


"""simple docstring"""



import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
 from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
 from tensorflow.keras.optimizers import Adam
class 					__magic_name__		(			tf.keras.optimizers.schedules.LearningRateSchedule     ):






       def __init__(   self  :    List[str]  ,     snake_case__  :    float  ,     snake_case__  :    Callable  ,     snake_case__  :    int  ,     snake_case__  :    float = 1.0  ,     snake_case__  :    str = None  ,     ):

              '''simple docstring'''






              super().__init__()
              lowercase		:Union[str, Any]       							=				initial_learning_rate
              lowercase		:Dict       							=				warmup_steps
              lowercase		:str       							=				power
              lowercase		:int       							=				decay_schedule_fn
              lowercase		:str       							=				name






       def __call__(   self  :    str  ,     snake_case__  :    List[Any]				):

              '''simple docstring'''






              with tf.name_scope(self.name or '''WarmUp'''				) as name:
                     # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
                     # learning rate will be `global_step/num_warmup_steps * init_lr`.
                     lowercase		:Optional[int]       							=				tf.cast(snake_case__  ,     tf.floataa				)
                     lowercase		:Optional[int]       							=				tf.cast(self.warmup_steps  ,     tf.floataa				)
                     lowercase		:Optional[Any]       							=				global_step_float / warmup_steps_float
                     lowercase		:Tuple       							=				self.initial_learning_rate * tf.math.pow(snake_case__  ,     self.power				)
                     return tf.cond(
                         global_step_float < warmup_steps_float  ,     lambda: warmup_learning_rate  ,     lambda: self.decay_schedule_fn(step - self.warmup_steps				)  ,     name=snake_case__  ,     )







       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return {
                  "initial_learning_rate": self.initial_learning_rate,
                  "decay_schedule_fn": self.decay_schedule_fn,
                  "warmup_steps": self.warmup_steps,
                  "power": self.power,
                  "name": self.name,
              }







def       lowerCamelCase					(a_						:float				,				a_						:int				,				a_						:int				,				a_						:float = 0.0				,				a_						:float = 0.9				,				a_						:float = 0.9_99				,				a_						:float = 1E-8				,				a_						:Optional[float] = None				,				a_						:Optional[float] = None				,				a_						:float = 0.0				,				a_						:float = 1.0				,				a_						:Optional[List[str]] = None				,				)     ->							List[Any]:
       lowercase		:Tuple       							=				tf.keras.optimizers.schedules.PolynomialDecay(
           initial_learning_rate=a_				,				decay_steps=num_train_steps - num_warmup_steps				,				end_learning_rate=init_lr * min_lr_ratio				,				power=a_				,				)
       if num_warmup_steps:
              lowercase		:Any       							=				WarmUp(
                  initial_learning_rate=a_				,				decay_schedule_fn=a_				,				warmup_steps=a_				,				)
       if weight_decay_rate > 0.0:
              lowercase		:List[Any]       							=				AdamWeightDecay(
                  learning_rate=a_				,				weight_decay_rate=a_				,				beta_a=a_				,				beta_a=a_				,				epsilon=a_				,				clipnorm=a_				,				global_clipnorm=a_				,				exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']				,				include_in_weight_decay=a_				,				)
       else:
              lowercase		:str       							=				tf.keras.optimizers.Adam(
                  learning_rate=a_				,				beta_a=a_				,				beta_a=a_				,				epsilon=a_				,				clipnorm=a_				,				global_clipnorm=a_				,				)
       # We return the optimizer and the LR scheduler in order to better track the
       # evolution of the LR independently of the optimizer.
       return optimizer, lr_schedule
class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    Any  ,     snake_case__  :    Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_01  ,     snake_case__  :    float = 0.9  ,     snake_case__  :    float = 0.9_99  ,     snake_case__  :    float = 1e-7  ,     snake_case__  :    bool = False  ,     snake_case__  :    float = 0.0  ,     snake_case__  :    Optional[List[str]] = None  ,     snake_case__  :    Optional[List[str]] = None  ,     snake_case__  :    str = "AdamWeightDecay"  ,     **snake_case__  :    List[Any]  ,     ):

              '''simple docstring'''






              super().__init__(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     **snake_case__				)
              lowercase		:Optional[Any]       							=				weight_decay_rate
              lowercase		:int       							=				include_in_weight_decay
              lowercase		:Any       							=				exclude_from_weight_decay






       @classmethod
       def  __snake_case						(   cls  :    str  ,     snake_case__  :    List[str]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				{'''WarmUp''': WarmUp}
              return super(snake_case__  ,     cls				).from_config(snake_case__  ,     custom_objects=snake_case__				)






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              super(snake_case__  ,     self				)._prepare_local(snake_case__  ,     snake_case__  ,     snake_case__				)
              lowercase		:List[str]       							=				tf.constant(
                  self.weight_decay_rate  ,     name='''adam_weight_decay_rate'''				)






       def  __snake_case						(   self  :    str  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    List[str]  ,     snake_case__  :    List[str]				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				self._do_use_weight_decay(var.name				)
              if do_decay:
                     return var.assign_sub(
                         learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']  ,     use_locking=self._use_locking  ,     )
              return tf.no_op()






       def  __snake_case						(   self  :    str  ,     snake_case__  :    int  ,     snake_case__  :    Optional[Any]=None  ,     **snake_case__  :    List[Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				list(zip(*snake_case__				)				)
              return super(snake_case__  ,     self				).apply_gradients(zip(snake_case__  ,     snake_case__				)  ,     name=snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    List[Any]				):

              '''simple docstring'''






              if apply_state is None:
                     return self._decayed_lr_t[var_dtype], {}

              lowercase		:int       							=				apply_state or {}
              lowercase		:Any       							=				apply_state.get((var_device, var_dtype)				)
              if coefficients is None:
                     lowercase		:List[str]       							=				self._fallback_apply_state(snake_case__  ,     snake_case__				)
                     lowercase		:int       							=				coefficients

              return coefficients["lr_t"], {"apply_state": apply_state}






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    List[Any]=None				):

              '''simple docstring'''






              lowercase							,  lowercase		:Tuple       							=				self._get_lr(var.device  ,     var.dtype.base_dtype  ,     snake_case__				)
              lowercase		:Dict       							=				self._decay_weights_op(snake_case__  ,     snake_case__  ,     snake_case__				)
              with tf.control_dependencies([decay]				):
                     return super(snake_case__  ,     self				)._resource_apply_dense(snake_case__  ,     snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Optional[int]=None				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[Any]       							=				self._get_lr(var.device  ,     var.dtype.base_dtype  ,     snake_case__				)
              lowercase		:Any       							=				self._decay_weights_op(snake_case__  ,     snake_case__  ,     snake_case__				)
              with tf.control_dependencies([decay]				):
                     return super(snake_case__  ,     self				)._resource_apply_sparse(snake_case__  ,     snake_case__  ,     snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				super().get_config()
              config.update({'''weight_decay_rate''': self.weight_decay_rate}				)
              return config







       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    Tuple				):

              '''simple docstring'''






              if self.weight_decay_rate == 0:
                     return False

              if self._include_in_weight_decay:
                     for r in self._include_in_weight_decay:
                            if re.search(snake_case__  ,     snake_case__				) is not None:
                                   return True

              if self._exclude_from_weight_decay:
                     for r in self._exclude_from_weight_decay:
                            if re.search(snake_case__  ,     snake_case__				) is not None:
                                   return False
              return True







class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:Dict       							=				[]
              lowercase		:Dict       							=				None






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              if self._accum_steps is None:
                     lowercase		:Optional[Any]       							=				tf.Variable(
                         tf.constant(0  ,     dtype=tf.intaa				)  ,     trainable=snake_case__  ,     synchronization=tf.VariableSynchronization.ON_READ  ,     aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA  ,     )

              return self._accum_steps.value()






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              if not self._gradients:
                     raise ValueError('''The accumulator should be called first to initialize the gradients'''				)
              return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






       def __call__(   self  :    Union[str, Any]  ,     snake_case__  :    Any				):

              '''simple docstring'''






              if not self._gradients:
                     lowercase		:Dict       							=				self.step  # Create the step variable.
                     self._gradients.extend(
                         [
                             tf.Variable(
                                 tf.zeros_like(snake_case__				)  ,     trainable=snake_case__  ,     synchronization=tf.VariableSynchronization.ON_READ  ,     aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA  ,     )
                             if gradient is not None
                             else gradient
                             for gradient in gradients
                         ]				)
              if len(snake_case__				) != len(self._gradients				):
                     raise ValueError(f"""Expected {len(self._gradients				)} gradients, but got {len(snake_case__				)}"""				)

              for accum_gradient, gradient in zip(self._gradients  ,     snake_case__				):
                     if accum_gradient is not None and gradient is not None:
                            accum_gradient.assign_add(snake_case__				)

              self._accum_steps.assign_add(1				)







       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              if not self._gradients:
                     return
              self._accum_steps.assign(0				)
              for gradient in self._gradients:
                     if gradient is not None:
                            gradient.assign(tf.zeros_like(snake_case__				)				)





"""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       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:str				,				a_						:PreTrainedTokenizer				,				a_						:int				,				a_						:Optional[int] = None				,				)     ->							int:
       lowercase		:Dict       							=				{}

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

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

       if len(a_) == 1:
              for k in files.keys():
                     lowercase		:Dict       							=				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():
                     lowercase		:Optional[Any]       							=				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]:
                     lowercase		:Any       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:List[str]       							=				labelaid[ex[label_name]]
                     yield (d, label)

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

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

       lowercase		:List[str]       							=				(
           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:
              lowercase		:Union[str, Any]       							=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

       lowercase		:Optional[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:
              lowercase		:List[Any]       							=				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

       lowercase		:Optional[Any]       							=				(
           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:
              lowercase		:int       							=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

       return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase      		=			logging.getLogger(__name__)
@dataclass
class 					__magic_name__		:
       __A			:      int        =   field(metadata={"help": "Which column contains the label"}     )
       __A			:      str        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the training file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the development file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the test file"}     )
       __A			:      int        =   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."
               )
           }			,						)
       __A			:      bool        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Overwrite the cached training and evaluation sets"}     )







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







def       lowerCamelCase					()     ->							Dict:
       # 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.
       lowercase		:Dict       							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
       lowercase							,  lowercase							,  lowercase		:Union[str, 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.

       lowercase		:Optional[int]       							=				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				,				)

       lowercase							,  lowercase							,  lowercase							,  lowercase		:int       							=				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				,				)

       lowercase		: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():
              lowercase		:int       							=				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:
              lowercase		:Tuple       							=				np.argmax(p.predictions				,				axis=1)

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

       # Initialize our Trainer
       lowercase		:Any       							=				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
       lowercase		:Union[str, Any]       							=				{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***''')
              lowercase		:str       							=				trainer.evaluate()
              lowercase		:int       							=				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()

