




"""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


UpperCamelCase__ :    Dict 					=							logging.getLogger(__name__)
@dataclass
class     A__      :
	A_							:				str						     =      field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
	A_							:				Optional[str]						     =      field(
	    default=lowercase_     ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
	A_							:				Optional[str]						     =      field(
	    default=lowercase_     ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
	A_							:				bool						     =      field(default=lowercase_     ,	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_							:				Optional[str]						     =      field(
	    default=lowercase_     ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}     ,	)




def       snake_case__	(       ):
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					__snake_case          =	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.
										__snake_case      ,      __snake_case      ,      __snake_case          =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
					else:
										__snake_case      ,      __snake_case      ,      __snake_case          =	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"""				,					lowerCAmelCase		)

					# 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.

					__snake_case          =	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				,					)
					__snake_case          =	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=lowerCAmelCase				,					)
					__snake_case          =	AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path				,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)				,					config=lowerCAmelCase				,					cache_dir=model_args.cache_dir				,					)

					# Get datasets
					__snake_case          =	hasattr(model.config				,					"""lang2id"""		)
					__snake_case          =	(
					    SquadDataset(
					        lowerCAmelCase				,					tokenizer=lowerCAmelCase				,					is_language_sensitive=lowerCAmelCase				,					cache_dir=model_args.cache_dir		)
					    if training_args.do_train
					    else None
					)
					__snake_case          =	(
					    SquadDataset(
					        lowerCAmelCase				,					tokenizer=lowerCAmelCase				,					mode="""dev"""				,					is_language_sensitive=lowerCAmelCase				,					cache_dir=model_args.cache_dir				,					)
					    if training_args.do_eval
					    else None
					)

					# Data collator
					__snake_case          =	DataCollatorWithPadding(lowerCAmelCase				,					pad_to_multiple_of=8		) if training_args.fpaa else None

					# Initialize our Trainer
					__snake_case          =	Trainer(
					    model=lowerCAmelCase				,					args=lowerCAmelCase				,					train_dataset=lowerCAmelCase				,					eval_dataset=lowerCAmelCase				,					data_collator=lowerCAmelCase				,					)

					# 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       snake_case__	(       lowerCAmelCase   :		int		):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
		main()








"""simple docstring"""

from functools import lru_cache




@lru_cache
def       snake_case__	(       lowerCAmelCase   :		int		):
					if not isinstance(lowerCAmelCase				,					lowerCAmelCase		):
										raise TypeError("""k must be an integer."""		)
					if k < 0:
										raise ValueError("""k integer must be greater or equal to zero."""		)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1		) + fibonacci(k - 2		)




def       snake_case__	(       lowerCAmelCase   :		list				,					lowerCAmelCase   :		int		):
					__snake_case          =	len(lowerCAmelCase		)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					__snake_case          =	0
					while True:
										if fibonacci(lowerCAmelCase		) >= len_list:
															__snake_case          =	i
															break
										i += 1
					__snake_case          =	0
					while fibb_k > 0:
										__snake_case          =	min(
										    offset + fibonacci(fibb_k - 1		)				,					len_list - 1		)  # Prevent out of range
										__snake_case          =	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1		)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


