



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


__magic_name__       =     logging.getLogger(__name__)


@dataclass
class 	a__       :


					"""simple docstring"""



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




@dataclass
class 	a__       :


					"""simple docstring"""



					A__  :				str					 =				field(
					    metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()       )}       )
					A__  :				str					 =				field(
					    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}       )
					A__  :				int					 =				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.'''
					        )
					    }  ,			)
					A__  :				bool					 =				field(
					    default=_snake_case  ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       default_data_collator(_UpperCAmelCase				)
					lowercase						   =       batch.pop('pairID'       ,			_UpperCAmelCase				)
					return batch





def 		__snake_case						(    ):
					"""simple docstring"""






					lowercase						   =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
					lowercase   ,      lowercase   ,      lowercase						   =       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:
										lowercase						   =       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						   =       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       ,			)
					lowercase						   =       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						   =       AutoModelForSequenceClassification.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
					lowercase						   =       (
					    HansDataset(
					        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       ,			)
					    if training_args.do_train
					    else None
					)
					lowercase						   =       (
					    HansDataset(
					        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       ,			evaluate=_UpperCAmelCase       ,			)
					    if training_args.do_eval
					    else None
					)

					# Initialize our Trainer
					lowercase						   =       Trainer(
					    model=_UpperCAmelCase       ,			args=_UpperCAmelCase       ,			train_dataset=_UpperCAmelCase       ,			eval_dataset=_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
					if training_args.do_eval:
										logger.info('*** Evaluate ***'				)

										lowercase						   =       trainer.predict(_UpperCAmelCase				)
										lowercase						   =       output.predictions
										lowercase						   =       np.argmax(_UpperCAmelCase       ,			axis=1				)

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

										trainer._log(output.metrics				)





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					main()


if __name__ == "__main__":
		main()











import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
		from .testing_utils import (
		    floats_tensor,
		    load_hf_numpy,
		    load_image,
		    load_numpy,
		    load_pt,
		    nightly,
		    parse_flag_from_env,
		    print_tensor_test,
		    require_torch_a,
		    require_torch_gpu,
		    skip_mps,
		    slow,
		    torch_all_close,
		    torch_device,
		)
		from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


__magic_name__       =     get_logger(__name__)





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if version.parse(_UpperCAmelCase				) < version.parse(_UpperCAmelCase				):
										if "dev" in min_version:
															lowercase						   =       (
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															lowercase						   =       f"""This example requires a minimum version of {min_version},"""
										error_message += f""" but the version found is {__version__}.\n"""
										raise ImportError(_UpperCAmelCase				)






