




'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_lowerCAmelCase				     = logging.getLogger(__name__)



@dataclass
class  A		:




				'''simple docstring'''





				A				     =    field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
				A				     =    field(
				    default=UpperCAmelCase_      ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
				A				     =    field(
				    default=UpperCAmelCase_      ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
				A				     =    field(default=UpperCAmelCase_      ,							metadata={"help": "Set this flag to use fast tokenization."}		)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				A				     =    field(
				    default=UpperCAmelCase_      ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,							)







def       __lowerCAmelCase (   ):
			__UpperCamelCase				:      Dict									=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

			if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      Union[str, Any]									=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
			else:
						__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      str									=       parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir						)
			    and os.listdir(training_args.output_dir						)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
						    " --overwrite_output_dir to overcome."						)

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,				datefmt="%m/%d/%Y %H:%M:%S" ,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,				)
			logger.warning(
			    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" ,				training_args.local_rank ,				training_args.device ,				training_args.n_gpu ,				bool(training_args.local_rank != -1						) ,				training_args.fpaa ,				)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank						):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info("Training/evaluation parameters %s" ,				lowercase_						)

			# Prepare Question-Answering task
			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			__UpperCamelCase				:      List[Any]									=       AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,				cache_dir=model_args.cache_dir ,				)
			__UpperCamelCase				:      str									=       AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,				cache_dir=model_args.cache_dir ,				use_fast=lowercase_ ,				)
			__UpperCamelCase				:      Optional[Any]									=       AutoModelForQuestionAnswering.from_pretrained(
			    model_args.model_name_or_path ,				from_tf=bool(".ckpt" in model_args.model_name_or_path						) ,				config=lowercase_ ,				cache_dir=model_args.cache_dir ,				)

			# Get datasets
			__UpperCamelCase				:      Union[str, Any]									=       hasattr(model.config ,				"lang2id"						)
			__UpperCamelCase				:      Dict									=       (
			    SquadDataset(
			        lowercase_ ,				tokenizer=lowercase_ ,				is_language_sensitive=lowercase_ ,				cache_dir=model_args.cache_dir						)
			    if training_args.do_train
			    else None
			)
			__UpperCamelCase				:      Dict									=       (
			    SquadDataset(
			        lowercase_ ,				tokenizer=lowercase_ ,				mode="dev" ,				is_language_sensitive=lowercase_ ,				cache_dir=model_args.cache_dir ,				)
			    if training_args.do_eval
			    else None
			)

			# Data collator
			__UpperCamelCase				:      Optional[int]									=       DataCollatorWithPadding(lowercase_ ,				pad_to_multiple_of=8						) if training_args.fpaa else None

			# Initialize our Trainer
			__UpperCamelCase				:      List[str]									=       Trainer(
			    model=lowercase_ ,				args=lowercase_ ,				train_dataset=lowercase_ ,				eval_dataset=lowercase_ ,				data_collator=lowercase_ ,				)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir						)

def       __lowerCAmelCase (   snake_case__						):
			main()


if __name__ == "__main__":
				main()





'''simple docstring'''






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

							if random.choice([True, False]      ):
										__UpperCamelCase		, __UpperCamelCase				:      Dict									=       roota.right, roota.left

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()



