


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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE      :			Dict      				=   16
_SCREAMING_SNAKE_CASE      :			Dict      				=   32
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_ = 16    ):



     """simple docstring"""






     snake_case					=    AutoTokenizer.from_pretrained('''bert-base-cased'''    )
     snake_case					=    load_dataset('''glue'''							,'''mrpc'''    )

     def tokenize_function(UpperCamelCase_    ):
          # max_length=None => use the model max length (it's actually the default)
          snake_case					=    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():
          snake_case					=    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
     snake_case					=    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.
          snake_case					=    1_28 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					=    16
          elif accelerator.mixed_precision != "no":
               snake_case					=    8
          else:
               snake_case					=    None

          return tokenizer.pad(
              UpperCamelCase_							,padding='''longest'''							,max_length=UpperCamelCase_							,pad_to_multiple_of=UpperCamelCase_							,return_tensors='''pt'''							,)

     # Instantiate dataloaders.
     snake_case					=    DataLoader(
         tokenized_datasets['''train''']							,shuffle=UpperCamelCase_							,collate_fn=UpperCamelCase_							,batch_size=UpperCamelCase_    )
     snake_case					=    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

 _SCREAMING_SNAKE_CASE      :			List[str]      				=   mocked_dataloaders  # noqa: F811
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,UpperCamelCase_    ) == "1":
          snake_case					=    2
     # New Code #
     snake_case					=    int(args.gradient_accumulation_steps    )
     snake_case					=    int(args.local_sgd_steps    )
     # Initialize accelerator
     snake_case					=    Accelerator(
         cpu=args.cpu							,mixed_precision=args.mixed_precision							,gradient_accumulation_steps=UpperCamelCase_    )
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''    )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     snake_case					=    config['''lr''']
     snake_case					=    int(config['''num_epochs''']    )
     snake_case					=    int(config['''seed''']    )
     snake_case					=    int(config['''batch_size''']    )

     snake_case					=    evaluate.load('''glue'''							,'''mrpc'''    )

     set_seed(UpperCamelCase_    )
     snake_case			,  snake_case					=    get_dataloaders(UpperCamelCase_							,UpperCamelCase_    )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     snake_case					=    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).
     snake_case					=    model.to(accelerator.device    )

     # Instantiate optimizer
     snake_case					=    AdamW(params=model.parameters()							,lr=UpperCamelCase_    )

     # Instantiate scheduler
     snake_case					=    get_linear_schedule_with_warmup(
         optimizer=UpperCamelCase_							,num_warmup_steps=1_00							,num_training_steps=(len(UpperCamelCase_    ) * 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.
     snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    accelerator.prepare(
         UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

     # Now we train the model
     for epoch in range(UpperCamelCase_    ):
          model.train()
          with LocalSGD(
              accelerator=UpperCamelCase_							,model=UpperCamelCase_							,local_sgd_steps=UpperCamelCase_							,enabled=local_sgd_steps is not None    ) as local_sgd:
               for step, batch in enumerate(UpperCamelCase_    ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device    )
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(UpperCamelCase_    ):
                         snake_case					=    model(**UpperCamelCase_    )
                         snake_case					=    output.loss
                         accelerator.backward(UpperCamelCase_    )
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

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

          snake_case					=    metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''							,UpperCamelCase_    )
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    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.'''							,)
     # New Code #
     parser.add_argument(
         '''--gradient_accumulation_steps'''							,type=UpperCamelCase_							,default=1							,help='''The number of minibatches to be ran before gradients are accumulated.'''							,)
     parser.add_argument(
         '''--local_sgd_steps'''							,type=UpperCamelCase_							,default=8							,help='''Number of local SGD steps or None to disable local SGD'''    )
     parser.add_argument('''--cpu'''							,action='''store_true'''							,help='''If passed, will train on the CPU.'''    )
     snake_case					=    parser.parse_args()
     snake_case					=    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(UpperCamelCase_							,UpperCamelCase_    )


if __name__ == "__main__":
 main()








from __future__ import annotations

import time

_SCREAMING_SNAKE_CASE      :			List[Any]      				=   list[tuple[int, int]]

_SCREAMING_SNAKE_CASE      :			Any      				=   [
    [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],
]

_SCREAMING_SNAKE_CASE      :			Any      				=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    pos_x
         snake_case					=    pos_y
         snake_case					=    (pos_y, pos_x)
         snake_case					=    goal_x
         snake_case					=    goal_y
         snake_case					=    parent






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    Node(start[1]      , start[0]      , goal[1]      , goal[0]      , __snake_case   )
         snake_case					=    Node(goal[1]      , goal[0]      , goal[1]      , goal[0]      , __snake_case   )

         snake_case					=    [self.start]
         snake_case					=    False
    def 		a_      (	self   ):
         while self.node_queue:
              snake_case					=    self.node_queue.pop(0   )

              if current_node.pos == self.target.pos:
                   snake_case					=    True
                   return self.retrace_path(__snake_case   )

              snake_case					=    self.get_successors(__snake_case   )

              for node in successors:
                   self.node_queue.append(__snake_case   )

         if not self.reached:
              return [self.start.pos]
         return None
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         for action in delta:
              snake_case					=    parent.pos_x + action[1]
              snake_case					=    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      , __snake_case   )   )
         return successors




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    node
         snake_case					=    []
         while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x)   )
              snake_case					=    current_node.parent
         path.reverse()
         return path






class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case   ):
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    BreadthFirstSearch(__snake_case      , __snake_case   )
         snake_case					=    False
    def 		a_      (	self   ):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
              snake_case					=    self.fwd_bfs.node_queue.pop(0   )
              snake_case					=    self.bwd_bfs.node_queue.pop(0   )

              if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case					=    True
                   return self.retrace_bidirectional_path(
                       __snake_case      , __snake_case   )

              snake_case					=    current_bwd_node
              snake_case					=    current_fwd_node

              snake_case					=    {
                  self.fwd_bfs: self.fwd_bfs.get_successors(__snake_case   ),
                  self.bwd_bfs: self.bwd_bfs.get_successors(__snake_case   ),
              }

              for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                        bfs.node_queue.append(__snake_case   )

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




    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    self.fwd_bfs.retrace_path(__snake_case   )
         snake_case					=    self.bwd_bfs.retrace_path(__snake_case   )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case					=    fwd_path + bwd_path
         return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   (0, 0)
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   (len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   BreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   bfs.search()
 _SCREAMING_SNAKE_CASE      :			int      				=   time.time() - start_bfs_time

 print("Unidirectional BFS computation time : ", bfs_time)

 _SCREAMING_SNAKE_CASE      :			Any      				=   time.time()
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   BidirectionalBreadthFirstSearch(init, goal)
 _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   bd_bfs.search()
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   time.time() - start_bd_bfs_time

 print("Bidirectional BFS computation time : ", bd_bfs_time)




