





import argparse

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


A    :					Optional[int] =      1_6
A    :					Optional[Any] =      3_2







def    __lowerCamelCase					(							__a   :Accelerator    ,     __a   :int = 1_6    )		->				Optional[int]:






		"""simple docstring"""

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

		def tokenize_function(__a   :Union[str, Any]    ):
				# max_length=None => use the model max length (it's actually the default)
				A__        =   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__        =   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__        =   tokenized_datasets.rename_column("""label"""    ,     """labels"""    )

		def collate_fn(__a   :Optional[int]    ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				A__        =   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":
						A__        =   1_6
				elif accelerator.mixed_precision != "no":
						A__        =   8
				else:
						A__        =   None

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

		# Instantiate dataloaders.
		A__        =   DataLoader(
		    tokenized_datasets["""train"""]    ,     shuffle=__a    ,     collate_fn=__a    ,     batch_size=__a    ,     drop_last=__a    )
		A__        =   DataLoader(
		    tokenized_datasets["""validation"""]    ,     shuffle=__a    ,     collate_fn=__a    ,     batch_size=__a    ,     drop_last=(accelerator.mixed_precision == """fp8""")    ,     )

		return train_dataloader, eval_dataloader







def    __lowerCamelCase					(							__a   :List[str]    ,     __a   :Optional[Any]    )		->				Dict:






		"""simple docstring"""

		A__        =   Accelerator(cpu=args.cpu    ,     mixed_precision=args.mixed_precision    )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		A__        =   config["""lr"""]
		A__        =   int(config["""num_epochs"""]    )
		A__        =   int(config["""seed"""]    )
		A__        =   int(config["""batch_size"""]    )

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

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

		set_seed(__a    )
		A__      ,			A__        =   get_dataloaders(__a    ,     __a    )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A__        =   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__        =   model.to(accelerator.device    )
		# Instantiate optimizer
		A__        =   AdamW(params=model.parameters()    ,     lr=__a    )

		# Instantiate scheduler
		A__        =   get_linear_schedule_with_warmup(
		    optimizer=__a    ,     num_warmup_steps=1_0_0    ,     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__        =   accelerator.prepare(
		    __a    ,     __a    ,     __a    ,     __a    ,     __a    )

		# Now we train the model
		for epoch in range(__a    ):
				model.train()
				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__        =   model(**__a    )
						A__        =   outputs.loss
						A__        =   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`.
						batch.to(accelerator.device    )
						with torch.no_grad():
								A__        =   model(**__a    )
						A__        =   outputs.logits.argmax(dim=-1    )
						A__      ,			A__        =   accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
						metric.add_batch(
						    predictions=__a    ,     references=__a    ,     )

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







def    __lowerCamelCase					(							)		->				List[str]:






		"""simple docstring"""

		A__        =   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."""    )
		A__        =   parser.parse_args()
		A__        =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
		training_function(__a    ,     __a    )


if __name__ == "__main__":
					main()










from __future__ import annotations

from PIL import Image

# Define glider example
A    :					Any =      [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
A    :					Optional[Any] =      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def    __lowerCamelCase					(							__a   :list[list[int]]    )		->				list[list[int]]:






		"""simple docstring"""

		A__        =   []
		for i in range(len(__a    )    ):
				A__        =   []
				for j in range(len(cells[i]    )    ):
						# Get the number of live neighbours
						A__        =   0
						if i > 0 and j > 0:
								neighbour_count += cells[i - 1][j - 1]
						if i > 0:
								neighbour_count += cells[i - 1][j]
						if i > 0 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i - 1][j + 1]
						if j > 0:
								neighbour_count += cells[i][j - 1]
						if j < len(cells[i]    ) - 1:
								neighbour_count += cells[i][j + 1]
						if i < len(__a    ) - 1 and j > 0:
								neighbour_count += cells[i + 1][j - 1]
						if i < len(__a    ) - 1:
								neighbour_count += cells[i + 1][j]
						if i < len(__a    ) - 1 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i + 1][j + 1]

						# Rules of the game of life (excerpt from Wikipedia):
						# 1. Any live cell with two or three live neighbours survives.
						# 2. Any dead cell with three live neighbours becomes a live cell.
						# 3. All other live cells die in the next generation.
						#    Similarly, all other dead cells stay dead.
						A__        =   cells[i][j] == 1
						if (
						    (alive and 2 <= neighbour_count <= 3)
						    or not alive
						    and neighbour_count == 3
						):
								next_generation_row.append(1    )
						else:
								next_generation_row.append(0    )

				next_generation.append(__a    )
		return next_generation







def    __lowerCamelCase					(							__a   :list[list[int]]    ,     __a   :int    )		->				list[Image.Image]:






		"""simple docstring"""

		A__        =   []
		for _ in range(__a    ):
				# Create output image
				A__        =   Image.new("""RGB"""    ,     (len(cells[0]    ), len(__a    ))    )
				A__        =   img.load()

				# Save cells to image
				for x in range(len(__a    )    ):
						for y in range(len(cells[0]    )    ):
								A__        =   2_5_5 - cells[y][x] * 2_5_5
								A__        =   (colour, colour, colour)

        # Save image
				images.append(__a    )
				A__        =   new_generation(__a    )
		return images


if __name__ == "__main__":
					A    :					str =      generate_images(GLIDER, 1_6)
					images[0].save('''out.gif''', save_all=True, append_images=images[1:])



