





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
UpperCAmelCase__  :     int   			=HfArgumentParser(InitializationArguments)
UpperCAmelCase__  :     str   			=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
UpperCAmelCase__  :     List[Any]   			=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
UpperCAmelCase__  :     Optional[int]   			={
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
UpperCAmelCase__  :     Optional[Any]   			=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
UpperCAmelCase__  :     Dict   			=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)












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()





