




"""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					:     Union[str, Any]				       =    logging.getLogger(__name__)

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

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Tuple						=						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.
		A_				,	A_				,	A_					:			Union[str, Any]						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
	else:
		A_				,	A_				,	A_					:			Any						=						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'''					, _UpperCAmelCase		)

	# 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.

	A_					:			List[str]						=						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					, )
	A_					:			Optional[Any]						=						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=_UpperCAmelCase					, )
	A_					:			Optional[Any]						=						AutoModelForQuestionAnswering.from_pretrained(
	    model_args.model_name_or_path					, from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)					, config=_UpperCAmelCase					, cache_dir=model_args.cache_dir					, )

	# Get datasets
	A_					:			str						=						hasattr(model.config					, '''lang2id'''		)
	A_					:			List[Any]						=						(
	    SquadDataset(
	        _UpperCAmelCase					, tokenizer=_UpperCAmelCase					, is_language_sensitive=_UpperCAmelCase					, cache_dir=model_args.cache_dir		)
	    if training_args.do_train
	    else None
	)
	A_					:			Dict						=						(
	    SquadDataset(
	        _UpperCAmelCase					, tokenizer=_UpperCAmelCase					, mode='''dev'''					, is_language_sensitive=_UpperCAmelCase					, cache_dir=model_args.cache_dir					, )
	    if training_args.do_eval
	    else None
	)

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

	# Initialize our Trainer
	A_					:			Tuple						=						Trainer(
	    model=_UpperCAmelCase					, args=_UpperCAmelCase					, train_dataset=_UpperCAmelCase					, eval_dataset=_UpperCAmelCase					, data_collator=_UpperCAmelCase					, )

	# 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 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	main()


if __name__ == "__main__":
		main()











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase					:     List[Any]				       =    logging.get_logger(__name__)

_lowerCamelCase					:     Dict				       =    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase ,       bos_token_id=_lowerCamelCase ,       eos_token_id=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       forced_eos_token_id=_lowerCamelCase ,       **_lowerCamelCase ,       )

class     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.use_past:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )
					else:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )





