code: 
  
  
  
  
  
  
  import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=1024					,       SCREAMING_SNAKE_CASE_=1024					,       SCREAMING_SNAKE_CASE_=3.6					,       SCREAMING_SNAKE_CASE_=False					,       )       ->   List[Any]:
            UpperCamelCase :Union[str, Any]										=     tokenizer
            UpperCamelCase :Optional[Any]										=     tokenizer.bos_token_id
            UpperCamelCase :str										=     dataset
            UpperCamelCase :Tuple										=     seq_length
            UpperCamelCase :Tuple										=     0
            UpperCamelCase :Optional[Any]										=     infinite
            UpperCamelCase :Tuple										=     0
            UpperCamelCase :Optional[Any]										=     tokenized
  
            if self.tokenized:
                UpperCamelCase :Optional[int]										=     seq_length * num_of_sequences
                UpperCamelCase :List[str]										=     '''input_ids'''
            else:
                UpperCamelCase :List[str]										=     seq_length * chars_per_token * num_of_sequences
                UpperCamelCase :Dict										=     '''content'''
        def __iter__(       self				)       ->   int:
            UpperCamelCase :int										=     iter(self.dataset				)
            UpperCamelCase :Optional[int]										=     True
            while more_examples:
                UpperCamelCase							,     UpperCamelCase :List[Any]										=     [], 0
                while True:
                    if buffer_len >= self.max_buffer_size:
                        break
                    try:
                        buffer.append(next(SCREAMING_SNAKE_CASE_				)[self.content_field]				)
                        buffer_len += len(buffer[-1]				)
                    except StopIteration:
                        if self.infinite:
                            UpperCamelCase :int										=     iter(self.dataset				)
                            self.epoch += 1
                            logger.info(F'''Dataset epoch: {self.epoch}'''				)
                        else:
                            UpperCamelCase :List[Any]										=     False
                            break
                if self.tokenized:
                    UpperCamelCase :List[Any]										=     buffer
                else:
                    UpperCamelCase :Optional[Any]										=     self.tokenizer(SCREAMING_SNAKE_CASE_					,       truncation=SCREAMING_SNAKE_CASE_				)['''input_ids''']
                UpperCamelCase :str										=     []
                for tokenized_input in tokenized_inputs:
                    all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
                for i in range(0					,       len(SCREAMING_SNAKE_CASE_				)					,       self.seq_length				):
                    UpperCamelCase :List[Any]										=     all_token_ids[i : i + self.seq_length]
                    if len(SCREAMING_SNAKE_CASE_				) == self.seq_length:
                        self.current_size += 1
                        yield torch.tensor(SCREAMING_SNAKE_CASE_				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=1000				)       ->   Union[str, Any]:
            return ShufflerIterDataPipe(self					,       buffer_size=SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any] ):
      UpperCamelCase :str										=     args.model_ckpt.split('''/''' )[-1]
      UpperCamelCase :Dict										=     logging.getLogger(__name__ )
      UpperCamelCase :Tuple										=     Path(args.save_dir ) / '''log/'''
      log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :Any										=     F'''debug_{accelerator.process_index}.log'''
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,						datefmt='''%m/%d/%Y %H:%M:%S'''  ,						level=logging.INFO  ,						handlers=[logging.FileHandler(log_dir / filename ), logging.StreamHandler()]  ,						)
      if accelerator.is_main_process:  # we only want to setup logging once
          accelerator.init_trackers(SCREAMING_SNAKE_CASE__  ,						vars(SCREAMING_SNAKE_CASE__ ) )
          UpperCamelCase :List[str]										=     accelerator.trackers[0].run.name
          logger.setLevel(logging.INFO )
          datasets.utils.logging.set_verbosity_info()
          transformers.utils.logging.set_verbosity_info()
      else:
          UpperCamelCase :int										=     ''''''
          logger.setLevel(logging.ERROR )
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
      return logger, run_name
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Union[str, Any] ):
      UpperCamelCase :Any										=     {'''streaming''': True}
      UpperCamelCase :Union[str, Any]										=     load_dataset(args.dataset_name_train  ,						split='''train'''  ,						**SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :Optional[Any]										=     train_data.shuffle(buffer_size=args.shuffle_buffer  ,						seed=args.seed )
      UpperCamelCase :int										=     load_dataset(args.dataset_name_valid  ,						split='''train'''  ,						**SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :Tuple										=     ConstantLengthDataset(
          SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						infinite=SCREAMING_SNAKE_CASE__  ,						seq_length=args.seq_length  ,						tokenized=args.tokenized )
      UpperCamelCase :List[Any]										=     ConstantLengthDataset(
          SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						infinite=SCREAMING_SNAKE_CASE__  ,						seq_length=args.seq_length  ,						tokenized=args.tokenized )
      UpperCamelCase :List[str]										=     train_dataset.shuffle(buffer_size=args.shuffle_buffer )
      UpperCamelCase :Dict										=     DataLoader(SCREAMING_SNAKE_CASE__  ,						batch_size=args.train_batch_size  ,						shuffle=SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :Any										=     DataLoader(SCREAMING_SNAKE_CASE__  ,						batch_size=args.valid_batch_size )
      return train_dataloader, eval_dataloader
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			Dict=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] ):
      UpperCamelCase							,     UpperCamelCase :Dict										=     [], []
      for n, p in model.named_parameters():
          if any(nd in n for nd in no_decay ):
              params_without_wd.append(SCREAMING_SNAKE_CASE__ )
          else:
              params_with_wd.append(SCREAMING_SNAKE_CASE__ )
      return [
          {"params": params_with_wd, "weight_decay": args.weight_decay},
          {"params": params_without_wd, "weight_decay": 0.0},
      ]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[Any]  ,						SCREAMING_SNAKE_CASE__						:			Dict ):
      logger.info(F'''Step {step}: {metrics}''' )
      if accelerator.is_main_process:
          accelerator.log(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			Tuple ):
      # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
      UpperCamelCase :str										=     accelerator.unwrap_model(SCREAMING_SNAKE_CASE__ ).config
      UpperCamelCase :str										=     4 if args.gradient_checkpointing else 3
      UpperCamelCase :Union[str, Any]										=     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
      UpperCamelCase :Union[str, Any]										=     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
      UpperCamelCase :Union[str, Any]										=     factor * (
          1.0
          + (args.seq_length / (6.0 * config_model.n_embd))
          + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
      )
      UpperCamelCase :Optional[Any]										=     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
      return tflops
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			str ):
      model.eval()
      UpperCamelCase :Any										=     []
      for step, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
          with torch.no_grad():
              UpperCamelCase :Optional[Any]										=     model(SCREAMING_SNAKE_CASE__  ,						labels=SCREAMING_SNAKE_CASE__ )
          UpperCamelCase :Tuple										=     outputs.loss.repeat(args.valid_batch_size )
          losses.append(accelerator.gather(SCREAMING_SNAKE_CASE__ ) )
          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
              break
      UpperCamelCase :Any										=     torch.cat(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :List[Any]										=     losses[: eval_dataloader.dataset.current_size].mean()
      try:
          UpperCamelCase :List[Any]										=     torch.exp(SCREAMING_SNAKE_CASE__ )
      except OverflowError:
          UpperCamelCase :List[str]										=     float('''inf''' )
      return loss.item(), perplexity.item()
  
  
  # Settings
  __snake_case							      =						HfArgumentParser(TrainingArguments)
  __snake_case							      =						parser.parse_args()
  
  # Accelerator
  __snake_case							      =						ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
  __snake_case							      =						Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
  __snake_case							      =						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  __snake_case							      =						Namespace(**vars(args), **acc_state)
  __snake_case							      =						accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
   __snake_case							      =						Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  __snake_case      ,   __snake_case							      =						setup_logging(args)
  logger.info(accelerator.state)
  
  # Checkout new branch on repo
  if accelerator.is_main_process:
   hf_repo.git_checkout(run_name, create_branch_ok=True)
  
  # Load model and tokenizer
  __snake_case							      =						AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
   model.gradient_checkpointing_enable()
  __snake_case							      =						AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  __snake_case      ,   __snake_case							      =						create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  __snake_case							      =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  __snake_case							      =						get_scheduler(
      name=args.lr_scheduler_type,
      optimizer=optimizer,
      num_warmup_steps=args.num_warmup_steps,
      num_training_steps=args.max_train_steps,
  )
  accelerator.register_for_checkpointing(lr_scheduler)
  
  
  def 						_A     (       ):
      return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  __snake_case      ,   __snake_case      ,   __snake_case      ,   __snake_case							      =						accelerator.prepare(
      model, optimizer, train_dataloader, eval_dataloader
  )
  
  # 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)
    __snake_case							      =						os.path.basename(args.resume_from_checkpoint)
   else:
    # Get the most recent checkpoint
    __snake_case							      =						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
    dirs.sort(key=os.path.getctime)
    __snake_case							      =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
   # Extract the step of the checkpoint to continue from there
   __snake_case							      =						os.path.splitext(path)[0]
   __snake_case							      =						int(training_difference.replace("""step_""", """"""))
  
  # Train model
  model.train()
  __snake_case							      =						0
  __snake_case							      =						time.time()
  __snake_case							      =						0
  for step, batch in enumerate(train_dataloader, start=1):
   if args.resume_from_checkpoint and step < resume_step:
    continue  # we need to skip steps until we reach the resumed step
   __snake_case							      =						model(batch, labels=batch, use_cache=False).loss
   __snake_case							      =						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
   loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
   log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
   __snake_case							      =						loss / args.gradient_accumulation_steps
   if step % args.gradient_accumulation_steps != 0:
    # Prevent backward from doing gradient all_reduce in every step
    if accelerator.distributed_type == DistributedType.MULTI_GPU:
     with model.no_sync():
      accelerator.backward(loss)
    else:
     accelerator.backward(loss)
   else:
    __snake_case							      =						get_lr()
    accelerator.backward(loss)
    accelerator.clip_grad_norm_(model.parameters(), 1.0)
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()
    __snake_case							      =						time.time() - t_start
    __snake_case							      =						compute_tflops(elapsed_time, accelerator, args)
    log_metrics(
        step,
        {
            """steps""": completed_steps,
            """loss/train""": loss_tracking,
            """lr""": lr,
            """tflops""": tflops,
            """time_per_iteration""": elapsed_time,
        },
    )
    __snake_case							      =						time.time()
    __snake_case							      =						0
    completed_steps += 1
   if step % args.save_checkpoint_steps == 0:
    logger.info("""Evaluating and saving model checkpoint""")
    __snake_case      ,   __snake_case							      =						evaluate(args)
    log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
    accelerator.wait_for_everyone()
    __snake_case							      =						os.path.join(args.save_dir, f'''step_{step}''')
    accelerator.save_state(save_dir)
    if accelerator.is_main_process:
     hf_repo.push_to_hub(commit_message=f'''step {step}''')
    model.train()
   if completed_steps >= args.max_train_steps:
    break
  
  # Evaluate and save the last checkpoint
  logger.info("""Evaluating and saving model after training""")
  __snake_case      ,   __snake_case							      =						evaluate(args)
  log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
  accelerator.wait_for_everyone()
  __snake_case							      =						accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  __snake_case							      =						os.path.join(args.save_dir, f'''step_{step}''')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
   hf_repo.push_to_hub(commit_message="""final model""")
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import os
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Union[str, Any]     =(
            'High card',
            'One pair',
            'Two pairs',
            'Three of a kind',
            'Straight',
            'Flush',
            'Full house',
            'Four of a kind',
            'Straight flush',
            'Royal flush',
        )
  
        UpperCamelCase_       :      List[str]     =(
            '',  # placeholder as tuples are zero-indexed
            'One',
            'Two',
            'Three',
            'Four',
            'Five',
            'Six',
            'Seven',
            'Eight',
            'Nine',
            'Ten',
            'Jack',
            'Queen',
            'King',
            'Ace',
        )
        def __init__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if not isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Tuple										=     F'''Hand should be of type \'str\': {hand!r}'''
                raise TypeError(SCREAMING_SNAKE_CASE_				)
            # split removes duplicate whitespaces so no need of strip
            if len(hand.split(''' '''				)				) != 5:
                UpperCamelCase :List[Any]										=     F'''Hand should contain only 5 cards: {hand!r}'''
                raise ValueError(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Dict										=     hand
            UpperCamelCase :int										=     0
            UpperCamelCase :Union[str, Any]										=     0
            UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     self._internal_state()
            UpperCamelCase :Any										=     self._get_hand_type()
            UpperCamelCase :List[Any]										=     self._card_values[0]
        @property
        def 					UpperCAmelCase		(       self				)       ->   str:
            return self._hand
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   str:
            # Breaking the tie works on the following order of precedence:
            # 1. First pair (default 0)
            # 2. Second pair (default 0)
            # 3. Compare all cards in reverse order because they are sorted.
  
            # First pair and second pair will only be a non-zero value if the card
            # type is either from the following:
            # 21: Four of a kind
            # 20: Full house
            # 17: Three of a kind
            # 16: Two pairs
            # 15: One pair
            if self._hand_type > other._hand_type:
                return "Win"
            elif self._hand_type < other._hand_type:
                return "Loss"
            elif self._first_pair == other._first_pair:
                if self._second_pair == other._second_pair:
                    return self._compare_cards(SCREAMING_SNAKE_CASE_				)
                else:
                    return "Win" if self._second_pair > other._second_pair else "Loss"
            return "Win" if self._first_pair > other._first_pair else "Loss"
        def 					UpperCAmelCase		(       self				)       ->   str:
            UpperCamelCase :List[str]										=     PokerHand._HAND_NAME[self._hand_type - 14]
            UpperCamelCase :Optional[Any]										=     PokerHand._CARD_NAME[self._high_card]
            UpperCamelCase :List[Any]										=     PokerHand._CARD_NAME[self._first_pair]
            UpperCamelCase :Dict										=     PokerHand._CARD_NAME[self._second_pair]
            if self._hand_type in [22, 19, 18]:
                return name + F''', {high}-high'''
            elif self._hand_type in [21, 17, 15]:
                return name + F''', {paira}s'''
            elif self._hand_type in [20, 16]:
                UpperCamelCase :Tuple										=     '''over''' if self._hand_type == 20 else '''and'''
                return name + F''', {paira}s {join} {paira}s'''
            elif self._hand_type == 23:
                return name
            else:
                return name + F''', {high}'''
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   str:
            # Enumerate gives us the index as well as the element of a list
            for index, card_value in enumerate(self._card_values				):
                if card_value != other._card_values[index]:
                    return "Win" if card_value > other._card_values[index] else "Loss"
            return "Tie"
        def 					UpperCAmelCase		(       self				)       ->   int:
            # Number representing the type of hand internally:
            # 23: Royal flush
            # 22: Straight flush
            # 21: Four of a kind
            # 20: Full house
            # 19: Flush
            # 18: Straight
            # 17: Three of a kind
            # 16: Two pairs
            # 15: One pair
            # 14: High card
            if self._is_flush():
                if self._is_five_high_straight() or self._is_straight():
                    return 23 if sum(self._card_values				) == 60 else 22
                return 19
            elif self._is_five_high_straight() or self._is_straight():
                return 18
            return 14 + self._is_same_kind()
        def 					UpperCAmelCase		(       self				)       ->   bool:
            return len(self._card_suit				) == 1
        def 					UpperCAmelCase		(       self				)       ->   bool:
            # If a card is a five high straight (low ace) change the location of
            # ace from the start of the list to the end. Check whether the first
            # element is ace or not. (Don't want to change again)
            # Five high straight (low ace): AH 2H 3S 4C 5D
            # Why use sorted here? One call to this function will mutate the list to
            # [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
            # need to compare the sorted version.
            # Refer test_multiple_calls_five_high_straight in test_poker_hand.py
            if sorted(self._card_values				) == [2, 3, 4, 5, 14]:
                if self._card_values[0] == 14:
                    # Remember, our list is sorted in reverse order
                    UpperCamelCase :Optional[int]										=     self._card_values.pop(0				)
                    self._card_values.append(SCREAMING_SNAKE_CASE_				)
                return True
            return False
        def 					UpperCAmelCase		(       self				)       ->   bool:
            for i in range(4				):
                if self._card_values[i] - self._card_values[i + 1] != 1:
                    return False
            return True
        def 					UpperCAmelCase		(       self				)       ->   int:
            # Kind Values for internal use:
            # 7: Four of a kind
            # 6: Full house
            # 3: Three of a kind
            # 2: Two pairs
            # 1: One pair
            # 0: False
            UpperCamelCase :Optional[int]										=     0
            for i in range(4				):
                # Compare two cards at a time, if they are same increase 'kind',
                # add the value of the card to val1, if it is repeating again we
                # will add 2 to 'kind' as there are now 3 cards with same value.
                # If we get card of different value than val1, we will do the same
                # thing with val2
                if self._card_values[i] == self._card_values[i + 1]:
                    if not vala:
                        UpperCamelCase :List[Any]										=     self._card_values[i]
                        kind += 1
                    elif vala == self._card_values[i]:
                        kind += 2
                    elif not vala:
                        UpperCamelCase :Optional[Any]										=     self._card_values[i]
                        kind += 1
                    elif vala == self._card_values[i]:
                        kind += 2
          # For consistency in hand type (look at note in _get_hand_type function)
            UpperCamelCase :List[Any]										=     kind + 2 if kind in [4, 5] else kind
            # first meaning first pair to compare in 'compare_with'
            UpperCamelCase :int										=     max(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Optional[int]										=     min(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            # If it's full house (three count pair + two count pair), make sure
            # first pair is three count and if not then switch them both.
            if kind == 6 and self._card_values.count(SCREAMING_SNAKE_CASE_				) != 3:
                UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     second, first
            UpperCamelCase :int										=     first
            UpperCamelCase :int										=     second
            return kind
        def 					UpperCAmelCase		(       self				)       ->   tuple[list[int], set[str]]:
            # Internal representation of hand as a list of card values and
            # a set of card suit
            UpperCamelCase :dict										=     {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
            UpperCamelCase :Any										=     self._hand.translate(str.maketrans(SCREAMING_SNAKE_CASE_				)				).split()
            UpperCamelCase :Any										=     [int(card[:-1]				) for card in new_hand]
            UpperCamelCase :Optional[int]										=     {card[-1] for card in new_hand}
            return sorted(SCREAMING_SNAKE_CASE_					,       reverse=SCREAMING_SNAKE_CASE_				), card_suit
        def __repr__(       self				)       ->   List[Any]:
            return F'''{self.__class__}("{self._hand}")'''
        def __str__(       self				)       ->   Optional[Any]:
            return self._hand
        def __eq__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Dict:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return self.compare_with(SCREAMING_SNAKE_CASE_				) == "Tie"
            return NotImplemented
        def __lt__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Tuple:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return self.compare_with(SCREAMING_SNAKE_CASE_				) == "Loss"
            return NotImplemented
        def __le__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return self < other or self == other
            return NotImplemented
        def __gt__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Tuple:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return not self < other and self != other
            return NotImplemented
        def __ge__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[int]:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return not self < other
            return NotImplemented
  
  
  
  
        def __hash__(       self				)       ->   List[Any]:
            return object.__hash__(self				)
  
  
  
  
  
  
  
  def 						_A     (       ):
      # Solution for problem number 54 from Project Euler
      # Input from poker_hands.txt file
      UpperCamelCase :str										=     0
      UpperCamelCase :str										=     os.path.abspath(os.path.dirname(SCREAMING_SNAKE_CASE__ ) )
      UpperCamelCase :Dict										=     os.path.join(SCREAMING_SNAKE_CASE__  ,						'''poker_hands.txt''' )
      with open(SCREAMING_SNAKE_CASE__ ) as file_hand:
          for line in file_hand:
              UpperCamelCase :Dict										=     line[:14].strip()
              UpperCamelCase :Optional[Any]										=     line[15:].strip()
              UpperCamelCase							,     UpperCamelCase :Tuple										=     PokerHand(SCREAMING_SNAKE_CASE__ ), PokerHand(SCREAMING_SNAKE_CASE__ )
              UpperCamelCase :Optional[Any]										=     player.compare_with(SCREAMING_SNAKE_CASE__ )
              if output == "Win":
                  answer += 1
      return answer
  
  
  if __name__ == "__main__":
   solution()
  
  
  
  
  
  