





'''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


_a					: str      =    logging.getLogger(__name__)





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





def    _lowerCAmelCase						(						)					->					int:
			# 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.

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

			__lowerCAmelCase											=				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					,	)
			__lowerCAmelCase											=				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					,	)
			__lowerCAmelCase											=				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
			__lowerCAmelCase											=				hasattr(model.config					,	"""lang2id"""	)
			__lowerCAmelCase											=				(
			    SquadDataset(
			        lowercase					,	tokenizer=lowercase					,	is_language_sensitive=lowercase					,	cache_dir=model_args.cache_dir	)
			    if training_args.do_train
			    else None
			)
			__lowerCAmelCase											=				(
			    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
			__lowerCAmelCase											=				DataCollatorWithPadding(lowercase					,	pad_to_multiple_of=8	) if training_args.fpaa else None

			# Initialize our Trainer
			__lowerCAmelCase											=				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						(						lowercase	)					->					int:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()








'''simple docstring'''







import torch
from torch import autograd





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.numel()
					__lowerCAmelCase											=				int(0.005 * nb_elems			) + 1
					if sigmoid:
								__lowerCAmelCase											=				(torch.sigmoid(__SCREAMING_SNAKE_CASE			) > threshold).type(inputs.type()			)
					else:
								__lowerCAmelCase											=				(inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__lowerCAmelCase											=				inputs.flatten().kthvalue(max(nb_elems - nb_min,1			)			).values
								__lowerCAmelCase											=				(inputs > k_threshold).type(inputs.type()			)
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None, None





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask

