




"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase						   =     logging.get_logger(__name__)

_lowercase						   =     {}

class    __lowercase							(      A_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="fsmt_decoder"

				def __init__( self :  List[str]   ,SCREAMING_SNAKE_CASE_ :  Tuple=0   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=0   ):
										super().__init__()
										snake_case__							:       Dict				      =       vocab_size
										snake_case__							:       List[str]				      =       bos_token_id







class    __lowercase							(      A_						):


				'''simple docstring'''


				_lowerCAmelCase						      ="fsmt"
				_lowerCAmelCase						      ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

				def __init__( self :  List[Any]   ,SCREAMING_SNAKE_CASE_ :  Dict=["en", "de"]   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=4_2_0_2_4   ,SCREAMING_SNAKE_CASE_ :  Any=4_2_0_2_4   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]="relu"   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=1_0_2_4   ,SCREAMING_SNAKE_CASE_ :  str=2_0_0   ,SCREAMING_SNAKE_CASE_ :  Tuple=1_0_2_4   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]=4_0_9_6   ,SCREAMING_SNAKE_CASE_ :  Optional[Any]=1_2   ,SCREAMING_SNAKE_CASE_ :  int=1_6   ,SCREAMING_SNAKE_CASE_ :  List[str]=0.0   ,SCREAMING_SNAKE_CASE_ :  Tuple=4_0_9_6   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=1_2   ,SCREAMING_SNAKE_CASE_ :  List[str]=1_6   ,SCREAMING_SNAKE_CASE_ :  Tuple=0.0   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]=0.0   ,SCREAMING_SNAKE_CASE_ :  Any=0.1   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]=0.0   ,SCREAMING_SNAKE_CASE_ :  List[str]=0.02   ,SCREAMING_SNAKE_CASE_ :  List[Any]=2   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=True   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=True   ,SCREAMING_SNAKE_CASE_ :  int=False   ,SCREAMING_SNAKE_CASE_ :  Any=5   ,SCREAMING_SNAKE_CASE_ :  Dict=1.0   ,SCREAMING_SNAKE_CASE_ :  str=False   ,SCREAMING_SNAKE_CASE_ :  Tuple=True   ,SCREAMING_SNAKE_CASE_ :  List[Any]=1   ,SCREAMING_SNAKE_CASE_ :  Tuple=0   ,SCREAMING_SNAKE_CASE_ :  Dict=2   ,SCREAMING_SNAKE_CASE_ :  int=2   ,**SCREAMING_SNAKE_CASE_ :  Dict   ,):
										snake_case__							:       Optional[Any]				      =       langs
										snake_case__							:       List[str]				      =       src_vocab_size
										snake_case__							:       Union[str, Any]				      =       tgt_vocab_size
										snake_case__							:       Tuple				      =       d_model  # encoder_embed_dim and decoder_embed_dim

										snake_case__							:       Tuple				      =       encoder_ffn_dim
										snake_case__							:       Any				      =       encoder_layers
										snake_case__							:       Optional[Any]				      =       encoder_attention_heads
										snake_case__							:       Tuple				      =       encoder_layerdrop
										snake_case__							:       List[Any]				      =       decoder_layerdrop
										snake_case__							:       List[Any]				      =       decoder_ffn_dim
										snake_case__							:       Tuple				      =       decoder_layers
										snake_case__							:       Tuple				      =       decoder_attention_heads
										snake_case__							:       Union[str, Any]				      =       max_position_embeddings
										snake_case__							:       int				      =       init_std  # Normal(0, this parameter)
										snake_case__							:       str				      =       activation_function

										snake_case__							:       Dict				      =       DecoderConfig(vocab_size=_A   ,bos_token_id=_A   )
										if "decoder" in common_kwargs:
																del common_kwargs["decoder"]

										snake_case__							:       str				      =       scale_embedding  # scale factor will be sqrt(d_model) if True

										# 3 Types of Dropout
										snake_case__							:       List[Any]				      =       attention_dropout
										snake_case__							:       int				      =       activation_dropout
										snake_case__							:       str				      =       dropout

										snake_case__							:       Optional[Any]				      =       use_cache
										super().__init__(
										    pad_token_id=_A   ,bos_token_id=_A   ,eos_token_id=_A   ,decoder_start_token_id=_A   ,is_encoder_decoder=_A   ,tie_word_embeddings=_A   ,forced_eos_token_id=_A   ,max_length=_A   ,num_beams=_A   ,length_penalty=_A   ,early_stopping=_A   ,**_A   ,)





				def    _lowerCamelCase       ( self :  int   ):
										snake_case__							:       str				      =       copy.deepcopy(self.__dict__   )
										snake_case__							:       List[str]				      =       self.decoder.to_dict()
										snake_case__							:       List[str]				      =       self.__class__.model_type
										return output




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       =					logging.getLogger(__name__)







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



def 		lowercase_     (  )				->				int:
    """simple docstring"""

    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"					,						A__						)

    # 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=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path					,						from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,						config=A__					,						cache_dir=model_args.cache_dir					,						)

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

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

    # Initialize our Trainer
    snake_case						=   Trainer(
        model=A__					,						args=A__					,						train_dataset=A__					,						eval_dataset=A__					,						data_collator=A__					,						)

    # 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_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

