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


UpperCAmelCase							=       1_6
UpperCAmelCase							=       3_2






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any]     ):
      return int(x / 2**2_0     )



class 					snake_case__       :



   def __enter__(				self   :   List[Any]   )							->     List[Any]:




         '''simple docstring'''




         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         snake_case_     :			str       =       torch.cuda.memory_allocated()
         snake_case_     :			Optional[int]       =       psutil.Process()

         snake_case_     :			Any       =       self.cpu_mem_used()
         snake_case_     :			Optional[int]       =       True
         snake_case_     :			List[Any]       =       threading.Thread(target=self.peak_monitor_func   )
         snake_case_     :			List[Any]       =       True
         peak_monitor_thread.start()
         return self



   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     Dict:




         '''simple docstring'''




         return self.process.memory_info().rss



   def 					UpperCAmelCase__       (				self   :   Tuple   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			Dict       =       -1

         while True:
               snake_case_     :			List[str]       =       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   :   Union[str, Any]			,	*A__   :   Any   )							->     str:




         '''simple docstring'''




         snake_case_     :			Any       =       False

         gc.collect()
         torch.cuda.empty_cache()
         snake_case_     :			List[str]       =       torch.cuda.memory_allocated()
         snake_case_     :			Dict       =       torch.cuda.max_memory_allocated()
         snake_case_     :			List[str]       =       bamb(self.end - self.begin   )
         snake_case_     :			str       =       bamb(self.peak - self.begin   )

         snake_case_     :			List[str]       =       self.cpu_mem_used()
         snake_case_     :			int       =       bamb(self.cpu_end - self.cpu_begin   )
         snake_case_     :			Optional[Any]       =       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

     UpperCAmelCase							=       mocked_dataloaders  # noqa: F811






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: Tuple     ):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS" ,	lowerCAmelCase_     ) == "1":
            snake_case_     :			int       =       2

      # New Code #
      # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
      snake_case_     :			str       =       FullyShardedDataParallelPlugin(
          state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCAmelCase_ ,	ranka_only=lowerCAmelCase_     ) ,	optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCAmelCase_ ,	ranka_only=lowerCAmelCase_     ) ,	)

      # Initialize accelerator
      if args.with_tracking:
            snake_case_     :			List[Any]       =       Accelerator(
                cpu=args.cpu ,	mixed_precision=args.mixed_precision ,	log_with="wandb" ,	project_dir=args.logging_dir ,	fsdp_plugin=lowerCAmelCase_ ,	)
      else:
            snake_case_     :			str       =       Accelerator(fsdp_plugin=lowerCAmelCase_     )
      accelerator.print(accelerator.distributed_type     )

      if hasattr(args.checkpointing_steps ,	"isdigit"     ):
            if args.checkpointing_steps == "epoch":
                  snake_case_     :			Union[str, Any]       =       args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  snake_case_     :			List[Any]       =       int(args.checkpointing_steps     )
            else:
                  raise ValueError(
                      f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
      else:
            snake_case_     :			Union[str, Any]       =       None
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case_     :			List[Any]       =       config["lr"]
      snake_case_     :			Optional[int]       =       int(config["num_epochs"]     )
      snake_case_     :			Union[str, Any]       =       int(config["seed"]     )
      snake_case_     :			List[str]       =       int(config["batch_size"]     )

      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
            snake_case_     :			Union[str, Any]       =       vars(lowerCAmelCase_     )
            accelerator.init_trackers("fsdp_glue_no_trainer" ,	lowerCAmelCase_     )

      snake_case_     :			List[Any]       =       AutoTokenizer.from_pretrained(args.model_name_or_path     )
      snake_case_     :			Optional[int]       =       load_dataset("glue" ,	"mrpc"     )
      snake_case_     :			Any       =       evaluate.load("glue" ,	"mrpc"     )

      def tokenize_function(lowerCAmelCase_: Optional[Any]     ):
            # max_length=None => use the model max length (it's actually the default)
            snake_case_     :			Dict       =       tokenizer(examples["sentence1"] ,	examples["sentence2"] ,	truncation=lowerCAmelCase_ ,	max_length=lowerCAmelCase_     )
            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():
            snake_case_     :			Tuple       =       datasets.map(
                lowerCAmelCase_ ,	batched=lowerCAmelCase_ ,	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
      snake_case_     :			Dict       =       tokenized_datasets.rename_column("label" ,	"labels"     )

      # If the batch size is too big we use gradient accumulation
      snake_case_     :			Union[str, Any]       =       1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            snake_case_     :			Optional[Any]       =       batch_size // MAX_GPU_BATCH_SIZE
            snake_case_     :			Dict       =       MAX_GPU_BATCH_SIZE

      def collate_fn(lowerCAmelCase_: Tuple     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case_     :			Any       =       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  snake_case_     :			Union[str, Any]       =       1_6
            elif accelerator.mixed_precision != "no":
                  snake_case_     :			Union[str, Any]       =       8
            else:
                  snake_case_     :			List[str]       =       None

            return tokenizer.pad(
                lowerCAmelCase_ ,	padding="longest" ,	max_length=lowerCAmelCase_ ,	pad_to_multiple_of=lowerCAmelCase_ ,	return_tensors="pt" ,	)

      # Instantiate dataloaders.
      snake_case_     :			Any       =       DataLoader(
          tokenized_datasets["train"] ,	shuffle=lowerCAmelCase_ ,	collate_fn=lowerCAmelCase_ ,	batch_size=lowerCAmelCase_     )
      snake_case_     :			str       =       DataLoader(
          tokenized_datasets["validation"] ,	shuffle=lowerCAmelCase_ ,	collate_fn=lowerCAmelCase_ ,	batch_size=lowerCAmelCase_     )

      set_seed(lowerCAmelCase_     )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case_     :			Optional[Any]       =       AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path ,	return_dict=lowerCAmelCase_ ,	low_cpu_mem_usage=lowerCAmelCase_     )

      # New Code #
      # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
      snake_case_     :			List[str]       =       accelerator.prepare(lowerCAmelCase_     )
      accelerator.print(lowerCAmelCase_     )

      # 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
      snake_case_     :			Optional[Any]       =       torch.optim.AdamW(params=model.parameters() ,	lr=lowerCAmelCase_ ,	weight_decay=2e-4     )

      # Instantiate scheduler
      snake_case_     :			Union[str, Any]       =       get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase_ ,	num_warmup_steps=1_0 ,	num_training_steps=(len(lowerCAmelCase_     ) * 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.
      snake_case_  ,snake_case_  ,snake_case_  ,snake_case_     :			List[str]       =       accelerator.prepare(
          lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_     )

      snake_case_     :			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     )
                  snake_case_     :			Union[str, Any]       =       os.path.basename(args.resume_from_checkpoint     )
            else:
                  # Get the most recent checkpoint
                  snake_case_     :			Dict       =       [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
                  dirs.sort(key=os.path.getctime     )
                  snake_case_     :			Optional[Any]       =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            snake_case_     :			Dict       =       os.path.splitext(lowerCAmelCase_     )[0]

            if "epoch" in training_difference:
                  num_epochs -= int(training_difference.replace("epoch_" ,	""     )     )
                  snake_case_     :			Tuple       =       None
            else:
                  snake_case_     :			Tuple       =       int(training_difference.replace("step_" ,	""     )     )
                  num_epochs -= resume_step // len(lowerCAmelCase_     )
                  # If resuming by step, we also need to know exactly how far into the DataLoader we went
                  snake_case_     :			Optional[Any]       =       (num_epochs * len(lowerCAmelCase_     )) - resume_step

    # Now we train the model
      for epoch in range(lowerCAmelCase_     ):
            # New Code #
            # context manager to track the peak memory usage during the training epoch
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  if args.with_tracking:
                        snake_case_     :			Optional[int]       =       0
                  for step, batch in enumerate(lowerCAmelCase_     ):
                        # 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     )
                        snake_case_     :			List[str]       =       model(**lowerCAmelCase_     )
                        snake_case_     :			Union[str, Any]       =       outputs.loss
                        snake_case_     :			Optional[Any]       =       loss / gradient_accumulation_steps
                        # We keep track of the loss at each epoch
                        if args.with_tracking:
                              total_loss += loss.detach().float()
                        accelerator.backward(lowerCAmelCase_     )
                        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(lowerCAmelCase_ ,	lowerCAmelCase_     ):
                              snake_case_     :			Any       =       f"step_{overall_step}"
                              if overall_step % checkpointing_steps == 0:
                                    if args.output_dir is not None:
                                          snake_case_     :			Tuple       =       os.path.join(args.output_dir ,	lowerCAmelCase_     )
                                    accelerator.save_state(lowerCAmelCase_     )
        # 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=lowerCAmelCase_ ,	)

            # New Code #
            # context manager to track the peak memory usage during the evaluation
            with TorchTracemalloc() as tracemalloc:
                  model.eval()
                  for step, batch in enumerate(lowerCAmelCase_     ):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device     )
                        with torch.no_grad():
                              snake_case_     :			Optional[int]       =       model(**lowerCAmelCase_     )
                        snake_case_     :			Dict       =       outputs.logits.argmax(dim=-1     )
                        snake_case_  ,snake_case_     :			Optional[int]       =       accelerator.gather_for_metrics((predictions, batch["labels"])     )
                        metric.add_batch(
                            predictions=lowerCAmelCase_ ,	references=lowerCAmelCase_ ,	)

                  snake_case_     :			Any       =       metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f"epoch {epoch}:" ,	lowerCAmelCase_     )
                  if args.with_tracking:
                        accelerator.log(
                            {
                                "accuracy": eval_metric["accuracy"],
                                "f1": eval_metric["f1"],
                                "train_loss": total_loss.item() / len(lowerCAmelCase_     ),
                            } ,	step=lowerCAmelCase_ ,	)

                  if checkpointing_steps == "epoch":
                        snake_case_     :			List[str]       =       f"epoch_{epoch}"
                        if args.output_dir is not None:
                              snake_case_     :			int       =       os.path.join(args.output_dir ,	lowerCAmelCase_     )
                        accelerator.save_state(lowerCAmelCase_     )
        # 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=lowerCAmelCase_ ,	)

      if args.with_tracking:
            accelerator.end_training()






def 					SCREAMING_SNAKE_CASE_			(		):
      snake_case_     :			Union[str, Any]       =       argparse.ArgumentParser(description="Simple example of training script."     )
      parser.add_argument(
          "--mixed_precision" ,	type=lowerCAmelCase_ ,	default=lowerCAmelCase_ ,	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=lowerCAmelCase_ ,	default=lowerCAmelCase_ ,	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=lowerCAmelCase_ ,	default=lowerCAmelCase_ ,	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=lowerCAmelCase_ ,	default="." ,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,	)
      parser.add_argument(
          "--logging_dir" ,	type=lowerCAmelCase_ ,	default="logs" ,	help="Location on where to store experiment tracking logs`" ,	)
      parser.add_argument(
          "--model_name_or_path" ,	type=lowerCAmelCase_ ,	help="Path to pretrained model or model identifier from huggingface.co/models." ,	required=lowerCAmelCase_ ,	)
      snake_case_     :			int       =       parser.parse_args()
      snake_case_     :			List[str]       =       {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(lowerCAmelCase_ ,	lowerCAmelCase_     )


if __name__ == "__main__":
     main()




from __future__ import annotations

import os



class 					snake_case__       :
   _SCREAMING_SNAKE_CASE	: Any						    =      (
       "High card",
       "One pair",
       "Two pairs",
       "Three of a kind",
       "Straight",
       "Flush",
       "Full house",
       "Four of a kind",
       "Straight flush",
       "Royal flush",
   )

   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      (
       "",  # placeholder as tuples are zero-indexed
       "One",
       "Two",
       "Three",
       "Four",
       "Five",
       "Six",
       "Seven",
       "Eight",
       "Nine",
       "Ten",
       "Jack",
       "Queen",
       "King",
       "Ace",
   )



   def __init__(				self   :   List[str]			,	A__   :   str   )							->     None:




         '''simple docstring'''




         if not isinstance(A__			,	A__   ):
               snake_case_     :			int       =       f"Hand should be of type 'str': {hand!r}"
               raise TypeError(A__   )
         # split removes duplicate whitespaces so no need of strip
         if len(hand.split(" "   )   ) != 5:
               snake_case_     :			Optional[int]       =       f"Hand should contain only 5 cards: {hand!r}"
               raise ValueError(A__   )
         snake_case_     :			str       =       hand
         snake_case_     :			Union[str, Any]       =       0
         snake_case_     :			str       =       0
         snake_case_  ,snake_case_     :			Union[str, Any]       =       self._internal_state()
         snake_case_     :			int       =       self._get_hand_type()
         snake_case_     :			Tuple       =       self._card_values[0]



   @property
   def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     str:




         '''simple docstring'''




         return self._hand



   def 					UpperCAmelCase__       (				self   :   int			,	A__   :   PokerHand   )							->     str:




         '''simple docstring'''




         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(A__   )
               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   :   Union[str, Any]   )							->     str:




         '''simple docstring'''




         snake_case_     :			int       =       PokerHand._HAND_NAME[self._hand_type - 14]
         snake_case_     :			Any       =       PokerHand._CARD_NAME[self._high_card]
         snake_case_     :			Tuple       =       PokerHand._CARD_NAME[self._first_pair]
         snake_case_     :			Union[str, Any]       =       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]:
               snake_case_     :			Optional[Any]       =       "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   :   Optional[Any]			,	A__   :   PokerHand   )							->     str:




         '''simple docstring'''




         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   :   Dict   )							->     int:




         '''simple docstring'''




         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   :   Optional[int]   )							->     bool:




         '''simple docstring'''




         return len(self._card_suit   ) == 1



   def 					UpperCAmelCase__       (				self   :   str   )							->     bool:




         '''simple docstring'''




         if sorted(self._card_values   ) == [2, 3, 4, 5, 14]:
               if self._card_values[0] == 14:
                     # Remember, our list is sorted in reverse order
                     snake_case_     :			Tuple       =       self._card_values.pop(0   )
                     self._card_values.append(A__   )
               return True
         return False



   def 					UpperCAmelCase__       (				self   :   str   )							->     bool:




         '''simple docstring'''




         for i in range(4   ):
               if self._card_values[i] - self._card_values[i + 1] != 1:
                     return False
         return True



   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     int:




         '''simple docstring'''




         snake_case_     :			Union[str, Any]       =       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:
                           snake_case_     :			Union[str, Any]       =       self._card_values[i]
                           kind += 1
                     elif vala == self._card_values[i]:
                           kind += 2
                     elif not vala:
                           snake_case_     :			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)
         snake_case_     :			Union[str, Any]       =       kind + 2 if kind in [4, 5] else kind
         # first meaning first pair to compare in 'compare_with'
         snake_case_     :			Optional[Any]       =       max(A__			,	A__   )
         snake_case_     :			Optional[int]       =       min(A__			,	A__   )
         # 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(A__   ) != 3:
               snake_case_  ,snake_case_     :			Union[str, Any]       =       second, first
         snake_case_     :			Dict       =       first
         snake_case_     :			Tuple       =       second
         return kind



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]   )							->     tuple[list[int], set[str]]:




         '''simple docstring'''




         snake_case_     :			dict       =       {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
         snake_case_     :			Any       =       self._hand.translate(str.maketrans(A__   )   ).split()
         snake_case_     :			Optional[int]       =       [int(card[:-1]   ) for card in new_hand]
         snake_case_     :			Any       =       {card[-1] for card in new_hand}
         return sorted(A__			,	reverse=A__   ), card_suit



   def __repr__(				self   :   Optional[int]   )							->     Tuple:




         '''simple docstring'''




         return f"{self.__class__}(\"{self._hand}\")"



   def __str__(				self   :   Tuple   )							->     List[str]:




         '''simple docstring'''




         return self._hand



   def __eq__(				self   :   Any			,	A__   :   Any   )							->     str:




         '''simple docstring'''




         if isinstance(A__			,	A__   ):
               return self.compare_with(A__   ) == "Tie"
         return NotImplemented



   def __lt__(				self   :   Dict			,	A__   :   int   )							->     Dict:




         '''simple docstring'''




         if isinstance(A__			,	A__   ):
               return self.compare_with(A__   ) == "Loss"
         return NotImplemented



   def __le__(				self   :   List[str]			,	A__   :   Optional[int]   )							->     Dict:




         '''simple docstring'''




         if isinstance(A__			,	A__   ):
               return self < other or self == other
         return NotImplemented



   def __gt__(				self   :   Any			,	A__   :   int   )							->     Tuple:




         '''simple docstring'''




         if isinstance(A__			,	A__   ):
               return not self < other and self != other
         return NotImplemented



   def __ge__(				self   :   Optional[Any]			,	A__   :   int   )							->     Tuple:




         '''simple docstring'''




         if isinstance(A__			,	A__   ):
               return not self < other
         return NotImplemented



   def __hash__(				self   :   str   )							->     Union[str, Any]:




         '''simple docstring'''




         return object.__hash__(self   )






def 					SCREAMING_SNAKE_CASE_			(		):
      # Solution for problem number 54 from Project Euler
      # Input from poker_hands.txt file
      snake_case_     :			Optional[int]       =       0
      snake_case_     :			Optional[Any]       =       os.path.abspath(os.path.dirname(lowerCAmelCase_     )     )
      snake_case_     :			Dict       =       os.path.join(lowerCAmelCase_ ,	"poker_hands.txt"     )
      with open(lowerCAmelCase_     ) as file_hand:
            for line in file_hand:
                  snake_case_     :			List[str]       =       line[:1_4].strip()
                  snake_case_     :			Dict       =       line[1_5:].strip()
                  snake_case_  ,snake_case_     :			str       =       PokerHand(lowerCAmelCase_     ), PokerHand(lowerCAmelCase_     )
                  snake_case_     :			Dict       =       player.compare_with(lowerCAmelCase_     )
                  if output == "Win":
                        answer += 1
      return answer


if __name__ == "__main__":
     solution()



