





import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def        _lowercase   (  _UpperCAmelCase			)  ->					str:
				lowerCamelCase			=[]
				for line in lines:
								lowerCamelCase			=re.sub(r"""#.*"""     , """"""     , _UpperCAmelCase			)  # remove comments
								if line:
												filtered_lines.append(_UpperCAmelCase			)
				lowerCamelCase			="""\n""".join(_UpperCAmelCase			)

				# Make a hash from all this code
				lowerCamelCase			=full_str.encode("""utf-8"""			)
				return shaaaa(_UpperCAmelCase			).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase__  :     str   			={
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase__  :     Tuple   			={
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase__  :     Optional[Any]   			={'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
UpperCAmelCase__  :     Dict[str, List[str]]   			={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')




_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')












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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:
				return (preds == labels).mean()

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




@dataclass
class  __A       :
				__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=a      ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )





def        _lowercase   (  )  ->					Optional[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)			)
				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"""     , _UpperCAmelCase			)

				# Set seed
				set_seed(training_args.seed			)

				try:
								lowerCamelCase			=processors[data_args.task_name]()
								lowerCamelCase			=processor.get_labels()
								lowerCamelCase			=len(_UpperCAmelCase			)
				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			=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path     , num_labels=_UpperCAmelCase     , finetuning_task=data_args.task_name     , 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     , )
				lowerCamelCase			=AutoModelForMultipleChoice.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
				lowerCamelCase			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , 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			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , 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(_UpperCAmelCase			) -> Dict:
								lowerCamelCase			=np.argmax(p.predictions     , axis=1			)
								return {"acc": simple_accuracy(_UpperCAmelCase     , p.label_ids			)}

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

				# Initialize our Trainer
				lowerCamelCase			=Trainer(
				    model=_UpperCAmelCase     , args=_UpperCAmelCase     , train_dataset=_UpperCAmelCase     , eval_dataset=_UpperCAmelCase     , compute_metrics=_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			)

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

								lowerCamelCase			=trainer.evaluate()

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

																results.update(_UpperCAmelCase			)

				return results





def        _lowercase   (  _UpperCAmelCase			)  ->					Union[str, Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()





