
"""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_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase						,	__lowercase						,	__lowercase = None						,	):
 UpperCamelCase_     :					List[str]   	={}

 if train_file is not None:
  UpperCamelCase_     :					Union[str, Any]   	=[train_file]
 if eval_file is not None:
  UpperCamelCase_     :					Tuple   	=[eval_file]
 if test_file is not None:
  UpperCamelCase_     :					int   	=[test_file]

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

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

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

 UpperCamelCase_     :					Union[str, Any]   	=(
     tf.data.Dataset.from_generator(
         __lowercase						,	({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_     :					Any   	=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

 UpperCamelCase_     :					Optional[Any]   	=(
     tf.data.Dataset.from_generator(
         __lowercase						,	({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_     :					Union[str, Any]   	=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

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

 return train_ds, val_ds, test_ds, labelaid


__SCREAMING_SNAKE_CASE							=     logging.getLogger(__name__)





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





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




def       A_  (				):
 # 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_     :					List[str]   	=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
 UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					Optional[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_     :					int   	=get_tfds(
     train_file=data_args.train_file						,	eval_file=data_args.dev_file						,	test_file=data_args.test_file						,	tokenizer=__lowercase						,	label_column_id=data_args.label_column_id						,	max_seq_length=data_args.max_seq_length						,	)

 UpperCamelCase_     :					List[Any]   	=AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path						,	num_labels=len(__lowercase	)						,	labelaid=__lowercase						,	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_     :					Dict   	=TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path						,	from_pt=bool('.bin' in model_args.model_name_or_path	)						,	config=__lowercase						,	cache_dir=model_args.cache_dir						,	)

 def compute_metrics(__lowercase	) -> Dict:
  UpperCamelCase_     :					Any   	=np.argmax(p.predictions						,	axis=1	)

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

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

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

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

  with open(__lowercase						,	'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(__lowercase	)

 return results


if __name__ == "__main__":
 main()


"""simple docstring"""






# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 				a__					:



     def __init__(							self :List[str]      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :bool = True      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=scheduler
      UpperCamelCase_     :					Any   	=optimizers if isinstance(_lowerCamelCase      ,  (list, tuple)   ) else [optimizers]
      UpperCamelCase_     :					Dict   	=split_batches
      UpperCamelCase_     :					str   	=step_with_optimizer
      UpperCamelCase_     :					Dict   	=GradientState()



     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      if not self.step_with_optimizer:
       # No link between scheduler and optimizer -> just step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
       return

      # Otherwise, first make sure the optimizer was stepped.
      if not self.gradient_state.sync_gradients:
       if self.gradient_state.adjust_scheduler:
        self.scheduler._step_count += 1
       return

      for opt in self.optimizers:
       if opt.step_was_skipped:
        return
      if self.split_batches:
       # Split batches -> the training dataloader batch size is not changed so one step per training step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
      else:
       # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
       # num_processes steps per training step
       UpperCamelCase_     :					Optional[Any]   	=AcceleratorState().num_processes
       for _ in range(_lowerCamelCase   ):
        # Special case when using OneCycle and `drop_last` was not used
        if hasattr(self.scheduler      ,  'total_steps'   ):
         if self.scheduler._step_count <= self.scheduler.total_steps:
          self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
        else:
         self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      return self.scheduler.get_last_lr()



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      return self.scheduler.state_dict()



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      self.scheduler.load_state_dict(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      return self.scheduler.get_lr()




     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :Union[str, Any]      ,  **_lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      return self.scheduler.print_lr(*_lowerCamelCase      ,  **_lowerCamelCase   )
