code: 
  
  
  
  
  
  
  import argparse
  import gc
  import os
  import threading
  
  import evaluate
  import psutil
  import torch
  from datasets import load_dataset
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
  from torch.utils.data import DataLoader
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
      set_seed,
  )
  
  from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate
  #
  # 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)
  #   - FSDP
  #
  # This example also demonstrates the checkpointing and sharding capabilities
  #
  # 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 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[Any] ):
      return int(x / 2**20 )
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        def __enter__(       self				)       ->   Optional[Any]:
            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
            UpperCamelCase :Union[str, Any]										=     torch.cuda.memory_allocated()
            UpperCamelCase :int										=     psutil.Process()
  
            UpperCamelCase :Union[str, Any]										=     self.cpu_mem_used()
            UpperCamelCase :Tuple										=     True
            UpperCamelCase :Optional[int]										=     threading.Thread(target=self.peak_monitor_func				)
            UpperCamelCase :Union[str, Any]										=     True
            peak_monitor_thread.start()
            return self
        def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
            return self.process.memory_info().rss
        def 					UpperCAmelCase		(       self				)       ->   Any:
            UpperCamelCase :Dict										=     -1
  
            while True:
                UpperCamelCase :Union[str, Any]										=     max(self.cpu_mem_used()					,       self.cpu_peak				)
  
                # can't sleep or will not catch the peak right (this comment is here on purpose)
                # time.sleep(0.001) # 1msec
  
                if not self.peak_monitoring:
                    break
  
  
  
  
        def __exit__(       self					,       *SCREAMING_SNAKE_CASE_				)       ->   List[str]:
            UpperCamelCase :List[str]										=     False
  
            gc.collect()
            torch.cuda.empty_cache()
            UpperCamelCase :List[Any]										=     torch.cuda.memory_allocated()
            UpperCamelCase :Dict										=     torch.cuda.max_memory_allocated()
            UpperCamelCase :Union[str, Any]										=     bamb(self.end - self.begin				)
            UpperCamelCase :Any										=     bamb(self.peak - self.begin				)
  
            UpperCamelCase :List[Any]										=     self.cpu_mem_used()
            UpperCamelCase :str										=     bamb(self.cpu_end - self.cpu_begin				)
            UpperCamelCase :Optional[int]										=     bamb(self.cpu_peak - self.cpu_begin				)
            # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
  
  
  # 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 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			Dict ):
      # For testing only
      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,						SCREAMING_SNAKE_CASE__ ) == "1":
          UpperCamelCase :Any										=     2
  
      # New Code #
      # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
      UpperCamelCase :Dict										=     FullyShardedDataParallelPlugin(
          state_dict_config=FullStateDictConfig(offload_to_cpu=SCREAMING_SNAKE_CASE__  ,						ranka_only=SCREAMING_SNAKE_CASE__ )  ,						optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=SCREAMING_SNAKE_CASE__  ,						ranka_only=SCREAMING_SNAKE_CASE__ )  ,						)
  
      # Initialize accelerator
      if args.with_tracking:
          UpperCamelCase :Dict										=     Accelerator(
              cpu=args.cpu  ,						mixed_precision=args.mixed_precision  ,						log_with='''wandb'''  ,						project_dir=args.logging_dir  ,						fsdp_plugin=SCREAMING_SNAKE_CASE__  ,						)
      else:
          UpperCamelCase :int										=     Accelerator(fsdp_plugin=SCREAMING_SNAKE_CASE__ )
      accelerator.print(accelerator.distributed_type )
  
      if hasattr(args.checkpointing_steps  ,						'''isdigit''' ):
          if args.checkpointing_steps == "epoch":
              UpperCamelCase :Tuple										=     args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
              UpperCamelCase :Optional[Any]										=     int(args.checkpointing_steps )
          else:
              raise ValueError(
                  F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
      else:
          UpperCamelCase :int										=     None
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase :Any										=     config['''lr''']
      UpperCamelCase :Tuple										=     int(config['''num_epochs'''] )
      UpperCamelCase :Union[str, Any]										=     int(config['''seed'''] )
      UpperCamelCase :Union[str, Any]										=     int(config['''batch_size'''] )
  
      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
          UpperCamelCase :Tuple										=     vars(SCREAMING_SNAKE_CASE__ )
          accelerator.init_trackers('''fsdp_glue_no_trainer'''  ,						SCREAMING_SNAKE_CASE__ )
  
      UpperCamelCase :Optional[Any]										=     AutoTokenizer.from_pretrained(args.model_name_or_path )
      UpperCamelCase :List[str]										=     load_dataset('''glue'''  ,						'''mrpc''' )
      UpperCamelCase :List[str]										=     evaluate.load('''glue'''  ,						'''mrpc''' )
  
      def tokenize_function(SCREAMING_SNAKE_CASE__						:			List[Any] ):
          # 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 :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 :str										=     tokenized_datasets.rename_column('''label'''  ,						'''labels''' )
  
      # If the batch size is too big we use gradient accumulation
      UpperCamelCase :Union[str, Any]										=     1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          UpperCamelCase :Union[str, Any]										=     batch_size // MAX_GPU_BATCH_SIZE
          UpperCamelCase :Dict										=     MAX_GPU_BATCH_SIZE
  
      def collate_fn(SCREAMING_SNAKE_CASE__						:			List[Any] ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCamelCase :Union[str, 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":
              UpperCamelCase :List[Any]										=     16
          elif accelerator.mixed_precision != "no":
              UpperCamelCase :int										=     8
          else:
              UpperCamelCase :Dict										=     None
  
          return tokenizer.pad(
              SCREAMING_SNAKE_CASE__  ,						padding='''longest'''  ,						max_length=SCREAMING_SNAKE_CASE__  ,						pad_to_multiple_of=SCREAMING_SNAKE_CASE__  ,						return_tensors='''pt'''  ,						)
  
      # Instantiate dataloaders.
      UpperCamelCase :Optional[Any]										=     DataLoader(
          tokenized_datasets['''train''']  ,						shuffle=SCREAMING_SNAKE_CASE__  ,						collate_fn=SCREAMING_SNAKE_CASE__  ,						batch_size=SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :int										=     DataLoader(
          tokenized_datasets['''validation''']  ,						shuffle=SCREAMING_SNAKE_CASE__  ,						collate_fn=SCREAMING_SNAKE_CASE__  ,						batch_size=SCREAMING_SNAKE_CASE__ )
  
      set_seed(SCREAMING_SNAKE_CASE__ )
  
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase :List[Any]										=     AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path  ,						return_dict=SCREAMING_SNAKE_CASE__  ,						low_cpu_mem_usage=SCREAMING_SNAKE_CASE__ )
  
      # New Code #
      # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
      UpperCamelCase :int										=     accelerator.prepare(SCREAMING_SNAKE_CASE__ )
      accelerator.print(SCREAMING_SNAKE_CASE__ )
  
      # Instantiate optimizer
      # New Code #
      # For FSDP feature, at present it doesn't support multiple parameter groups,
      # so we need to create a single parameter group for the whole model
      UpperCamelCase :str										=     torch.optim.AdamW(params=model.parameters()  ,						lr=SCREAMING_SNAKE_CASE__  ,						weight_decay=2e-4 )
  
      # Instantiate scheduler
      UpperCamelCase :Optional[Any]										=     get_linear_schedule_with_warmup(
          optimizer=SCREAMING_SNAKE_CASE__  ,						num_warmup_steps=10  ,						num_training_steps=(len(SCREAMING_SNAKE_CASE__ ) * num_epochs) // gradient_accumulation_steps  ,						)
  
      # New Code #
      # For FSDP feature, prepare everything except the model as we have already prepared the model
      # before creating the optimizer
      # 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 :List[Any]										=     accelerator.prepare(
          SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
  
      UpperCamelCase :List[Any]										=     0
  
      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
              accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
              accelerator.load_state(args.resume_from_checkpoint )
              UpperCamelCase :List[str]										=     os.path.basename(args.resume_from_checkpoint )
          else:
              # Get the most recent checkpoint
              UpperCamelCase :Optional[int]										=     [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
              dirs.sort(key=os.path.getctime )
              UpperCamelCase :Dict										=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          UpperCamelCase :Dict										=     os.path.splitext(SCREAMING_SNAKE_CASE__ )[0]
  
          if "epoch" in training_difference:
              num_epochs -= int(training_difference.replace('''epoch_'''  ,						'''''' ) )
              UpperCamelCase :Dict										=     None
          else:
              UpperCamelCase :str										=     int(training_difference.replace('''step_'''  ,						'''''' ) )
              num_epochs -= resume_step // len(SCREAMING_SNAKE_CASE__ )
              # If resuming by step, we also need to know exactly how far into the DataLoader we went
              UpperCamelCase :List[str]										=     (num_epochs * len(SCREAMING_SNAKE_CASE__ )) - resume_step
  
      # Now we train the model
      for epoch in range(SCREAMING_SNAKE_CASE__ ):
          # New Code #
          # context manager to track the peak memory usage during the training epoch
          with TorchTracemalloc() as tracemalloc:
              model.train()
              if args.with_tracking:
                  UpperCamelCase :List[str]										=     0
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
                  # We need to skip steps until we reach the resumed step
                  if args.resume_from_checkpoint and epoch == 0:
                      if resume_step is not None and step < resume_step:
                          pass
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device )
                  UpperCamelCase :Optional[Any]										=     model(**SCREAMING_SNAKE_CASE__ )
                  UpperCamelCase :Dict										=     outputs.loss
                  UpperCamelCase :Tuple										=     loss / gradient_accumulation_steps
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                      total_loss += loss.detach().float()
                  accelerator.backward(SCREAMING_SNAKE_CASE__ )
                  if step % gradient_accumulation_steps == 0:
                      optimizer.step()
                      lr_scheduler.step()
                      optimizer.zero_grad()
                      # accelerator.print(lr_scheduler.get_lr())
  
                  overall_step += 1
  
                  if isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
                      UpperCamelCase :Any										=     F'''step_{overall_step}'''
                      if overall_step % checkpointing_steps == 0:
                          if args.output_dir is not None:
                              UpperCamelCase :Optional[Any]										=     os.path.join(args.output_dir  ,						SCREAMING_SNAKE_CASE__ )
                          accelerator.save_state(SCREAMING_SNAKE_CASE__ )
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin ) ) )
          accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used ) )
          accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked ) )
          accelerator.print(
              '''Total Peak Memory consumed during the train (max): {}'''.format(
                  tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
              accelerator.log(
                  {
                      '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin ),
                  }  ,						step=SCREAMING_SNAKE_CASE__  ,						)
  
          # New Code #
          # context manager to track the peak memory usage during the evaluation
          with TorchTracemalloc() as tracemalloc:
              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 :Union[str, Any]										=     model(**SCREAMING_SNAKE_CASE__ )
                  UpperCamelCase :List[str]										=     outputs.logits.argmax(dim=-1 )
                  UpperCamelCase							,     UpperCamelCase :List[str]										=     accelerator.gather_for_metrics((predictions, batch['''labels''']) )
                  metric.add_batch(
                      predictions=SCREAMING_SNAKE_CASE__  ,						references=SCREAMING_SNAKE_CASE__  ,						)
  
              UpperCamelCase :Union[str, Any]										=     metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''  ,						SCREAMING_SNAKE_CASE__ )
              if args.with_tracking:
                  accelerator.log(
                      {
                          '''accuracy''': eval_metric['''accuracy'''],
                          '''f1''': eval_metric['''f1'''],
                          '''train_loss''': total_loss.item() / len(SCREAMING_SNAKE_CASE__ ),
                      }  ,						step=SCREAMING_SNAKE_CASE__  ,						)
  
              if checkpointing_steps == "epoch":
                  UpperCamelCase :Optional[int]										=     F'''epoch_{epoch}'''
                  if args.output_dir is not None:
                      UpperCamelCase :Any										=     os.path.join(args.output_dir  ,						SCREAMING_SNAKE_CASE__ )
                  accelerator.save_state(SCREAMING_SNAKE_CASE__ )
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin ) ) )
          accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used ) )
          accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked ) )
          accelerator.print(
              '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
              accelerator.log(
                  {
                      '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin ),
                  }  ,						step=SCREAMING_SNAKE_CASE__  ,						)
  
      if args.with_tracking:
          accelerator.end_training()
  
  
  def 						_A     (       ):
      UpperCamelCase :str										=     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.''' )
      parser.add_argument(
          '''--checkpointing_steps'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=SCREAMING_SNAKE_CASE__  ,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,						)
      parser.add_argument(
          '''--resume_from_checkpoint'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=SCREAMING_SNAKE_CASE__  ,						help='''If the training should continue from a checkpoint folder.'''  ,						)
      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(
          '''--output_dir'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default='''.'''  ,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,						)
      parser.add_argument(
          '''--logging_dir'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default='''logs'''  ,						help='''Location on where to store experiment tracking logs`'''  ,						)
      parser.add_argument(
          '''--model_name_or_path'''  ,						type=SCREAMING_SNAKE_CASE__  ,						help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,						required=SCREAMING_SNAKE_CASE__  ,						)
      UpperCamelCase :str										=     parser.parse_args()
      UpperCamelCase :List[Any]										=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  __snake_case							      =						[
      ["""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 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any] ):
      UpperCamelCase :List[Any]										=     set()
      for row in edge_array:
          for item in row:
              distinct_edge.add(item[0] )
      return list(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[str]  ,						SCREAMING_SNAKE_CASE__						:			str ):
      UpperCamelCase :int										=     ['''0'''] * len(SCREAMING_SNAKE_CASE__ )
      for i, row in enumerate(SCREAMING_SNAKE_CASE__ ):
          for item in row:
              if distinct_edge in item[0]:
                  UpperCamelCase :Tuple										=     '''1'''
                  break
      return "".join(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Union[str, Any] ):
      UpperCamelCase :Optional[Any]										=     get_distinct_edge(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :Optional[int]										=     {}
  
      for item in distinct_edge:
          UpperCamelCase :List[str]										=     get_bitcode(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          # print('bit',bit)
          # bt=''.join(bit)
          UpperCamelCase :str										=     bit.count('''1''' )
          UpperCamelCase :Optional[int]										=     [s, bit]
      # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
      UpperCamelCase :Optional[int]										=     [
          [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 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :List[Any]										=     {}
      for _, item in enumerate(SCREAMING_SNAKE_CASE__ ):
          nodes.setdefault(item[2]  ,						[] ).append(item[0] )
      return nodes
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Tuple ):
      UpperCamelCase :str										=     {}
      for key, value in nodes.items():
          UpperCamelCase :str										=     value
      return cluster
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any] ):
      return [i * 100 / len(SCREAMING_SNAKE_CASE__ ) for i in cluster]
  
  
  def 						_A     (       ):
      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 						_A     (       SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]  ,						SCREAMING_SNAKE_CASE__						:			Optional[Any] ):
      for i in cluster[ca]:
          UpperCamelCase :Union[str, Any]										=     0
          UpperCamelCase :Dict										=     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:
                          UpperCamelCase :Union[str, Any]										=     [nodes[j]]
                      count += 1
              if count == 0:
                  UpperCamelCase :int										=     ca + 1
              else:
                  break
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			str ):
      UpperCamelCase :str										=     cluster[max(cluster.keys() )]
      UpperCamelCase :Any										=     '''Header'''
      UpperCamelCase :str										=     {}
      for i in x:
          if (["Header"],) in graph:
              graph[(["Header"],)].append(x[i] )
          else:
              UpperCamelCase :str										=     [x[i]]
      for i in x:
          UpperCamelCase :Dict										=     [['''Header''']]
      UpperCamelCase :Union[str, Any]										=     1
      while i < max(SCREAMING_SNAKE_CASE__ ) - 1:
          create_edge(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          UpperCamelCase :Optional[int]										=     i + 1
      return graph
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[str]  ,						SCREAMING_SNAKE_CASE__						:			Tuple  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			List[str]=None ):
      UpperCamelCase :Tuple										=     (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 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any] ):
      UpperCamelCase :str										=     int(s / 100 * (len(SCREAMING_SNAKE_CASE__ ) - 1) )
      for i in cluster[k]:
          my_dfs(SCREAMING_SNAKE_CASE__  ,						tuple(cluster[k][i] )  ,						(['''Header'''],) )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			str ):
      UpperCamelCase :Dict										=     []
      for edges in paths:
          UpperCamelCase :Tuple										=     []
          for j in range(len(SCREAMING_SNAKE_CASE__ ) - 1 ):
              UpperCamelCase :Dict										=     list(edges[j] )
              for e in temp:
                  UpperCamelCase :Union[str, Any]										=     (e[0], e[1])
                  el.append(SCREAMING_SNAKE_CASE__ )
          freq_sub_el.append(SCREAMING_SNAKE_CASE__ )
      return freq_sub_el
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[str] ):
      for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
          for j in range(len(edge_array[i] ) ):
              UpperCamelCase :Tuple										=     edge_array[i][j].split('''-''' )
              UpperCamelCase :Dict										=     t
  
  
  if __name__ == "__main__":
   preprocess(edge_array)
   __snake_case							      =						get_frequency_table(edge_array)
   __snake_case							      =						get_nodes(frequency_table)
   __snake_case							      =						get_cluster(nodes)
   __snake_case							      =						get_support(cluster)
   __snake_case							      =						construct_graph(cluster, nodes)
   find_freq_subgraph_given_support(60, cluster, graph)
   __snake_case							      =						[]
   __snake_case							      =						freq_subgraphs_edge_list(paths)
   print_all()
  
  
  
  
  
  