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


__UpperCamelCase		:       Optional[Any]									=     logging.getLogger(__name__)
def       A	(    _lowercase		,  _lowercase						):
						return (preds == labels).mean()





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






@dataclass
class 					lowercase__						:
						UpperCamelCase_     =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys())})
						UpperCamelCase_     =		field(metadata={"""help""": """Should contain the data files for the task."""})
						UpperCamelCase_     =		field(
						    default=128	,							metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }	,							)
						UpperCamelCase_     =		field(
						    default=UpperCamelCase_	,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""})






def       A	(    ):
						# 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.

						SCREAMING_SNAKE_CASE							:    List[str]							=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[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'''		,  _lowercase						)

						# Set seed
						set_seed(training_args.seed						)

						try:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							processors[data_args.task_name]()
												SCREAMING_SNAKE_CASE							:    Any							=							processor.get_labels()
												SCREAMING_SNAKE_CASE							:    List[str]							=							len(_lowercase						)
						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.

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,  num_labels=_lowercase		,  finetuning_task=data_args.task_name		,  cache_dir=model_args.cache_dir		,  )
						SCREAMING_SNAKE_CASE							:    int							=							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		,  )
						SCREAMING_SNAKE_CASE							:    Tuple							=							AutoModelForMultipleChoice.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
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir		,  tokenizer=_lowercase		,  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
						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir		,  tokenizer=_lowercase		,  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(_lowercase						) -> Dict:
												SCREAMING_SNAKE_CASE							:    Any							=							np.argmax(p.predictions		,  axis=1						)
												return {"acc": simple_accuracy(_lowercase		,  p.label_ids						)}

						# Data collator
						SCREAMING_SNAKE_CASE							:    List[str]							=							DataCollatorWithPadding(_lowercase		,  pad_to_multiple_of=8						) if training_args.fpaa else None

						# Initialize our Trainer
						SCREAMING_SNAKE_CASE							:    str							=							Trainer(
						    model=_lowercase		,  args=_lowercase		,  train_dataset=_lowercase		,  eval_dataset=_lowercase		,  compute_metrics=_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						)

    # Evaluation
						SCREAMING_SNAKE_CASE							:    Any							=							{}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							trainer.evaluate()

												SCREAMING_SNAKE_CASE							:    int							=							os.path.join(training_args.output_dir		,  '''eval_results.txt'''						)
												if trainer.is_world_master():
																		with open(_lowercase		,  '''w'''						) as writer:
																								logger.info('''***** Eval results *****'''						)
																								for key, value in result.items():
																														logger.info('''  %s = %s'''		,  _lowercase		,  _lowercase						)
																														writer.write('''%s = %s\n''' % (key, value)						)

																								results.update(_lowercase						)

						return results
def       A	(    _lowercase						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
					main()






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
					import accelerate
def       A	(    _lowercase						):
						if not is_accelerate_available():
												return method
						SCREAMING_SNAKE_CASE							:    int							=							version.parse(accelerate.__version__						).base_version
						if version.parse(_lowercase						) < version.parse('''0.17.0'''						):
												return method

						def wrapper(self		,  *_lowercase		,  **_lowercase						):
												if hasattr(self		,  '''_hf_hook'''						) and hasattr(self._hf_hook		,  '''pre_forward'''						):
																		self._hf_hook.pre_forward(self						)
												return method(self		,  *_lowercase		,  **_lowercase						)

						return wrapper





