





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__lowercase       :str       =  {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

__lowercase       :List[str]       =  logging.WARNING







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					os.getenv("DATASETS_VERBOSITY"			,   UpperCAmelCase__     )
						if env_level_str:
												if env_level_str in log_levels:
																		return log_levels[env_level_str]
												else:
																		logging.getLogger().warning(
																		    f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
																		    f"""has to be one of: { ', '.join(log_levels.keys()     ) }"""     )
						return _default_log_level







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						return __name__.split("."     )[0]







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						return logging.getLogger(_get_library_name()     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					_get_library_root_logger()
						library_root_logger.setLevel(_get_default_logging_level()     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[str]   =					_get_library_root_logger()
						library_root_logger.setLevel(logging.NOTSET     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[str] = None     ):





						'''simple docstring'''


						if name is None:
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					_get_library_name()
						return logging.getLogger(UpperCAmelCase__     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						return _get_library_root_logger().getEffectiveLevel()







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						_get_library_root_logger().setLevel(UpperCAmelCase__     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						return set_verbosity(UpperCAmelCase__     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						return set_verbosity(UpperCAmelCase__     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						return set_verbosity(UpperCAmelCase__     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						return set_verbosity(UpperCAmelCase__     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					False







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Union[str, Any]							,	*a				:     Any							,	**a				:     int    )	->Tuple:  # pylint: disable=unused-argument
													SCREAMING_SNAKE_CASE__     :				Any   =					args[0] if args else None

							def __iter__(      self				:     Tuple    )	->Tuple:
													return iter(self._iterator    )

							def __getattr__(      self				:     str							,	a				:     List[str]    )	->List[str]:

													def empty_fn(*a				:     List[str]							,	**a				:     str    ):  # pylint: disable=unused-argument
																			return

													return empty_fn

							def __enter__(      self				:     Union[str, Any]    )	->int:
													return self






							def __exit__(      self				:     Optional[Any]							,	a				:     List[str]							,	a				:     str							,	a				:     Dict    )	->int:
													return







__lowercase       :List[str]       =  True



class    _a     :






							"""simple docstring"""

							def __call__(      self				:     Any							,	*a				:     str							,	a				:     str=False							,	**a				:     Optional[Any]    )	->Union[str, Any]:
													if _tqdm_active and not disable:
																			return tqdm_lib.tqdm(*__UpperCamelCase							,	**__UpperCamelCase    )
													else:
																			return EmptyTqdm(*__UpperCamelCase							,	**__UpperCamelCase    )

							def      A_  (      self				:     Optional[Any]							,	*a				:     List[Any]							,	**a				:     List[Any]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					None
													if _tqdm_active:
																			return tqdm_lib.tqdm.set_lock(*__UpperCamelCase							,	**__UpperCamelCase    )






							def      A_  (      self				:     List[str]    )	->Optional[int]:
													if _tqdm_active:
																			return tqdm_lib.tqdm.get_lock()







__lowercase       :Any       =  _tqdm_cls()







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						global _tqdm_active
						return bool(_tqdm_active     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						global _tqdm_active
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					True







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						global _tqdm_active
						SCREAMING_SNAKE_CASE__     :				List[str]   =					False






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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  PreTrainedTokenizer			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  Optional[int] = None			,   ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					{}

						if train_file is not None:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[train_file]
						if eval_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[eval_file]
						if test_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[test_file]

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					datasets.load_dataset("csv"			,   data_files=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					list(ds[list(files.keys()     )[0]].features.keys()     )
						SCREAMING_SNAKE_CASE__     :				int   =					features_name.pop(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					list(set(ds[list(files.keys()     )[0]][label_name]     )     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					{label: i for i, label in enumerate(_lowerCamelCase     )}
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tokenizer.model_input_names
						SCREAMING_SNAKE_CASE__     :				Any   =					{}

						if len(_lowerCamelCase     ) == 1:
												for k in files.keys():
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					ds[k].map(
																		    lambda _lowerCamelCase     : tokenizer.batch_encode_plus(
																		        example[features_name[0]]			,   truncation=_lowerCamelCase			,   max_length=_lowerCamelCase			,   padding="max_length"     )			,   batched=_lowerCamelCase			,   )
						elif len(_lowerCamelCase     ) == 2:
												for k in files.keys():
																		SCREAMING_SNAKE_CASE__     :				Any   =					ds[k].map(
																		    lambda _lowerCamelCase     : tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]])			,   truncation=_lowerCamelCase			,   max_length=_lowerCamelCase			,   padding="max_length"			,   )			,   batched=_lowerCamelCase			,   )

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		SCREAMING_SNAKE_CASE__     :				Tuple   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		SCREAMING_SNAKE_CASE__     :				int   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		SCREAMING_SNAKE_CASE__     :				int   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					labelaid[ex[label_name]]
																		yield (d, label)

						SCREAMING_SNAKE_CASE__     :				Tuple   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Any   =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Dict   =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]     )     )     )

						return train_ds, val_ds, test_ds, labelaid


__lowercase       :List[Any]       =  logging.getLogger(__name__)



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						field(metadata={"help": "Which column contains the label"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the training file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the development file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the test file"}							)
							snake_case_							 =						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."
							        )
							    }					,							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Overwrite the cached training and evaluation sets"}							)







@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "Set this flag to use fast tokenization."}							)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,							)







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
						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			,   )
						logger.info(
						    f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1     )}, """
						    f"""16-bits training: {training_args.fpaa}"""     )
						logger.info(f"""Training/evaluation parameters {training_args}"""     )

						# 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__     :				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			,   )

						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_tfds(
						    train_file=data_args.train_file			,   eval_file=data_args.dev_file			,   test_file=data_args.test_file			,   tokenizer=_lowerCamelCase			,   label_column_id=data_args.label_column_id			,   max_seq_length=data_args.max_seq_length			,   )

						SCREAMING_SNAKE_CASE__     :				str   =					AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,   num_labels=len(_lowerCamelCase     )			,   labelaid=_lowerCamelCase			,   idalabel={id: label for label, id in labelaid.items()}			,   finetuning_task="text-classification"			,   cache_dir=model_args.cache_dir			,   )

						with training_args.strategy.scope():
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path			,   from_pt=bool(".bin" in model_args.model_name_or_path     )			,   config=_lowerCamelCase			,   cache_dir=model_args.cache_dir			,   )

						def compute_metrics(_lowerCamelCase      :  EvalPrediction     ) -> Dict:
												SCREAMING_SNAKE_CASE__     :				Dict   =					np.argmax(p.predictions			,   axis=1     )

												return {"acc": (preds == p.label_ids).mean()}

						# Initialize our Trainer
						SCREAMING_SNAKE_CASE__     :				str   =					TFTrainer(
						    model=_lowerCamelCase			,   args=_lowerCamelCase			,   train_dataset=_lowerCamelCase			,   eval_dataset=_lowerCamelCase			,   compute_metrics=_lowerCamelCase			,   )

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir     )

						# Evaluation
						SCREAMING_SNAKE_CASE__     :				Dict   =					{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"     )
												SCREAMING_SNAKE_CASE__     :				str   =					trainer.evaluate()
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					os.path.join(training_args.output_dir			,   "eval_results.txt"     )

												with open(_lowerCamelCase			,   "w"     ) as writer:
																		logger.info("***** Eval results *****"     )

																		for key, value in result.items():
																								logger.info(f"""  {key} = {value}"""     )
																								writer.write(f"""{key} = {value}\n"""     )

																		results.update(_lowerCamelCase     )

						return results


if __name__ == "__main__":
				main()