
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


UpperCamelCase_   	=	1_6
UpperCamelCase_   	=	3_2






def 		_UpperCAmelCase   (	UpperCamelCase:     int			,							UpperCamelCase:     Tuple = 1_6  ):






 """simple docstring"""


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

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

 def collate_fn(UpperCamelCase:     Union[str, Any]  ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __lowerCAmelCase         =							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":
   __lowerCAmelCase         =							1_6
  elif accelerator.mixed_precision != "no":
   __lowerCAmelCase         =							8
  else:
   __lowerCAmelCase         =							None

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

 # Instantiate dataloaders.
 __lowerCAmelCase         =							DataLoader(
     tokenized_datasets["train"]			,							shuffle=__snake_case			,							collate_fn=__snake_case			,							batch_size=__snake_case  )
 __lowerCAmelCase         =							DataLoader(
     tokenized_datasets["validation"]			,							shuffle=__snake_case			,							collate_fn=__snake_case			,							batch_size=__snake_case  )

 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

     UpperCamelCase_   	=	mocked_dataloaders  # noqa: F811






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Dict  ):






 """simple docstring"""


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

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

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=__snake_case  )
 def inner_training_loop(UpperCamelCase:     Optional[int]  ):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(__snake_case  )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __lowerCAmelCase         =							AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,							return_dict=__snake_case  )

  # 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         =							model.to(accelerator.device  )

  # Instantiate optimizer
  __lowerCAmelCase         =							AdamW(params=model.parameters()			,							lr=__snake_case  )
  __lowerCAmelCase   ,						__lowerCAmelCase         =							get_dataloaders(__snake_case			,							__snake_case  )

  # Instantiate scheduler
  __lowerCAmelCase         =							get_linear_schedule_with_warmup(
      optimizer=__snake_case			,							num_warmup_steps=1_0_0			,							num_training_steps=(len(__snake_case  ) * num_epochs)			,							)

  # 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         =							accelerator.prepare(
      __snake_case			,							__snake_case			,							__snake_case			,							__snake_case			,							__snake_case  )

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

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

   __lowerCAmelCase         =							metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(F"epoch {epoch}:"			,							__snake_case  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()






def 		_UpperCAmelCase   (	):






 """simple docstring"""


 __lowerCAmelCase         =							argparse.ArgumentParser(description="Simple example of training script."  )
 parser.add_argument(
     "--mixed_precision"			,							type=__snake_case			,							default=__snake_case			,							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."  )
 __lowerCAmelCase         =							parser.parse_args()
 __lowerCAmelCase         =							{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
 training_function(__snake_case			,							__snake_case  )


if __name__ == "__main__":
     main()








from __future__ import annotations

UpperCamelCase_   	=	list[tuple[int, int]]

UpperCamelCase_   	=	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCamelCase_   	=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class       a		:







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  float      ,				snake_case__  :  Node | None      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							pos_x
								__lowerCAmelCase         =							pos_y
								__lowerCAmelCase         =							(pos_y, pos_x)
								__lowerCAmelCase         =							goal_x
								__lowerCAmelCase         =							goal_y
								__lowerCAmelCase         =							g_cost
								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							self.calculate_heuristic()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							abs(self.pos_x - self.goal_x )
								__lowerCAmelCase         =							abs(self.pos_y - self.goal_y )
								return dx + dy







							def __lt__(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								return self.f_cost < other.f_cost

class       a		:







							def __init__(						self  :  Dict      ,				snake_case__  :  tuple[int, int]      ,				snake_case__  :  tuple[int, int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Node(start[1]      ,				start[0]      ,				goal[1]      ,				goal[0]      ,				0      ,				snake_case__ )
								__lowerCAmelCase         =							Node(goal[1]      ,				goal[0]      ,				goal[1]      ,				goal[0]      ,				99_999      ,				snake_case__ )

								__lowerCAmelCase         =							[self.start]
								__lowerCAmelCase         =							[]

								__lowerCAmelCase         =							False







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__lowerCAmelCase         =							self.open_nodes.pop(0 )

									if current_node.pos == self.target.pos:
										__lowerCAmelCase         =							True
										return self.retrace_path(snake_case__ )

									self.closed_nodes.append(snake_case__ )
									__lowerCAmelCase         =							self.get_successors(snake_case__ )

									for child_node in successors:
										if child_node in self.closed_nodes:
											continue

										if child_node not in self.open_nodes:
											self.open_nodes.append(snake_case__ )
										else:
											# retrieve the best current path
											__lowerCAmelCase         =							self.open_nodes.pop(self.open_nodes.index(snake_case__ ) )

											if child_node.g_cost < better_node.g_cost:
												self.open_nodes.append(snake_case__ )
											else:
												self.open_nodes.append(snake_case__ )

								if not self.reached:
									return [self.start.pos]
								return None







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Node ):
								"""simple docstring"""







								__lowerCAmelCase         =							[]
								for action in delta:
									__lowerCAmelCase         =							parent.pos_x + action[1]
									__lowerCAmelCase         =							parent.pos_y + action[0]

									if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case__ ) - 1):
										continue

									if grid[pos_y][pos_x] != 0:
										continue

									successors.append(
									    Node(
									        snake_case__      ,				snake_case__      ,				self.target.pos_y      ,				self.target.pos_x      ,				parent.g_cost + 1      ,				snake_case__      ,				) )
								return successors







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Node | None ):
								"""simple docstring"""







								__lowerCAmelCase         =							node
								__lowerCAmelCase         =							[]
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x) )
									__lowerCAmelCase         =							current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
					UpperCamelCase_   	=	(0, 0)
					UpperCamelCase_   	=	(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("------")

					UpperCamelCase_   	=	GreedyBestFirst(init, goal)
					UpperCamelCase_   	=	greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															UpperCamelCase_   	=	2

										for elem in grid:
															print(elem)






