





"""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     __A      (			a_						:str   ,       a_						:str   ,       a_						:str   ,       a_						:PreTrainedTokenizer   ,       a_						:int   ,       a_						:Optional[int] = None   ,       )		->		Union[str, Any]:
				__a :						Any			       =  {}

				if train_file is not None:
								__a :						str			       =  [train_file]
				if eval_file is not None:
								__a :						Optional[int]			       =  [eval_file]
				if test_file is not None:
								__a :						Tuple			       =  [test_file]

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

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

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

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

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

				__a :						Tuple			       =  (
				    tf.data.Dataset.from_generator(
				        a_   ,       ({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:
								__a :						Optional[int]			       =  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

				__a :						int			       =  (
				    tf.data.Dataset.from_generator(
				        a_   ,       ({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:
								__a :						Union[str, Any]			       =  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

				__a :						int			       =  (
				    tf.data.Dataset.from_generator(
				        a_   ,       ({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:
								__a :						Dict			       =  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

				return train_ds, val_ds, test_ds, labelaid


A 			=  logging.getLogger(__name__)




@dataclass
class 				__lowercase			:







					'''simple docstring'''




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






@dataclass
class 				__lowercase			:







					'''simple docstring'''




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






def     __A      (			)		->		Union[str, 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.
				__a :						Dict			       =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
				__a       ,						__a       ,						__a :						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.

				__a :						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   ,       )

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

				__a :						Optional[int]			       =  AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,       num_labels=len(a_)   ,       labelaid=a_   ,       idalabel={id: label for label, id in labelaid.items()}   ,       finetuning_task='''text-classification'''   ,       cache_dir=model_args.cache_dir   ,       )

				with training_args.strategy.scope():
								__a :						List[str]			       =  TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path   ,       from_pt=bool('''.bin''' in model_args.model_name_or_path)   ,       config=a_   ,       cache_dir=model_args.cache_dir   ,       )

				def compute_metrics(a_						:EvalPrediction) -> Dict:
								__a :						Tuple			       =  np.argmax(p.predictions   ,       axis=1)

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

				# Initialize our Trainer
				__a :						int			       =  TFTrainer(
				    model=a_   ,       args=a_   ,       train_dataset=a_   ,       eval_dataset=a_   ,       compute_metrics=a_   ,       )

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

				# Evaluation
				__a :						str			       =  {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''')
								__a :						List[str]			       =  trainer.evaluate()
								__a :						Optional[Any]			       =  os.path.join(training_args.output_dir   ,       '''eval_results.txt''')

								with open(a_   ,       '''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(a_)

				return results


if __name__ == "__main__":
		main()






"""simple docstring"""






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=_UpperCamelCase							)
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  field(default='''language-modeling'''   ,      metadata={'''include_in_asdict_even_if_is_default''': True}							)

					__lowerCAmelCase  =  Features({'''text''': Value('''string'''							)}							)
					__lowerCAmelCase  =  Features({}							)
					__lowerCAmelCase  =  "text"







					@property
					def  _lowerCamelCase		(						self     ):
									return {self.text_column: "text"}