code: 
  
  
  
  import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities import rank_zero_info
  
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      PretrainedConfig,
      PreTrainedTokenizer,
  )
  from transformers.optimization import (
      Adafactor,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )
  from transformers.utils.versions import require_version
  
  
  __A       					=		logging.getLogger(__name__)
  
  require_version('''pytorch_lightning>=1.0.4''')
  
  __A       					=		{
      '''base''': AutoModel,
      '''sequence-classification''': AutoModelForSequenceClassification,
      '''question-answering''': AutoModelForQuestionAnswering,
      '''pretraining''': AutoModelForPreTraining,
      '''token-classification''': AutoModelForTokenClassification,
      '''language-modeling''': AutoModelWithLMHead,
      '''summarization''': AutoModelForSeqaSeqLM,
      '''translation''': AutoModelForSeqaSeqLM,
  }
  
  
  # update this and the import above to support new schedulers from transformers.optimization
  __A       					=		{
      '''linear''': get_linear_schedule_with_warmup,
      '''cosine''': get_cosine_schedule_with_warmup,
      '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
      '''polynomial''': get_polynomial_decay_schedule_with_warmup,
      # '': get_constant_schedule,             # not supported for now
      # '': get_constant_schedule_with_warmup, # not supported for now
  }
  __A       					=		sorted(arg_to_scheduler.keys())
  __A       					=		'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
  
  
  
  
  
  
  class 	lowercase		(				pl.LightningModule):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		int		,						__UpperCAmelCase			:		argparse.Namespace		,						__UpperCAmelCase			:		Optional[Any]=None		,						__UpperCAmelCase			:		List[str]="base"		,						__UpperCAmelCase			:		List[Any]=None		,						__UpperCAmelCase			:		List[Any]=None		,						__UpperCAmelCase			:		str=None		,						**__UpperCAmelCase			:		int		,						)    ->				Tuple:
  							super().__init__()
  							# TODO: move to self.save_hyperparameters()
  							# self.save_hyperparameters()
  							# can also expand arguments into trainer signature for easier reading
  
  							self.save_hyperparameters(__UpperCAmelCase  )
  							UpperCAmelCase_=     0
  							UpperCAmelCase_=     Path(self.hparams.output_dir  )
  							UpperCAmelCase_=     self.hparams.cache_dir if self.hparams.cache_dir else None
  							if config is None:
  													UpperCAmelCase_=     AutoConfig.from_pretrained(
  													    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,						**({"""num_labels""": num_labels} if num_labels is not None else {})		,						cache_dir=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  							else:
  													UpperCAmelCase_=     config
  
  							UpperCAmelCase_=     ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  							for p in extra_model_params:
  													if getattr(self.hparams		,						__UpperCAmelCase		,						__UpperCAmelCase  ):
  																			assert hasattr(self.config		,						__UpperCAmelCase  ), F"""model config doesn't have a `{p}` attribute"""
  																			setattr(self.config		,						__UpperCAmelCase		,						getattr(self.hparams		,						__UpperCAmelCase  )  )
  
  							if tokenizer is None:
  													UpperCAmelCase_=     AutoTokenizer.from_pretrained(
  													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,						cache_dir=__UpperCAmelCase		,						)
  							else:
  													UpperCAmelCase_=     tokenizer
  							UpperCAmelCase_=     MODEL_MODES[mode]
  							if model is None:
  													UpperCAmelCase_=     self.model_type.from_pretrained(
  													    self.hparams.model_name_or_path		,						from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path  )		,						config=self.config		,						cache_dir=__UpperCAmelCase		,						)
  							else:
  													UpperCAmelCase_=     model
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						*__UpperCAmelCase			:		Union[str, Any]		,						**__UpperCAmelCase			:		int  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.model_type.from_pretrained(*__UpperCAmelCase		,						**__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				int:
  							UpperCAmelCase_=     arg_to_scheduler[self.hparams.lr_scheduler]
  							UpperCAmelCase_=     get_schedule_func(
  							    self.opt		,						num_warmup_steps=self.hparams.warmup_steps		,						num_training_steps=self.total_steps()  )
  							UpperCAmelCase_=     {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
  							return scheduler
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				int:
  							UpperCAmelCase_=     self.model
  							UpperCAmelCase_=     ["""bias""", """LayerNorm.weight"""]
  							UpperCAmelCase_=     [
  							    {
  							        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )],
  							        """weight_decay""": self.hparams.weight_decay,
  							    },
  							    {
  							        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )],
  							        """weight_decay""": 0.0,
  							    },
  							]
  							if self.hparams.adafactor:
  													UpperCAmelCase_=     Adafactor(
  													    __UpperCAmelCase		,						lr=self.hparams.learning_rate		,						scale_parameter=__UpperCAmelCase		,						relative_step=__UpperCAmelCase  )
  
  							else:
  													UpperCAmelCase_=     AdamW(
  													    __UpperCAmelCase		,						lr=self.hparams.learning_rate		,						eps=self.hparams.adam_epsilon  )
  							UpperCAmelCase_=     optimizer
  
  							UpperCAmelCase_=     self.get_lr_scheduler()
  
  							return [optimizer], [scheduler]
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[Any]  )    ->				Union[str, Any]:
  							return self.validation_step(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]  )    ->				Any:
  							return self.validation_end(__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				int:
  							UpperCAmelCase_=     max(1		,						self.hparams.gpus  )  # TODO: consider num_tpu_cores
  							UpperCAmelCase_=     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Tuple  )    ->				Union[str, Any]:
  							if stage == "test":
  													UpperCAmelCase_=     len(self.test_dataloader().dataset  )
  							else:
  													UpperCAmelCase_=     self.get_dataloader("""train"""		,						self.hparams.train_batch_size		,						shuffle=__UpperCAmelCase  )
  													UpperCAmelCase_=     len(self.train_dataloader().dataset  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		bool = False  )    ->				Any:
  							raise NotImplementedError("""You must implement this for your task"""  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				Dict:
  							return self.train_loader
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Dict:
  							return self.get_dataloader("""dev"""		,						self.hparams.eval_batch_size		,						shuffle=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				List[str]:
  							return self.get_dataloader("""test"""		,						self.hparams.eval_batch_size		,						shuffle=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Any  )    ->				Optional[int]:
  							return os.path.join(
  							    self.hparams.data_dir		,						"""cached_{}_{}_{}""".format(
  							        __UpperCAmelCase		,						list(filter(__UpperCAmelCase		,						self.hparams.model_name_or_path.split("""/"""  )  )  ).pop()		,						str(self.hparams.max_seq_length  )		,						)		,						)
  
  
  
  
  	@pl.utilities.rank_zero_only
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Dict[str, Any]  )    ->				None:
  							UpperCAmelCase_=     self.output_dir.joinpath("""best_tfmr"""  )
  							UpperCAmelCase_=     self.step_count
  							self.model.save_pretrained(__UpperCAmelCase  )
  							self.tokenizer.save_pretrained(__UpperCAmelCase  )
  
  
  
  
  	@staticmethod
  	def 						_SCREAMING_SNAKE_CASE	(			__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		str  )    ->				List[str]:
  							parser.add_argument(
  							    """--model_name_or_path"""		,						default=__UpperCAmelCase		,						type=__UpperCAmelCase		,						required=__UpperCAmelCase		,						help="""Path to pretrained model or model identifier from huggingface.co/models"""		,						)
  							parser.add_argument(
  							    """--config_name"""		,						default=""""""		,						type=__UpperCAmelCase		,						help="""Pretrained config name or path if not the same as model_name"""  )
  							parser.add_argument(
  							    """--tokenizer_name"""		,						default=__UpperCAmelCase		,						type=__UpperCAmelCase		,						help="""Pretrained tokenizer name or path if not the same as model_name"""		,						)
  							parser.add_argument(
  							    """--cache_dir"""		,						default=""""""		,						type=__UpperCAmelCase		,						help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""		,						)
  							parser.add_argument(
  							    """--encoder_layerdrop"""		,						type=__UpperCAmelCase		,						help="""Encoder layer dropout probability (Optional). Goes into model.config"""		,						)
  							parser.add_argument(
  							    """--decoder_layerdrop"""		,						type=__UpperCAmelCase		,						help="""Decoder layer dropout probability (Optional). Goes into model.config"""		,						)
  							parser.add_argument(
  							    """--dropout"""		,						type=__UpperCAmelCase		,						help="""Dropout probability (Optional). Goes into model.config"""		,						)
  							parser.add_argument(
  							    """--attention_dropout"""		,						type=__UpperCAmelCase		,						help="""Attention dropout probability (Optional). Goes into model.config"""		,						)
  							parser.add_argument("""--learning_rate"""		,						default=5E-5		,						type=__UpperCAmelCase		,						help="""The initial learning rate for Adam."""  )
  							parser.add_argument(
  							    """--lr_scheduler"""		,						default="""linear"""		,						choices=__UpperCAmelCase		,						metavar=__UpperCAmelCase		,						type=__UpperCAmelCase		,						help="""Learning rate scheduler"""		,						)
  							parser.add_argument("""--weight_decay"""		,						default=0.0		,						type=__UpperCAmelCase		,						help="""Weight decay if we apply some."""  )
  							parser.add_argument("""--adam_epsilon"""		,						default=1E-8		,						type=__UpperCAmelCase		,						help="""Epsilon for Adam optimizer."""  )
  							parser.add_argument("""--warmup_steps"""		,						default=0		,						type=__UpperCAmelCase		,						help="""Linear warmup over warmup_steps."""  )
  							parser.add_argument("""--num_workers"""		,						default=4		,						type=__UpperCAmelCase		,						help="""kwarg passed to DataLoader"""  )
  							parser.add_argument("""--num_train_epochs"""		,						dest="""max_epochs"""		,						default=3		,						type=__UpperCAmelCase  )
  							parser.add_argument("""--train_batch_size"""		,						default=32		,						type=__UpperCAmelCase  )
  							parser.add_argument("""--eval_batch_size"""		,						default=32		,						type=__UpperCAmelCase  )
  							parser.add_argument("""--adafactor"""		,						action="""store_true"""  )
  
  
  
  
  
  
  class 	lowercase		(				pl.Callback):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				Dict:
  							if (
  							    trainer.is_global_zero and trainer.global_rank == 0
  							):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
  													pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.
  
  
  
  
  
  
  class 	lowercase		(				pl.Callback):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[Any]  )    ->				Optional[int]:
  							UpperCAmelCase_=     trainer.lr_schedulers[0]["""scheduler"""]
  							UpperCAmelCase_=     {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
  							pl_module.logger.log_metrics(__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple		,						__UpperCAmelCase			:		pl.Trainer		,						__UpperCAmelCase			:		pl.LightningModule  )    ->				Optional[Any]:
  							rank_zero_info("""***** Validation results *****"""  )
  							UpperCAmelCase_=     trainer.callback_metrics
  							# Log results
  							for key in sorted(__UpperCAmelCase  ):
  													if key not in ["log", "progress_bar"]:
  																			rank_zero_info("""{} = {}\n""".format(__UpperCAmelCase		,						str(metrics[key]  )  )  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		pl.Trainer		,						__UpperCAmelCase			:		pl.LightningModule  )    ->				Any:
  							rank_zero_info("""***** Test results *****"""  )
  							UpperCAmelCase_=     trainer.callback_metrics
  							# Log and save results to file
  							UpperCAmelCase_=     os.path.join(pl_module.hparams.output_dir		,						"""test_results.txt"""  )
  							with open(__UpperCAmelCase		,						"""w"""  ) as writer:
  													for key in sorted(__UpperCAmelCase  ):
  																			if key not in ["log", "progress_bar"]:
  																									rank_zero_info("""{} = {}\n""".format(__UpperCAmelCase		,						str(metrics[key]  )  )  )
  																									writer.write("""{} = {}\n""".format(__UpperCAmelCase		,						str(metrics[key]  )  )  )
  
  
  
  def  __a  (					lowerCAmelCase_     :      Dict    ,lowerCAmelCase_     :      Union[str, Any]       )   ->				None:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						parser.add_argument(
  						    """--output_dir"""    ,default=lowerCAmelCase_    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_    ,help="""The output directory where the model predictions and checkpoints will be written."""    ,)
  						parser.add_argument(
  						    """--fp16"""    ,action="""store_true"""    ,help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""    ,)
  
  						parser.add_argument(
  						    """--fp16_opt_level"""    ,type=lowerCAmelCase_    ,default="""O2"""    ,help=(
  						        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
  						        """See details at https://nvidia.github.io/apex/amp.html"""
  						    )    ,)
  						parser.add_argument("""--n_tpu_cores"""    ,dest="""tpu_cores"""    ,type=lowerCAmelCase_       )
  						parser.add_argument("""--max_grad_norm"""    ,dest="""gradient_clip_val"""    ,default=1.0    ,type=lowerCAmelCase_    ,help="""Max gradient norm"""       )
  						parser.add_argument("""--do_train"""    ,action="""store_true"""    ,help="""Whether to run training."""       )
  						parser.add_argument("""--do_predict"""    ,action="""store_true"""    ,help="""Whether to run predictions on the test set."""       )
  						parser.add_argument(
  						    """--gradient_accumulation_steps"""    ,dest="""accumulate_grad_batches"""    ,type=lowerCAmelCase_    ,default=1    ,help="""Number of updates steps to accumulate before performing a backward/update pass."""    ,)
  						parser.add_argument("""--seed"""    ,type=lowerCAmelCase_    ,default=42    ,help="""random seed for initialization"""       )
  						parser.add_argument(
  						    """--data_dir"""    ,default=lowerCAmelCase_    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_    ,help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""    ,)
  
  
  
  def  __a  (					lowerCAmelCase_     :      BaseTransformer    ,lowerCAmelCase_     :      argparse.Namespace    ,lowerCAmelCase_     :      Dict=None    ,lowerCAmelCase_     :      Union[str, Any]=True    ,lowerCAmelCase_     :      Optional[int]=None    ,lowerCAmelCase_     :      Tuple=[]    ,lowerCAmelCase_     :      Union[str, Any]=None    ,lowerCAmelCase_     :      Tuple=None    ,**lowerCAmelCase_     :      List[str]    ,)   ->				Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						pl.seed_everything(args.seed       )
  
  						# init model
  						UpperCAmelCase_=     Path(model.hparams.output_dir       )
  						odir.mkdir(exist_ok=lowerCAmelCase_       )
  
  						# add custom checkpoints
  						if checkpoint_callback is None:
  												UpperCAmelCase_=     pl.callbacks.ModelCheckpoint(
  												    filepath=args.output_dir    ,prefix="""checkpoint"""    ,monitor="""val_loss"""    ,mode="""min"""    ,save_top_k=1       )
  						if early_stopping_callback:
  												extra_callbacks.append(lowerCAmelCase_       )
  						if logging_callback is None:
  												UpperCAmelCase_=     LoggingCallback()
  
  						UpperCAmelCase_=     {}
  
  						# TODO: remove with PyTorch 1.6 since pl uses native amp
  						if args.fpaa:
  												UpperCAmelCase_=     16
  												# train_params["amp_level"] = args.fp16_opt_level
  
  						if args.gpus > 1:
  												UpperCAmelCase_=     """auto"""  # "ddp"
  												UpperCAmelCase_=     """ddp"""
  
  						UpperCAmelCase_=     args.accumulate_grad_batches
  						UpperCAmelCase_=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  						UpperCAmelCase_=     """auto"""
  
  						UpperCAmelCase_=     pl.Trainer.from_argparse_args(
  						    lowerCAmelCase_    ,weights_summary=lowerCAmelCase_    ,callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]    ,logger=lowerCAmelCase_    ,**lowerCAmelCase_    ,)
  
  						if args.do_train:
  												trainer.fit(lowerCAmelCase_       )
  
  						return trainer
  
  
  
  
  
style_context: 
  
  
  
  from __future__ import annotations
  
  import os
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[str]	     =							(
  	    "High card",
  	    "One pair",
  	    "Two pairs",
  	    "Three of a kind",
  	    "Straight",
  	    "Flush",
  	    "Full house",
  	    "Four of a kind",
  	    "Straight flush",
  	    "Royal flush",
  	)
  
  	a__   :      Optional[Any]	     =							(
  	    "",  # placeholder as tuples are zero-indexed
  	    "One",
  	    "Two",
  	    "Three",
  	    "Four",
  	    "Five",
  	    "Six",
  	    "Seven",
  	    "Eight",
  	    "Nine",
  	    "Ten",
  	    "Jack",
  	    "Queen",
  	    "King",
  	    "Ace",
  	)
  
  
  
  
  	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		str  )    ->				None:
  							if not isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													UpperCAmelCase_=     F"""Hand should be of type 'str': {hand!r}"""
  													raise TypeError(__UpperCAmelCase  )
  							# split removes duplicate whitespaces so no need of strip
  							if len(hand.split(""" """  )  ) != 5:
  													UpperCAmelCase_=     F"""Hand should contain only 5 cards: {hand!r}"""
  													raise ValueError(__UpperCAmelCase  )
  							UpperCAmelCase_=     hand
  							UpperCAmelCase_=     0
  							UpperCAmelCase_=     0
  							UpperCAmelCase_,     UpperCAmelCase_=     self._internal_state()
  							UpperCAmelCase_=     self._get_hand_type()
  							UpperCAmelCase_=     self._card_values[0]
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Union[str, Any]:
  							return self._hand
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		PokerHand  )    ->				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(__UpperCAmelCase  )
  													else:
  																			return "Win" if self._second_pair > other._second_pair else "Loss"
  							return "Win" if self._first_pair > other._first_pair else "Loss"
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				str:
  							UpperCAmelCase_=     PokerHand._HAND_NAME[self._hand_type - 14]
  							UpperCAmelCase_=     PokerHand._CARD_NAME[self._high_card]
  							UpperCAmelCase_=     PokerHand._CARD_NAME[self._first_pair]
  							UpperCAmelCase_=     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_=     """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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		PokerHand  )    ->				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 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				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 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				bool:
  							return len(self._card_suit  ) == 1
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				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_=     self._card_values.pop(0  )
  																			self._card_values.append(__UpperCAmelCase  )
  													return True
  							return False
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				bool:
  							for i in range(4  ):
  													if self._card_values[i] - self._card_values[i + 1] != 1:
  																			return False
  							return True
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				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_=     UpperCAmelCase_=     UpperCAmelCase_=     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_=     self._card_values[i]
  																									kind += 1
  																			elif vala == self._card_values[i]:
  																									kind += 2
  																			elif not vala:
  																									UpperCAmelCase_=     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_=     kind + 2 if kind in [4, 5] else kind
  							# first meaning first pair to compare in 'compare_with'
  							UpperCAmelCase_=     max(__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     min(__UpperCAmelCase		,						__UpperCAmelCase  )
  							# 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(__UpperCAmelCase  ) != 3:
  													UpperCAmelCase_,     UpperCAmelCase_=     second, first
  							UpperCAmelCase_=     first
  							UpperCAmelCase_=     second
  							return kind
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				tuple[list[int], set[str]]:
  							# Internal representation of hand as a list of card values and
  							# a set of card suit
  							UpperCAmelCase_=     {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
  							UpperCAmelCase_=     self._hand.translate(str.maketrans(__UpperCAmelCase  )  ).split()
  							UpperCAmelCase_=     [int(card[:-1]  ) for card in new_hand]
  							UpperCAmelCase_=     {card[-1] for card in new_hand}
  							return sorted(__UpperCAmelCase		,						reverse=__UpperCAmelCase  ), card_suit
  
  
  
  
  	def __repr__(			self			:		Dict  )    ->				Dict:
  							return F"""{self.__class__}(\"{self._hand}\")"""
  
  
  
  
  	def __str__(			self			:		Tuple  )    ->				List[Any]:
  							return self._hand
  
  
  
  
  	def __eq__(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[Any]  )    ->				int:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return self.compare_with(__UpperCAmelCase  ) == "Tie"
  							return NotImplemented
  
  
  
  
  	def __lt__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[int]  )    ->				Union[str, Any]:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return self.compare_with(__UpperCAmelCase  ) == "Loss"
  							return NotImplemented
  
  
  
  
  	def __le__(			self			:		List[str]		,						__UpperCAmelCase			:		Optional[int]  )    ->				Optional[Any]:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return self < other or self == other
  							return NotImplemented
  
  
  
  
  	def __gt__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]  )    ->				int:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return not self < other and self != other
  							return NotImplemented
  
  
  
  
  	def __ge__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Tuple  )    ->				Any:
  							if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  													return not self < other
  							return NotImplemented
  
  
  
  
  	def __hash__(			self			:		Any  )    ->				int:
  							return object.__hash__(self  )
  
  
  
  def  __a  (					)   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     0
  						UpperCAmelCase_=     os.path.abspath(os.path.dirname(lowerCAmelCase_       )       )
  						UpperCAmelCase_=     os.path.join(lowerCAmelCase_    ,"""poker_hands.txt"""       )
  						with open(lowerCAmelCase_       ) as file_hand:
  												for line in file_hand:
  																		UpperCAmelCase_=     line[:14].strip()
  																		UpperCAmelCase_=     line[15:].strip()
  																		UpperCAmelCase_,     UpperCAmelCase_=     PokerHand(lowerCAmelCase_       ), PokerHand(lowerCAmelCase_       )
  																		UpperCAmelCase_=     player.compare_with(lowerCAmelCase_       )
  																		if output == "Win":
  																								answer += 1
  						return answer
  
  
  if __name__ == "__main__":
  	solution()
  
  
  
  
  