"""simple docstring"""






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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A         =				logging.getLogger(__name__)


@dataclass
class 					UpperCAmelCase		:



				"""simple docstring"""

				_UpperCAmelCase			:str							=				field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
				_UpperCAmelCase			:Union[str, Any]							=				field(
				    default=UpperCamelCase__					,metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
				_UpperCAmelCase			:Dict							=				field(
				    default=UpperCamelCase__					,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
				_UpperCAmelCase			:Optional[int]							=				field(
				    default=UpperCamelCase__					,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,)



@dataclass
class 					UpperCAmelCase		:



				"""simple docstring"""

				_UpperCAmelCase			:Optional[Any]							=				field(
				    metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()						)}						)
				_UpperCAmelCase			:Optional[int]							=				field(
				    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
				_UpperCAmelCase			:Tuple							=				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			:Union[str, Any]							=				field(
				    default=UpperCamelCase__					,metadata={"help": "Overwrite the cached training and evaluation sets"}						)



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Dict[str, torch.Tensor]:
			lowercase__:     List[Any]      =   default_data_collator(__a				)
			lowercase__:     Optional[int]      =   batch.pop('''pairID''' ,					__a				)
			return batch



def SCREAMING_SNAKE_CASE__    (     )						->						Dict:
			lowercase__:     Union[str, Any]      =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
			lowercase__:     Optional[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:
						lowercase__:     Dict      =   hans_tasks_num_labels[data_args.task_name]
			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.

			lowercase__:     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 ,					)
			lowercase__:     Union[str, 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 ,					)
			lowercase__:     Optional[Any]      =   AutoModelForSequenceClassification.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
			lowercase__:     List[Any]      =   (
			    HansDataset(
			        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 ,					)
			    if training_args.do_train
			    else None
			)
			lowercase__:     Union[str, Any]      =   (
			    HansDataset(
			        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 ,					evaluate=__a ,					)
			    if training_args.do_eval
			    else None
			)

			# Initialize our Trainer
			lowercase__:     str      =   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				)

    # Evaluation
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''				)

						lowercase__:     Tuple      =   trainer.predict(__a				)
						lowercase__:     Union[str, Any]      =   output.predictions
						lowercase__:     List[Any]      =   np.argmax(__a ,					axis=1				)

						lowercase__:     List[Any]      =   [ex.pairID for ex in eval_dataset]
						lowercase__:     Union[str, Any]      =   os.path.join(training_args.output_dir ,					'''hans_predictions.txt'''				)
						lowercase__:     int      =   eval_dataset.get_labels()
						if trainer.is_world_master():
									with open(__a ,					'''w'''				) as writer:
												writer.write('''pairID,gold_label\n'''				)
												for pid, pred in zip(__a ,					__a				):
															writer.write('''ex''' + str(__a				) + ''',''' + label_list[int(__a				)] + '''\n'''				)

						trainer._log(output.metrics				)



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Dict:
			main()


if __name__ == "__main__":
							main()

"""simple docstring"""






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


__A         =				logging.get_logger(__name__)

__A         =				{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Tuple							=				"yoso"






    def __init__(     self					,    _UpperCAmelCase=50265					,    _UpperCAmelCase=768					,    _UpperCAmelCase=12					,    _UpperCAmelCase=12					,    _UpperCAmelCase=3072					,    _UpperCAmelCase="gelu"					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=4096					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0.02					,    _UpperCAmelCase=1e-1_2					,    _UpperCAmelCase="absolute"					,    _UpperCAmelCase=True					,    _UpperCAmelCase=9					,    _UpperCAmelCase=64					,    _UpperCAmelCase=None					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0					,    _UpperCAmelCase=2					,    **_UpperCAmelCase					,    ):
       super().__init__(pad_token_id=_UpperCAmelCase					,    bos_token_id=_UpperCAmelCase					,    eos_token_id=_UpperCAmelCase					,    **_UpperCAmelCase							)

       lowercase__:     int      =   vocab_size
       lowercase__:     List[str]      =   max_position_embeddings
       lowercase__:     Tuple      =   hidden_size
       lowercase__:     Optional[Any]      =   num_hidden_layers
       lowercase__:     Optional[int]      =   num_attention_heads
       lowercase__:     List[Any]      =   intermediate_size
       lowercase__:     Union[str, Any]      =   hidden_act
       lowercase__:     int      =   hidden_dropout_prob
       lowercase__:     Optional[Any]      =   attention_probs_dropout_prob
       lowercase__:     List[Any]      =   initializer_range
       lowercase__:     Optional[Any]      =   type_vocab_size
       lowercase__:     int      =   layer_norm_eps
       lowercase__:     Optional[Any]      =   position_embedding_type
       lowercase__:     int      =   use_expectation
       lowercase__:     List[str]      =   hash_code_len
       lowercase__:     Union[str, Any]      =   num_hash
       lowercase__:     Any      =   conv_window
       lowercase__:     Dict      =   use_fast_hash
       lowercase__:     Dict      =   lsh_backward
