




'''simple docstring'''

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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = None,      )		->					str:

					'''simple docstring'''

					snake_case_									=				{}

					if train_file is not None:
										snake_case_									=				[train_file]
					if eval_file is not None:
										snake_case_									=				[eval_file]
					if test_file is not None:
										snake_case_									=				[test_file]

					snake_case_									=				datasets.load_dataset('''csv''',      data_files=__UpperCAmelCase	)
					snake_case_									=				list(ds[list(files.keys()	)[0]].features.keys()	)
					snake_case_									=				features_name.pop(__UpperCAmelCase	)
					snake_case_									=				list(set(ds[list(files.keys()	)[0]][label_name]	)	)
					snake_case_									=				{label: i for i, label in enumerate(__UpperCAmelCase	)}
					snake_case_									=				tokenizer.model_input_names
					snake_case_									=				{}

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

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

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

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

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

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

					snake_case_									=				(
					    tf.data.Dataset.from_generator(
					        __UpperCAmelCase,      ({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:
										snake_case_									=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

					return train_ds, val_ds, test_ds, labelaid


a  :				Tuple									=  logging.getLogger(__name__)






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







@dataclass
class 						a				:
				snake_case_      =		field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
				snake_case_      =		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.
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,     )







def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
					snake_case_			,snake_case_			,snake_case_									=				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.

					snake_case_									=				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,      )

					snake_case_			,snake_case_			,snake_case_			,snake_case_									=				get_tfds(
					    train_file=data_args.train_file,      eval_file=data_args.dev_file,      test_file=data_args.test_file,      tokenizer=__UpperCAmelCase,      label_column_id=data_args.label_column_id,      max_seq_length=data_args.max_seq_length,      )

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

					with training_args.strategy.scope():
										snake_case_									=				TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path,      from_pt=bool('''.bin''' in model_args.model_name_or_path	),      config=__UpperCAmelCase,      cache_dir=model_args.cache_dir,      )

					def compute_metrics(__UpperCAmelCase	) -> Dict:
										snake_case_									=				np.argmax(p.predictions,      axis=1	)

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

					# Initialize our Trainer
					snake_case_									=				TFTrainer(
					    model=__UpperCAmelCase,      args=__UpperCAmelCase,      train_dataset=__UpperCAmelCase,      eval_dataset=__UpperCAmelCase,      compute_metrics=__UpperCAmelCase,      )

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

					# Evaluation
					snake_case_									=				{}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''	)
										snake_case_									=				trainer.evaluate()
										snake_case_									=				os.path.join(training_args.output_dir,      '''eval_results.txt'''	)

										with open(__UpperCAmelCase,      '''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(__UpperCAmelCase	)

					return results


if __name__ == "__main__":
							main()





def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  bool = False						):

       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Optional[Any]				    = f"Expected string as input, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)
       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Union[str, Any]				    = f"Expected boolean as use_pascal parameter, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)

       _A      :       int				    = input_str.split("_"						)

       _A      :       str				    = 0 if use_pascal else 1

       _A      :       str				    = words[start_index:]

       _A      :       Optional[Any]				    = [word[0].upper() + word[1:] for word in words_to_capitalize]

       _A      :       Any				    = "" if use_pascal else words[0]

       return "".join([initial_word, *capitalized_words]						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
