


"""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
#
########################################################################

__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Union[str, Any]:


				"""simple docstring"""





				snake_case					:   int         =					AutoTokenizer.from_pretrained("bert-base-cased"				)
				snake_case					:   str         =					load_dataset("glue"		,							"mrpc"				)

				def tokenize_function(lowercase						:						Tuple				):
								# max_length=None => use the model max length (it's actually the default)
								snake_case					:   List[str]         =					tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=lowercase		,							max_length=lowercase				)
								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():
								snake_case					:   Any         =					datasets.map(
								    lowercase		,							batched=lowercase		,							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
				snake_case					:   Tuple         =					tokenized_datasets.rename_column("label"		,							"labels"				)

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

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

				# Instantiate dataloaders.
				snake_case					:   Any         =					DataLoader(
				    tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
				snake_case					:   List[str]         =					DataLoader(
				    tokenized_datasets["validation"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)

				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		(		lowercase						:						Dict		,							lowercase						:						int				)			->		Tuple:


				"""simple docstring"""





				if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
								snake_case					:   Union[str, Any]         =					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:
								snake_case					:   str         =					Accelerator(
								    cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with="all"		,							project_dir=args.project_dir				)
				else:
								snake_case					:   Optional[int]         =					Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case					:   str         =					config["lr"]
				snake_case					:   Dict         =					int(config["num_epochs"]				)
				snake_case					:   int         =					int(config["seed"]				)
				snake_case					:   Tuple         =					int(config["batch_size"]				)
				set_seed(lowercase				)

				snake_case					:   List[Any]         =					get_dataloaders(lowercase		,							lowercase				)
				snake_case					:   Optional[Any]         =					evaluate.load("glue"		,							"mrpc"				)

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

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case					:   List[Any]         =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=lowercase				)

				# 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).
				snake_case					:   str         =					model.to(accelerator.device				)

				# Instantiate optimizer
				snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

				# Instantiate scheduler
				snake_case					:   Optional[Any]         =					get_linear_schedule_with_warmup(
				    optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * 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.
				snake_case					:   Union[str, Any]         =					accelerator.prepare(
				    lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

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

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												snake_case					:   Any         =					0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												snake_case					:   Dict         =					model(**lowercase				)
												snake_case					:   int         =					outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												snake_case					:   Union[str, Any]         =					loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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

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

    # 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		(		)			->		str:


				"""simple docstring"""





				snake_case					:   Union[str, Any]         =					argparse.ArgumentParser(description="Simple example of training script."				)
				parser.add_argument(
				    "--mixed_precision"		,							type=lowercase		,							default=lowercase		,							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=lowercase		,							default="logs"		,							help="Location on where to store experiment tracking logs` and relevent project information"		,							)
				snake_case					:   int         =					parser.parse_args()
				snake_case					:   List[str]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
		main()









"""simple docstring"""



import numpy as np







class _lowerCAmelCase       :







   def __init__(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					(0, 0)
       snake_case					:   str         =					None
       snake_case					:   int         =					0
       snake_case					:   Optional[Any]         =					0
       snake_case					:   Tuple         =					0







   def __eq__(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return self.position == cell.position







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       print(self.position		)







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__=(5, 5)		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					np.zeros(UpperCamelCase__		)
       snake_case					:   str         =					world_size[0]
       snake_case					:   str         =					world_size[1]







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       print(self.w		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[
           (-1, -1),
           (-1, 0),
           (-1, 1),
           (0, -1),
           (0, 1),
           (1, -1),
           (1, 0),
           (1, 1),
       ]
       snake_case					:   Tuple         =					cell.position[0]
       snake_case					:   str         =					cell.position[1]
       snake_case					:   List[Any]         =					[]
       for n in neughbour_cord:
           snake_case					:   Any         =					current_x + n[0]
           snake_case					:   List[str]         =					current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
               snake_case					:   Tuple         =					Cell()
               snake_case					:   int         =					(x, y)
               snake_case					:   List[Any]         =					cell
               neighbours.append(UpperCamelCase__		)
       return neighbours







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]				)			->		Any:


    """simple docstring"""





    snake_case					:   Tuple         =					[]
    snake_case					:   List[Any]         =					[]
    _open.append(lowercase				)

    while _open:
        snake_case					:   Union[str, Any]         =					np.argmin([n.f for n in _open]				)
        snake_case					:   Tuple         =					_open[min_f]
        _closed.append(_open.pop(lowercase				)				)
        if current == goal:
            break
        for n in world.get_neigbours(lowercase				):
            for c in _closed:
                if c == n:
                    continue
            snake_case					:   int         =					current.g + 1
            snake_case      ,snake_case					:   Any         =					n.position
            snake_case      ,snake_case					:   Any         =					goal.position
            snake_case					:   int         =					(ya - ya) ** 2 + (xa - xa) ** 2
            snake_case					:   List[Any]         =					n.h + n.g

            for c in _open:
                if c == n and c.f < n.f:
                    continue
            _open.append(lowercase				)
    snake_case					:   str         =					[]
    while current.parent is not None:
        path.append(current.position				)
        snake_case					:   List[str]         =					current.parent
    path.append(current.position				)
    return path[::-1]


if __name__ == "__main__":
  __snake_case         =					Gridworld()
  # Start position and goal
  __snake_case         =					Cell()
  __snake_case         =					(0, 0)
  __snake_case         =					Cell()
  __snake_case         =					(4, 4)
  print(F'''path from {start.position} to {goal.position}''')
  __snake_case         =					astar(world, start, goal)
  #   Just for visual reasons.
  for i in s:
    __snake_case         =					1
  print(world.w)





