

'''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    A__ ( tf.keras.optimizers.schedules.LearningRateSchedule     ):



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = 1.0						,			UpperCamelCase__ = None						,			)  -> Optional[int]:





           '''simple docstring'''
           super().__init__()
           A_		   = initial_learning_rate
           A_		   = warmup_steps
           A_		   = power
           A_		   = decay_schedule_fn
           A_		   = name



    def __call__(   self						,			UpperCamelCase__    )  -> Tuple:





           '''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`.
                  A_		   = tf.cast(UpperCamelCase__						,			tf.floataa    )
                  A_		   = tf.cast(self.warmup_steps						,			tf.floataa    )
                  A_		   = global_step_float / warmup_steps_float
                  A_		   = self.initial_learning_rate * tf.math.pow(UpperCamelCase__						,			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=UpperCamelCase__						,			)







    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__ = 0.0,    UpperCAmelCase__ = 0.9,    UpperCAmelCase__ = 0.999,    UpperCAmelCase__ = 1e-8,    UpperCAmelCase__ = None,    UpperCAmelCase__ = None,    UpperCAmelCase__ = 0.0,    UpperCAmelCase__ = 1.0,    UpperCAmelCase__ = None,    )     ->     Any:
       A_		   = tf.keras.optimizers.schedules.PolynomialDecay(
           initial_learning_rate=UpperCAmelCase__,    decay_steps=num_train_steps - num_warmup_steps,    end_learning_rate=init_lr * min_lr_ratio,    power=UpperCAmelCase__,    )
       if num_warmup_steps:
              A_		   = WarmUp(
                  initial_learning_rate=UpperCAmelCase__,    decay_schedule_fn=UpperCAmelCase__,    warmup_steps=UpperCAmelCase__,    )
       if weight_decay_rate > 0.0:
              A_		   = AdamWeightDecay(
                  learning_rate=UpperCAmelCase__,    weight_decay_rate=UpperCAmelCase__,    beta_a=UpperCAmelCase__,    beta_a=UpperCAmelCase__,    epsilon=UpperCAmelCase__,    clipnorm=UpperCAmelCase__,    global_clipnorm=UpperCAmelCase__,    exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""],    include_in_weight_decay=UpperCAmelCase__,    )
       else:
              A_		   = tf.keras.optimizers.Adam(
                  learning_rate=UpperCAmelCase__,    beta_a=UpperCAmelCase__,    beta_a=UpperCAmelCase__,    epsilon=UpperCAmelCase__,    clipnorm=UpperCAmelCase__,    global_clipnorm=UpperCAmelCase__,    )
       # 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    A__ ( _snake_case     ):



    def __init__(   self						,			UpperCamelCase__ = 0.001						,			UpperCamelCase__ = 0.9						,			UpperCamelCase__ = 0.999						,			UpperCamelCase__ = 1e-7						,			UpperCamelCase__ = False						,			UpperCamelCase__ = 0.0						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = "AdamWeightDecay"						,			**UpperCamelCase__						,			)  -> Optional[Any]:





           '''simple docstring'''
           super().__init__(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			**UpperCamelCase__    )
           A_		   = weight_decay_rate
           A_		   = include_in_weight_decay
           A_		   = exclude_from_weight_decay



    @classmethod
    def    snake_case_     (   cls						,			UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = {"""WarmUp""": WarmUp}
           return super(UpperCamelCase__						,			cls    ).from_config(UpperCamelCase__						,			custom_objects=UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           super(UpperCamelCase__						,			self    )._prepare_local(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )
           A_		   = tf.constant(
               self.weight_decay_rate						,			name="""adam_weight_decay_rate"""    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = 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						,			UpperCamelCase__						,			UpperCamelCase__=None						,			**UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           A_   ,     A_		   = list(zip(*UpperCamelCase__    )    )
           return super(UpperCamelCase__						,			self    ).apply_gradients(zip(UpperCamelCase__						,			UpperCamelCase__    )						,			name=UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> int:





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

           A_		   = apply_state or {}
           A_		   = apply_state.get((var_device, var_dtype)    )
           if coefficients is None:
                  A_		   = self._fallback_apply_state(UpperCamelCase__						,			UpperCamelCase__    )
                  A_		   = coefficients

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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=None    )  -> List[Any]:





           '''simple docstring'''
           A_   ,     A_		   = self._get_lr(var.device						,			var.dtype.base_dtype						,			UpperCamelCase__    )
           A_		   = self._decay_weights_op(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )
           with tf.control_dependencies([decay]    ):
                  return super(UpperCamelCase__						,			self    )._resource_apply_dense(UpperCamelCase__						,			UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=None    )  -> Any:





           '''simple docstring'''
           A_   ,     A_		   = self._get_lr(var.device						,			var.dtype.base_dtype						,			UpperCamelCase__    )
           A_		   = self._decay_weights_op(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )
           with tf.control_dependencies([decay]    ):
                  return super(UpperCamelCase__						,			self    )._resource_apply_sparse(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = super().get_config()
           config.update({"""weight_decay_rate""": self.weight_decay_rate}    )
           return config







    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Any:





           '''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(UpperCamelCase__						,			UpperCamelCase__    ) is not None:
                                return True

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


class    A__ ( _snake_case     ):



    def __init__(   self    )  -> int:





           '''simple docstring'''
           A_		   = []
           A_		   = None



    @property
    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           if self._accum_steps is None:
                  A_		   = tf.Variable(
                      tf.constant(0						,			dtype=tf.intaa    )						,			trainable=UpperCamelCase__						,			synchronization=tf.VariableSynchronization.ON_READ						,			aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA						,			)

           return self._accum_steps.value()



    @property
    def    snake_case_     (   self    )  -> Any:





           '''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						,			UpperCamelCase__    )  -> Dict:





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

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

           self._accum_steps.assign_add(1    )







    def    snake_case_     (   self    )  -> List[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(UpperCamelCase__    )    )



'''simple docstring'''







import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCamelCase						    =      logging.get_logger(__name__)


@dataclass
class    A__ :
    lowercase		   =						field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()     )}     )
    lowercase		   =						field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}     )
    lowercase		   =						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."
            )
        }  ,  )
    lowercase		   =						field(
        default=_snake_case  ,  metadata={"help": "Overwrite the cached training and evaluation sets"}     )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.task_name.lower()


class    A__ ( _snake_case     ):
    lowercase		   =						"train"
    lowercase		   =						"dev"
    lowercase		   =						"test"


class    A__ ( _snake_case     ):
    lowercase		   =						42
    lowercase		   =						42
    lowercase		   =						42



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = Split.train						,			UpperCamelCase__ = None						,			)  -> List[Any]:





           '''simple docstring'''
           warnings.warn(
               """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
               """library. You can have a look at this example script for pointers: """
               """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""						,			UpperCamelCase__						,			)
           A_		   = args
           A_		   = glue_processors[args.task_name]()
           A_		   = glue_output_modes[args.task_name]
           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  try:
                         A_		   = Split[mode]
                  except KeyError:
                         raise KeyError("""mode is not a valid split name"""    )
        # Load data features from cache or dataset file
           A_		   = os.path.join(
               cache_dir if cache_dir is not None else args.data_dir						,			f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''						,			)
           A_		   = self.processor.get_labels()
           if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
               "RobertaTokenizer",
               "RobertaTokenizerFast",
               "XLMRobertaTokenizer",
               "BartTokenizer",
               "BartTokenizerFast",
           ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  A_   ,     A_		   = label_list[2], label_list[1]
           A_		   = label_list

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           A_		   = cached_features_file + """.lock"""
           with FileLock(UpperCamelCase__    ):
                  if os.path.exists(UpperCamelCase__    ) and not args.overwrite_cache:
                         A_		   = time.time()
                         A_		   = torch.load(UpperCamelCase__    )
                         logger.info(
                             f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						,			time.time() - start    )
                  else:
                         logger.info(f'''Creating features from dataset file at {args.data_dir}'''    )

                         if mode == Split.dev:
                                A_		   = self.processor.get_dev_examples(args.data_dir    )
                         elif mode == Split.test:
                                A_		   = self.processor.get_test_examples(args.data_dir    )
                         else:
                                A_		   = self.processor.get_train_examples(args.data_dir    )
                         if limit_length is not None:
                                A_		   = examples[:limit_length]
                         A_		   = glue_convert_examples_to_features(
                             UpperCamelCase__						,			UpperCamelCase__						,			max_length=args.max_seq_length						,			label_list=UpperCamelCase__						,			output_mode=self.output_mode						,			)
                         A_		   = time.time()
                         torch.save(self.features						,			UpperCamelCase__    )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''    )



    def __len__(   self    )  -> str:





           '''simple docstring'''
           return len(self.features    )



    def __getitem__(   self						,			UpperCamelCase__    )  -> InputFeatures:





           '''simple docstring'''
           return self.features[i]







    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           return self.label_list
