


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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A   :					int	=      logging.getLogger(__name__)

def    lowercase_	(	_A    :					Dict					,      _A    :					str		):

						"""simple docstring"""




						return (preds == labels).mean()







@dataclass
class     _lowercase				:


							"""simple docstring"""




							A__      		=    field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
							A__      		=    field(
							    default=lowercase__							,						metadata={"help": "Pretrained config name or path if not the same as model_name"})
							A__      		=    field(
							    default=lowercase__							,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
							A__      		=    field(
							    default=lowercase__							,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,						)







@dataclass
class     _lowercase				:


							"""simple docstring"""




							A__      		=    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
							A__      		=    field(metadata={"help": "Should contain the data files for the task."})
							A__      		=    field(
							    default=1_28							,						metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }							,						)
							A__      		=    field(
							    default=lowercase__							,						metadata={"help": "Overwrite the cached training and evaluation sets"})



def    lowercase_	(	):

						"""simple docstring"""




						lowerCamelCase__	:       List[Any]					=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
						lowerCamelCase__	:       int					=						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		)

						# Set seed
						set_seed(training_args.seed		)

						try:
												lowerCamelCase__	:       Dict					=						processors[data_args.task_name]()
												lowerCamelCase__	:       Dict					=						processor.get_labels()
												lowerCamelCase__	:       Optional[int]					=						len(_A		)
						except KeyError:
												raise ValueError("Task not found: %s" % (data_args.task_name)		)

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

						lowerCamelCase__	:       Union[str, Any]					=						AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,      num_labels=_A					,      finetuning_task=data_args.task_name					,      cache_dir=model_args.cache_dir					,      )
						lowerCamelCase__	:       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					,      )
						lowerCamelCase__	:       str					=						AutoModelForMultipleChoice.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
						lowerCamelCase__	:       int					=						(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir					,      tokenizer=_A					,      task=data_args.task_name					,      max_seq_length=data_args.max_seq_length					,      overwrite_cache=data_args.overwrite_cache					,      mode=Split.train					,      )
						    if training_args.do_train
						    else None
						)
						lowerCamelCase__	:       Tuple					=						(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir					,      tokenizer=_A					,      task=data_args.task_name					,      max_seq_length=data_args.max_seq_length					,      overwrite_cache=data_args.overwrite_cache					,      mode=Split.dev					,      )
						    if training_args.do_eval
						    else None
						)

						def compute_metrics(_A    :					EvalPrediction		) -> Dict:
												lowerCamelCase__	:       Optional[Any]					=						np.argmax(p.predictions					,      axis=1		)
												return {"acc": simple_accuracy(_A					,      p.label_ids		)}

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

						# Initialize our Trainer
						lowerCamelCase__	:       List[str]					=						Trainer(
						    model=_A					,      args=_A					,      train_dataset=_A					,      eval_dataset=_A					,      compute_metrics=_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		)

    # Evaluation
						lowerCamelCase__	:       int					=						{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"		)

												lowerCamelCase__	:       str					=						trainer.evaluate()

												lowerCamelCase__	:       List[Any]					=						os.path.join(training_args.output_dir					,      "eval_results.txt"		)
												if trainer.is_world_master():
																		with open(_A					,      "w"		) as writer:
																								logger.info("***** Eval results *****"		)
																								for key, value in result.items():
																														logger.info("  %s = %s"					,      _A					,      _A		)
																														writer.write("%s = %s\n" % (key, value)		)

																								results.update(_A		)

						return results

def    lowercase_	(	_A    :					Tuple		):

						"""simple docstring"""




						main()


if __name__ == "__main__":
				main()






from collections import OrderedDict
from typing import Mapping

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


A   :					int	=      logging.get_logger(__name__)

A   :					Optional[int]	=      {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "xmod"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Any=30522      ,   __lowerCamelCase							:    Any=768      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    Any=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    List[Any]="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    List[str]=0.0_2      ,   __lowerCamelCase							:    List[str]=1E-1_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Optional[int]=0      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    str="absolute"      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    Optional[Any]=False      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    str=("en_XX",)      ,   __lowerCamelCase							:    Union[str, Any]=None      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       List[Any]					=						num_attention_heads
             lowerCamelCase__	:       Union[str, Any]					=						hidden_act
             lowerCamelCase__	:       Optional[int]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       List[Any]					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Any					=						max_position_embeddings
             lowerCamelCase__	:       List[Any]					=						type_vocab_size
             lowerCamelCase__	:       int					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       Union[str, Any]					=						position_embedding_type
             lowerCamelCase__	:       str					=						use_cache
             lowerCamelCase__	:       Union[str, Any]					=						classifier_dropout
             lowerCamelCase__	:       Any					=						pre_norm
             lowerCamelCase__	:       Tuple					=						adapter_reduction_factor
             lowerCamelCase__	:       Tuple					=						adapter_layer_norm
             lowerCamelCase__	:       List[Any]					=						adapter_reuse_layer_norm
             lowerCamelCase__	:       Dict					=						ln_before_adapter
             lowerCamelCase__	:       List[Any]					=						list(__lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						default_language







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       Dict					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       List[str]					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


