'''simple docstring'''


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

snake_case__     :			Tuple      			=  32


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							str = 16   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      AutoTokenizer.from_pretrained('bert-base-cased'   )
     UpperCAmelCase_							:   Union[str, Any]   =      load_dataset('glue'	,     'mrpc'   )

     def tokenize_function(lowerCamelCase_					:							Tuple   ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_							:   int   =      tokenizer(examples['sentence1']	,     examples['sentence2']	,     truncation=SCREAMING_SNAKE_CASE_	,     max_length=SCREAMING_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():
          UpperCAmelCase_							:   Optional[int]   =      datasets.map(
              SCREAMING_SNAKE_CASE_	,     batched=SCREAMING_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
     UpperCAmelCase_							:   Tuple   =      tokenized_datasets.rename_column('label'	,     'labels'   )

     def collate_fn(lowerCamelCase_					:							Optional[Any]   ):
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               UpperCAmelCase_							:   Any   =      16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase_							:   Any   =      8
          else:
               UpperCAmelCase_							:   str   =      None

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

     # Instantiate dataloaders.
     UpperCAmelCase_							:   Optional[int]   =      DataLoader(
         tokenized_datasets['train']	,     shuffle=SCREAMING_SNAKE_CASE_	,     collate_fn=SCREAMING_SNAKE_CASE_	,     batch_size=SCREAMING_SNAKE_CASE_   )
     UpperCAmelCase_							:   Any   =      DataLoader(
         tokenized_datasets['validation']	,     shuffle=SCREAMING_SNAKE_CASE_	,     collate_fn=SCREAMING_SNAKE_CASE_	,     batch_size=SCREAMING_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

 snake_case__     :			List[str]      			=  mocked_dataloaders  # noqa: F811


def 					_lowerCamelCase    (      lowerCamelCase_					:							int	,     lowerCamelCase_					:							str   ):



     """simple docstring"""





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

     UpperCAmelCase_							:   Any   =      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(SCREAMING_SNAKE_CASE_   )   )
     def inner_training_loop(lowerCamelCase_					:							Union[str, Any]   ):
          # 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
          UpperCAmelCase_							:   Any   =      observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          UpperCAmelCase_							:   List[Any]   =      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(SCREAMING_SNAKE_CASE_   )

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          UpperCAmelCase_							:   Dict   =      AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,     return_dict=SCREAMING_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).
          UpperCAmelCase_							:   List[Any]   =      model.to(accelerator.device   )

          # Instantiate optimizer
          UpperCAmelCase_							:   Optional[int]   =      AdamW(params=model.parameters()	,     lr=SCREAMING_SNAKE_CASE_   )
          UpperCAmelCase_   ,			UpperCAmelCase_							:   Dict   =      get_dataloaders(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_   )

          # Instantiate scheduler
          UpperCAmelCase_							:   Optional[int]   =      get_linear_schedule_with_warmup(
              optimizer=SCREAMING_SNAKE_CASE_	,     num_warmup_steps=100	,     num_training_steps=(len(SCREAMING_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.
          UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[int]   =      accelerator.prepare(
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_   )

          # Now we train the model
          for epoch in range(SCREAMING_SNAKE_CASE_   ):
               model.train()
               for step, batch in enumerate(SCREAMING_SNAKE_CASE_   ):
                    # And perform gradient accumulation
                    with accelerator.accumulate(SCREAMING_SNAKE_CASE_   ):
                         # We could avoid this line since we set the accelerator with `device_placement=True`.
                         batch.to(accelerator.device   )
                         UpperCAmelCase_							:   Union[str, Any]   =      model(**SCREAMING_SNAKE_CASE_   )
                         UpperCAmelCase_							:   List[str]   =      outputs.loss
                         accelerator.backward(SCREAMING_SNAKE_CASE_   )
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

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

               UpperCAmelCase_							:   Any   =      metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'''epoch {epoch}:'''	,     SCREAMING_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 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   Optional[int]   =      argparse.ArgumentParser(description='Simple example of training script.'   )
     parser.add_argument(
         '--mixed_precision'	,     type=SCREAMING_SNAKE_CASE_	,     default=SCREAMING_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.'   )
     UpperCAmelCase_							:   int   =      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
     UpperCAmelCase_							:   Tuple   =      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
     training_function(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_   )


if __name__ == "__main__":
 main()








'''simple docstring'''



UpperCamelCase__					:						Tuple											=     [
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "bh-e12", "cd-e2", "ce-e4",
     "de-e1", "df-e8", "dg-e5", "dh-e10", "ef-e3", "eg-e2", "fg-e6", "gh-e6", "hi-e3"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "cd-e2", "de-e1", "df-e8",
     "ef-e3", "eg-e2", "fg-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "de-e1", "df-e8", "dg-e5", "ef-e3", "eg-e2",
     "eh-e12", "fg-e6", "fh-e10", "gh-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "bh-e12", "cd-e2", "df-e8", "dh-e10"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "cd-e2", "ce-e4", "de-e1", "df-e8",
     "dg-e5", "ef-e3", "eg-e2", "fg-e6"]
]
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = set()
   for row in edge_array:
      for item in row:
         distinct_edge.add(item[0]  )
   return list(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = ["""0"""] * len(SCREAMING_SNAKE_CASE_  )
   for i, row in enumerate(SCREAMING_SNAKE_CASE_  ):
      for item in row:
         if distinct_edge in item[0]:
            _SCREAMING_SNAKE_CASE          = """1"""
            break
   return "".join(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = get_distinct_edge(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = {}

   for item in distinct_edge:
      _SCREAMING_SNAKE_CASE          = get_bitcode(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
      # print('bit',bit)
      # bt=''.join(bit)
      _SCREAMING_SNAKE_CASE          = bit.count("""1"""  )
      _SCREAMING_SNAKE_CASE          = [s, bit]
   # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
   _SCREAMING_SNAKE_CASE          = [
       [k, v[0], v[1]]
       for k, v in sorted(frequency_table.items()			,					key=lambda SCREAMING_SNAKE_CASE_  : v[1][0]			,					reverse=SCREAMING_SNAKE_CASE_  )
   ]
   return sorted_frequency_table
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = {}
   for _, item in enumerate(SCREAMING_SNAKE_CASE_  ):
      nodes.setdefault(item[2]			,					[]  ).append(item[0]  )
   return nodes
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = {}
   for key, value in nodes.items():
      _SCREAMING_SNAKE_CASE          = value
   return cluster
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   str:




   """simple docstring"""
   return [i * 1_00 / len(SCREAMING_SNAKE_CASE_  ) for i in cluster]
def       lowerCAmelCase_		( )   ->   None:




   """simple docstring"""
   print("""\nNodes\n"""  )
   for key, value in nodes.items():
      print(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
   print("""\nSupport\n"""  )
   print(SCREAMING_SNAKE_CASE_  )
   print("""\n Cluster \n"""  )
   for key, value in sorted(cluster.items()			,					reverse=SCREAMING_SNAKE_CASE_  ):
      print(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
   print("""\n Graph\n"""  )
   for key, value in graph.items():
      print(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
   print("""\n Edge List of Frequent subgraphs \n"""  )
   for edge_list in freq_subgraph_edge_list:
      print(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   for i in cluster[ca]:
      _SCREAMING_SNAKE_CASE          = 0
      _SCREAMING_SNAKE_CASE          = ca + 1
      while ca < max(cluster.keys()  ):
         for j in cluster[ca]:
            if int(SCREAMING_SNAKE_CASE_			,					2  ) & int(SCREAMING_SNAKE_CASE_			,					2  ) == int(SCREAMING_SNAKE_CASE_			,					2  ):
               if tuple(nodes[i]  ) in graph:
                  graph[tuple(nodes[i]  )].append(nodes[j]  )
               else:
                  _SCREAMING_SNAKE_CASE          = [nodes[j]]
               count += 1
         if count == 0:
            _SCREAMING_SNAKE_CASE          = ca + 1
         else:
            break
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = cluster[max(cluster.keys()  )]
   _SCREAMING_SNAKE_CASE          = """Header"""
   _SCREAMING_SNAKE_CASE          = {}
   for i in x:
      if (["Header"],) in graph:
         graph[(["Header"],)].append(x[i]  )
      else:
         _SCREAMING_SNAKE_CASE          = [x[i]]
   for i in x:
      _SCREAMING_SNAKE_CASE          = [["""Header"""]]
   _SCREAMING_SNAKE_CASE          = 1
   while i < max(SCREAMING_SNAKE_CASE_  ) - 1:
      create_edge(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE          = i + 1
   return graph
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=None  )   ->   List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = (path or []) + [start]
   if start == end:
      paths.append(SCREAMING_SNAKE_CASE_  )
   for node in graph[start]:
      if tuple(SCREAMING_SNAKE_CASE_  ) not in path:
         my_dfs(SCREAMING_SNAKE_CASE_			,					tuple(SCREAMING_SNAKE_CASE_  )			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = int(s / 1_00 * (len(SCREAMING_SNAKE_CASE_  ) - 1)  )
   for i in cluster[k]:
      my_dfs(SCREAMING_SNAKE_CASE_			,					tuple(cluster[k][i]  )			,					(["""Header"""],)  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = []
   for edges in paths:
      _SCREAMING_SNAKE_CASE          = []
      for j in range(len(SCREAMING_SNAKE_CASE_  ) - 1  ):
         _SCREAMING_SNAKE_CASE          = list(edges[j]  )
         for e in temp:
            _SCREAMING_SNAKE_CASE          = (e[0], e[1])
            el.append(SCREAMING_SNAKE_CASE_  )
      freq_sub_el.append(SCREAMING_SNAKE_CASE_  )
   return freq_sub_el
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   for i in range(len(SCREAMING_SNAKE_CASE_  )  ):
      for j in range(len(edge_array[i]  )  ):
         _SCREAMING_SNAKE_CASE          = edge_array[i][j].split("""-"""  )
         _SCREAMING_SNAKE_CASE          = t


if __name__ == "__main__":
     preprocess(edge_array)
     UpperCamelCase__					:						List[Any]											=     get_frequency_table(edge_array)
     UpperCamelCase__					:						List[Any]											=     get_nodes(frequency_table)
     UpperCamelCase__					:						List[str]											=     get_cluster(nodes)
     UpperCamelCase__					:						List[Any]											=     get_support(cluster)
     UpperCamelCase__					:						Union[str, Any]											=     construct_graph(cluster, nodes)
     find_freq_subgraph_given_support(60, cluster, graph)
     UpperCamelCase__					:						list											=     []
     UpperCamelCase__					:						int											=     freq_subgraphs_edge_list(paths)
     print_all()






