






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""")













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()





