






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()













__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()





