader(
							    tokenized_datasets["""train"""]    ,      shuffle=UpperCamelCase    ,      collate_fn=UpperCamelCase    ,      batch_size=UpperCamelCase				)
							lowerCamelCase__ :     int	  =	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     :      Optional[Any]   					=mocked_dataloaders  # noqa: F811





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	int:
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,      UpperCamelCase				) == "1":
														lowerCamelCase__ :     List[str]	  =	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:
														lowerCamelCase__ :     List[str]	  =	Accelerator(
														    cpu=args.cpu    ,      mixed_precision=args.mixed_precision    ,      log_with="""all"""    ,      project_dir=args.project_dir				)
							else:
														lowerCamelCase__ :     str	  =	Accelerator(cpu=args.cpu    ,      mixed_precision=args.mixed_precision				)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCamelCase__ :     str	  =	config["""lr"""]
							lowerCamelCase__ :     int	  =	int(config["""num_epochs"""]				)
							lowerCamelCase__ :     Tuple	  =	int(config["""seed"""]				)
							lowerCamelCase__ :     Any	  =	int(config["""batch_size"""]				)
							set_seed(UpperCamelCase				)

							lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	get_dataloaders(UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Any	  =	evaluate.load("""glue"""    ,      """mrpc"""				)

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

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

							# Instantiate optimizer
							lowerCamelCase__ :     Dict	  =	AdamW(params=model.parameters()    ,      lr=UpperCamelCase				)

							# Instantiate scheduler
							lowerCamelCase__ :     Dict	  =	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.
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	accelerator.prepare(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

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

							# Now we train the model
							for epoch in range(UpperCamelCase				):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					lowerCamelCase__ :     Optional[Any]	  =	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				)
																					lowerCamelCase__ :     int	  =	model(**UpperCamelCase				)
																					lowerCamelCase__ :     Tuple	  =	outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					lowerCamelCase__ :     str	  =	loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase				)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

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

														# 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(UpperCamelCase				),
																					        """epoch""": epoch,
																					    }    ,      step=UpperCamelCase    ,      )

    # 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 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Optional[int]	  =	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."""				)
							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=UpperCamelCase    ,      default="""logs"""    ,      help="""Location on where to store experiment tracking logs` and relevent project information"""    ,      )
							lowerCamelCase__ :     Union[str, Any]	  =	parser.parse_args()
							lowerCamelCase__ :     Any	  =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(UpperCamelCase    ,      UpperCamelCase				)


if __name__ == "__main__":
			main()









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_A     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(UpperCamelCase    ,      110				)				).startswith(
														    """<PIL.Image.Image image mode=RGB size=100x100 at"""				)





def 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[int]	  =	canny.gen_gaussian_kernel(9    ,      sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							assert gg.gaussian_filter(UpperCamelCase    ,      5    ,      sigma=0.9				).all()





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							lowerCamelCase__ :     Tuple	  =	imread(UpperCamelCase    ,      0				)

							# Test for get_neighbors_pixel function() return not None
							lowerCamelCase__ :     int	  =	0
							lowerCamelCase__ :     Dict	  =	0
							lowerCamelCase__ :     Optional[Any]	  =	image[x_coordinate][y_coordinate]

							lowerCamelCase__ :     str	  =	lbp.get_neighbors_pixel(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert neighbors_pixels is not None

							# Test for local_binary_pattern function()
							# Create a numpy array as the same height and width of read image
							lowerCamelCase__ :     List[str]	  =	np.zeros((image.shape[0], image.shape[1])				)

							# Iterating through the image and calculating the local binary pattern value
							# for each pixel.
							for i in range(0    ,      image.shape[0]				):
														for j in range(0    ,      image.shape[1]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

