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      (	snake_case__,			snake_case__,			snake_case__,			snake_case__,			snake_case__,			snake_case__ = None,			)  ->      List[str]:
							__UpperCAmelCase  :	Dict   			=      {}

							if train_file is not None:
														__UpperCAmelCase  :	Dict   			=      [train_file]
							if eval_file is not None:
														__UpperCAmelCase  :	Union[str, Any]   			=      [eval_file]
							if test_file is not None:
														__UpperCAmelCase  :	Any   			=      [test_file]

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

							if len(snake_case__							) == 1:
														for k in files.keys():
																					__UpperCAmelCase  :	Optional[int]   			=      ds[k].map(
																					    lambda snake_case__							: tokenizer.batch_encode_plus(
																					        example[features_name[0]],			truncation=snake_case__,			max_length=snake_case__,			padding="max_length"							),			batched=snake_case__,			)
							elif len(snake_case__							) == 2:
														for k in files.keys():
																					__UpperCAmelCase  :	Dict   			=      ds[k].map(
																					    lambda snake_case__							: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]]),			truncation=snake_case__,			max_length=snake_case__,			padding="max_length",			),			batched=snake_case__,			)

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

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

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

							__UpperCAmelCase  :	str   			=      (
							    tf.data.Dataset.from_generator(
							        snake_case__,			({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:
														__UpperCAmelCase  :	Dict   			=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

							__UpperCAmelCase  :	List[Any]   			=      (
							    tf.data.Dataset.from_generator(
							        snake_case__,			({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:
														__UpperCAmelCase  :	str   			=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

							__UpperCAmelCase  :	List[str]   			=      (
							    tf.data.Dataset.from_generator(
							        snake_case__,			({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:
														__UpperCAmelCase  :	Optional[int]   			=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

							return train_ds, val_ds, test_ds, labelaid


_snake_case								=  logging.getLogger(__name__)




@dataclass
class     _snake_case  :
					lowerCamelCase__:						str     =   field(metadata={"help": "Which column contains the label"} )
					lowerCamelCase__:						Tuple     =   field(default=lowerCamelCase_    ,					metadata={"help": "The path of the training file"} )
					lowerCamelCase__:						Dict     =   field(default=lowerCamelCase_    ,					metadata={"help": "The path of the development file"} )
					lowerCamelCase__:						str     =   field(default=lowerCamelCase_    ,					metadata={"help": "The path of the test file"} )
					lowerCamelCase__:						Tuple     =   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."
					        )
					    }    ,					)
					lowerCamelCase__:						Union[str, Any]     =   field(
					    default=lowerCamelCase_    ,					metadata={"help": "Overwrite the cached training and evaluation sets"} )




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




def      _UpperCamelCase      (	)  ->      Any:
							# 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.
							__UpperCAmelCase  :	Any   			=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
							__UpperCAmelCase			,		__UpperCAmelCase			,		__UpperCAmelCase  :	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,			)
							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.

							__UpperCAmelCase  :	List[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,			)

							__UpperCAmelCase			,		__UpperCAmelCase			,		__UpperCAmelCase			,		__UpperCAmelCase  :	Optional[int]   			=      get_tfds(
							    train_file=data_args.train_file,			eval_file=data_args.dev_file,			test_file=data_args.test_file,			tokenizer=snake_case__,			label_column_id=data_args.label_column_id,			max_seq_length=data_args.max_seq_length,			)

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

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

							def compute_metrics(snake_case__							) -> Dict:
														__UpperCAmelCase  :	List[str]   			=      np.argmax(p.predictions,			axis=1							)

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

							# Initialize our Trainer
							__UpperCAmelCase  :	Any   			=      TFTrainer(
							    model=snake_case__,			args=snake_case__,			train_dataset=snake_case__,			eval_dataset=snake_case__,			compute_metrics=snake_case__,			)

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

							# Evaluation
							__UpperCAmelCase  :	Tuple   			=      {}
							if training_args.do_eval:
														logger.info("*** Evaluate ***"							)
														__UpperCAmelCase  :	Optional[int]   			=      trainer.evaluate()
														__UpperCAmelCase  :	Any   			=      os.path.join(training_args.output_dir,			"eval_results.txt"							)

														with open(snake_case__,			"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(snake_case__							)

							return results


if __name__ == "__main__":
	main()











'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      int						)	->	bool:
				if num < 0:
								return False

				_a								= num
				_a								= 0
				while num > 0:
								_a								= rev_num * 10 + (num % 10)
								num //= 10

				return num_copy == rev_num


if __name__ == "__main__":
					import doctest

					doctest.testmod()
