






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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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      				=							3_2





def 		A_			(		lowercase_					,       lowercase_ = 1_6	)							->Optional[int]:







 """simple docstring"""

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

 def tokenize_function(lowercase_	):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE							  =						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():
  SCREAMING_SNAKE_CASE							  =						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
 SCREAMING_SNAKE_CASE							  =						tokenized_datasets.rename_column('label'					,       'labels'	)

 def collate_fn(lowercase_	):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   SCREAMING_SNAKE_CASE							  =						1_6
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE							  =						8
  else:
   SCREAMING_SNAKE_CASE							  =						None

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

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

      __UpperCAmelCase      				=							mocked_dataloaders  # noqa: F811





def 		A_			(		lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

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

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

 # New Code #
 # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
 # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
 # half each time. From this, we can calculate the number of gradient accumulation steps needed
 # and modify the Accelerator object as a result
 @find_executable_batch_size(starting_batch_size=int(lowercase_	)	)
 def inner_training_loop(lowercase_	):
  # Since we need to modify the outside accelerator object, we need to bring it
  # to the local scope
  nonlocal accelerator

  # We can calculate the number of gradient accumulation steps based on the current
  # batch size vs the starting batch size
  SCREAMING_SNAKE_CASE							  =						observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  SCREAMING_SNAKE_CASE							  =						num_gradient_accumulation_steps

  # Next we need to free all of the stored model references in the Accelerator each time
  accelerator.free_memory()

  # And set the seed so our results are reproducable each reset
  set_seed(lowercase_	)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE							  =						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).
  SCREAMING_SNAKE_CASE							  =						model.to(accelerator.device	)

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE							  =						AdamW(params=model.parameters()					,       lr=lowercase_	)
  SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						get_dataloaders(lowercase_					,       lowercase_	)

  # Instantiate scheduler
  SCREAMING_SNAKE_CASE							  =						get_linear_schedule_with_warmup(
      optimizer=lowercase_					,       num_warmup_steps=1_0_0					,       num_training_steps=(len(lowercase_	) * 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.
  SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						accelerator.prepare(
      lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_	)

  # Now we train the model
  for epoch in range(lowercase_	):
   model.train()
   for step, batch in enumerate(lowercase_	):
    # And perform gradient accumulation
    with accelerator.accumulate(lowercase_	):
     # We could avoid this line since we set the accelerator with `device_placement=True`.
     batch.to(accelerator.device	)
     SCREAMING_SNAKE_CASE							  =						model(**lowercase_	)
     SCREAMING_SNAKE_CASE							  =						outputs.loss
     accelerator.backward(lowercase_	)
     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`.
    batch.to(accelerator.device	)
    with torch.no_grad():
     SCREAMING_SNAKE_CASE							  =						model(**lowercase_	)
    SCREAMING_SNAKE_CASE							  =						outputs.logits.argmax(dim=-1	)
    SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						accelerator.gather_for_metrics((predictions, batch['labels'])	)
    metric.add_batch(
        predictions=lowercase_					,       references=lowercase_					,       )

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

    # 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 		A_			(		)							->Tuple:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						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.'	)
 SCREAMING_SNAKE_CASE							  =						parser.parse_args()
 # New Code #
 # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
 SCREAMING_SNAKE_CASE							  =						{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 2_5_6}
 training_function(lowercase_					,       lowercase_	)


if __name__ == "__main__":
      main()












import math
import sys
class 					a_:



     """simple docstring"""



     def __init__(						self			:					Optional[int])    ->					List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						{}  # To store the pos of node in array





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Optional[int]:

      """simple docstring"""
      return self.cur_size == 0





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					str)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.left(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.right(lowerCAmelCase__)
      if lc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						lc
      else:
       SCREAMING_SNAKE_CASE							  =						idx
      if rc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						rc
      if smallest != idx:
       self.swap(lowerCAmelCase__       ,       lowerCAmelCase__)
       self.min_heapify(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					Tuple)    ->					Optional[Any]:

      """simple docstring"""
      # Inserts a node into the Priority Queue
      SCREAMING_SNAKE_CASE							  =						self.cur_size
      self.cur_size += 1
      self.array.append((sys.maxsize, tup[1]))
      self.decrease_key((sys.maxsize, tup[1])       ,       tup[0])





     def   __UpperCamelCase		(						self			:					Dict)    ->					Optional[Any]:

      """simple docstring"""
      # Removes and returns the min element at top of priority queue
      SCREAMING_SNAKE_CASE							  =						self.array[0][1]
      SCREAMING_SNAKE_CASE							  =						self.array[self.cur_size - 1]
      self.cur_size -= 1
      self.min_heapify(1)
      del self.pos[min_node]
      return min_node





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Dict)    ->					int:

      """simple docstring"""
      # returns the index of left child
      return 2 * i + 1





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					List[str])    ->					Optional[Any]:

      """simple docstring"""
      # returns the index of right child
      return 2 * i + 2





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					List[Any])    ->					Dict:

      """simple docstring"""
      # returns the index of parent
      return math.floor(i / 2)





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					List[str]       ,       lowerCAmelCase__			:					Any)    ->					List[str]:

      """simple docstring"""
      # swaps array elements at indices i and j
      # update the pos{}
      SCREAMING_SNAKE_CASE							  =						j
      SCREAMING_SNAKE_CASE							  =						i
      SCREAMING_SNAKE_CASE							  =						self.array[i]
      SCREAMING_SNAKE_CASE							  =						self.array[j]
      SCREAMING_SNAKE_CASE							  =						temp





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Dict       ,       lowerCAmelCase__			:					Optional[Any])    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      SCREAMING_SNAKE_CASE							  =						(new_d, tup[1])
      while idx > 0 and self.array[self.par(lowerCAmelCase__)][0] > self.array[idx][0]:
       self.swap(lowerCAmelCase__       ,       self.par(lowerCAmelCase__))
       SCREAMING_SNAKE_CASE							  =						self.par(lowerCAmelCase__)

class 					a_:



     """simple docstring"""



     def __init__(						self			:					Any       ,       lowerCAmelCase__			:					str)    ->					str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						{}  # To store graph: u -> (v,w)
      SCREAMING_SNAKE_CASE							  =						num  # Number of nodes in graph
      # To store the distance from source vertex
      SCREAMING_SNAKE_CASE							  =						[0] * self.num_nodes
      SCREAMING_SNAKE_CASE							  =						[-1] * self.num_nodes  # To store the path





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					List[Any])    ->					Tuple:

      """simple docstring"""
      #  Edge going from node u to v and v to u with weight w
      # u (w)-> v, v (w) -> u
      # Check if u already in graph
      if u in self.adjList:
       self.adjList[u].append((v, w))
      else:
       SCREAMING_SNAKE_CASE							  =						[(v, w)]

      # Assuming undirected graph
      if v in self.adjList:
       self.adjList[v].append((u, w))
      else:
       SCREAMING_SNAKE_CASE							  =						[(u, w)]





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Tuple:

      """simple docstring"""
      # u -> v(w)
      for u in self.adjList:
       print(lowerCAmelCase__       ,       '->'       ,       ' -> '.join(str(f'''{v}({w})''') for v, w in self.adjList[u]))





     def   __UpperCamelCase		(						self			:					int       ,       lowerCAmelCase__			:					List[str])    ->					List[Any]:

      """simple docstring"""
      # Flush old junk values in par[]
      SCREAMING_SNAKE_CASE							  =						[-1] * self.num_nodes
      # src is the source node
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						PriorityQueue()
      q.insert((0, src))  # (dist from src, node)
      for u in self.adjList:
       if u != src:
        SCREAMING_SNAKE_CASE							  =						sys.maxsize  # Infinity
        SCREAMING_SNAKE_CASE							  =						-1

      while not q.is_empty():
       SCREAMING_SNAKE_CASE							  =						q.extract_min()  # Returns node with the min dist from source
       # Update the distance of all the neighbours of u and
       # if their prev dist was INFINITY then push them in Q
       for v, w in self.adjList[u]:
        SCREAMING_SNAKE_CASE							  =						self.dist[u] + w
        if self.dist[v] > new_dist:
         if self.dist[v] == sys.maxsize:
          q.insert((new_dist, v))
         else:
          q.decrease_key((self.dist[v], v)       ,       lowerCAmelCase__)
         SCREAMING_SNAKE_CASE							  =						new_dist
         SCREAMING_SNAKE_CASE							  =						u

        # Show the shortest distances from src
      self.show_distances(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					List[str])    ->					Tuple:

      """simple docstring"""
      print(f'''Distance from node: {src}''')
      for u in range(self.num_nodes):
       print(f'''Node {u} has distance: {self.dist[u]}''')





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Any)    ->					Optional[int]:

      """simple docstring"""
      # To show the shortest path from src to dest
      # WARNING: Use it *after* calling dijkstra
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						dest
      # Backtracking from dest to src
      while self.par[temp] != -1:
       path.append(lowerCAmelCase__)
       if temp != src:
        for v, w in self.adjList[temp]:
         if v == self.par[temp]:
          cost += w
          break
       SCREAMING_SNAKE_CASE							  =						self.par[temp]
      path.append(lowerCAmelCase__)
      path.reverse()

      print(f'''----Path to reach {dest} from {src}----''')
      for u in path:
       print(f'''{u}'''       ,       end=' ')
       if u != dest:
        print('-> '       ,       end='')

      print('\nTotal cost of path: '       ,       lowerCAmelCase__)


if __name__ == "__main__":
      __UpperCAmelCase      				=							Graph(9)
      graph.add_edge(0, 1, 4)
      graph.add_edge(0, 7, 8)
      graph.add_edge(1, 2, 8)
      graph.add_edge(1, 7, 1_1)
      graph.add_edge(2, 3, 7)
      graph.add_edge(2, 8, 2)
      graph.add_edge(2, 5, 4)
      graph.add_edge(3, 4, 9)
      graph.add_edge(3, 5, 1_4)
      graph.add_edge(4, 5, 1_0)
      graph.add_edge(5, 6, 2)
      graph.add_edge(6, 7, 1)
      graph.add_edge(6, 8, 6)
      graph.add_edge(7, 8, 7)
      graph.show_graph()
      graph.dijkstra(0)
      graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21




