



"""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 			snake_case_    ( A_   :						str,							A_   :						str,							A_   :						str,							A_   :						PreTrainedTokenizer,							A_   :						int,							A_   :						Optional[int] = None,							):


       '''simple docstring'''





       _lowerCamelCase		:							Dict            =					{}

       if train_file is not None:
              _lowerCamelCase		:							Union[str, Any]            =					[train_file]
       if eval_file is not None:
              _lowerCamelCase		:							List[str]            =					[eval_file]
       if test_file is not None:
              _lowerCamelCase		:							Any            =					[test_file]

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

       if len(A_		) == 1:
              for k in files.keys():
                     _lowerCamelCase		:							int            =					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():
                     _lowerCamelCase		:							int            =					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]:
                     _lowerCamelCase		:							List[Any]            =					{k: v for k, v in ex.items() if k in input_names}
                     _lowerCamelCase		:							Tuple            =					labelaid[ex[label_name]]
                     yield (d, label)

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

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

       _lowerCamelCase		:							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:
              _lowerCamelCase		:							Optional[Any]            =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

       _lowerCamelCase		:							List[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.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              _lowerCamelCase		:							Union[str, Any]            =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

       _lowerCamelCase		:							List[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:
              _lowerCamelCase		:							Tuple            =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]		)		)		)

       return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase__              =							logging.getLogger(__name__)






@dataclass
class 			__snake_case			:
  snake_case__							:  int         =		field(metadata={"help": "Which column contains the label"})
  snake_case__							:  str         =		field(default=_lowercase    ,	metadata={"help": "The path of the training file"})
  snake_case__							:  Optional[str]         =		field(default=_lowercase    ,	metadata={"help": "The path of the development file"})
  snake_case__							:  Optional[str]         =		field(default=_lowercase    ,	metadata={"help": "The path of the test file"})
  snake_case__							:  int         =		field(
      default=1_2_8    ,	metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }    ,	)
  snake_case__							:  bool         =		field(
      default=_lowercase    ,	metadata={"help": "Overwrite the cached training and evaluation sets"})






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






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							Dict            =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							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.

       _lowerCamelCase		:							Optional[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,							)

       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Tuple            =					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,							)

       _lowerCamelCase		:							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():
              _lowerCamelCase		:							Union[str, Any]            =					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:
              _lowerCamelCase		:							Dict            =					np.argmax(p.predictions,							axis=1		)

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

       # Initialize our Trainer
       _lowerCamelCase		:							Tuple            =					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
       _lowerCamelCase		:							Tuple            =					{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''		)
              _lowerCamelCase		:							List[str]            =					trainer.evaluate()
              _lowerCamelCase		:							List[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'''


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCAmelCase_      :  int				     =  logging.get_logger(__name__)


lowerCAmelCase_      :  Optional[Any]				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_      :  List[str]				     =  {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_      :  Tuple				     =  {
    'allenai/led-base-16384': 1_6384,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       str   							=					VOCAB_FILES_NAMES
      __magic_name__  :       str   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       Optional[int]   							=					LEDTokenizer
      __magic_name__  :       Optional[int]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  str						,						lowercase__   :  int=None						,						lowercase__   :  Any=None						,						lowercase__   :  Optional[int]=None						,						lowercase__   :  List[Any]="replace"						,						lowercase__   :  Optional[Any]="<s>"						,						lowercase__   :  Optional[Any]="</s>"						,						lowercase__   :  Dict="</s>"						,						lowercase__   :  List[str]="<s>"						,						lowercase__   :  str="<unk>"						,						lowercase__   :  str="<pad>"						,						lowercase__   :  str="<mask>"						,						lowercase__   :  Union[str, Any]=False						,						lowercase__   :  Optional[int]=True						,						**lowercase__   :  str						,						):


           '''simple docstring'''

           super().__init__(
               lowercase__						,						lowercase__						,						tokenizer_file=lowercase__						,						errors=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						add_prefix_space=lowercase__						,						trim_offsets=lowercase__						,						**lowercase__						,						)

           a_     :							str     						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                a_     :							Optional[int]     						=  getattr(lowercase__						,						pre_tok_state.pop("""type"""       )       )
                a_     :							List[str]     						=  add_prefix_space
                a_     :							Union[str, Any]     						=  pre_tok_class(**lowercase__       )

           a_     :							Union[str, Any]     						=  add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           a_     :							Any     						=  """post_processor"""
           a_     :							int     						=  getattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )
           if tokenizer_component_instance:
                a_     :							Optional[int]     						=  json.loads(tokenizer_component_instance.__getstate__()       )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                     a_     :							int     						=  tuple(state["""sep"""]       )
                if "cls" in state:
                     a_     :							Optional[int]     						=  tuple(state["""cls"""]       )

                a_     :							List[Any]     						=  False

                if state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                     a_     :							Optional[Any]     						=  add_prefix_space
                     a_     :							Dict     						=  True

                if state.get("""trim_offsets"""						,						lowercase__       ) != trim_offsets:
                     a_     :							List[Any]     						=  trim_offsets
                     a_     :							Optional[Any]     						=  True

                if changes_to_apply:
                     a_     :							Optional[int]     						=  getattr(lowercase__						,						state.pop("""type"""       )       )
                     a_     :							Any     						=  component_class(**lowercase__       )
                     setattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )






      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           if self._mask_token is None:
                if self.verbose:
                     logger.error("""Using mask_token, but it is not set yet."""       )
                return None
           return str(self._mask_token       )






      @mask_token.setter
      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else value
           a_     :							Optional[int]     						=  value






      def 							lowercase_						( self   :  Optional[int]						,						*lowercase__   :  Any						,						**lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                    """to use it with pretokenized inputs."""       )

           return super()._batch_encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  List[Any]						,						*lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                    """to use it with pretokenized inputs."""       )

           return super()._encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self._tokenizer.model.save(lowercase__						,						name=lowercase__       )
           return tuple(lowercase__       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  int=None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  [self.sep_token_id]
           a_     :							int     						=  [self.cls_token_id]

           if token_ids_a is None:
                return len(cls + token_ids_a + sep       ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Union[Dict[str, EncodedInput], BatchEncoding]						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  Optional[bool] = None						,						):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  super()._pad(
               encoded_inputs=lowercase__						,						max_length=lowercase__						,						padding_strategy=lowercase__						,						pad_to_multiple_of=lowercase__						,						return_attention_mask=lowercase__						,						)

           # Load from model defaults
           if return_attention_mask is None:
                a_     :							Tuple     						=  """attention_mask""" in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                a_     :							Tuple     						=  encoded_inputs[self.model_input_names[0]]
                # `global_attention_mask` need to have the same length as other (sequential) inputs.
                a_     :							Optional[int]     						=  len(encoded_inputs["""global_attention_mask"""]       ) != len(lowercase__       )

                if needs_to_be_padded:
                     a_     :							int     						=  len(lowercase__       ) - len(encoded_inputs["""global_attention_mask"""]       )

                     if self.padding_side == "right":
                          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                          a_     :							Optional[int]     						=  (
                              encoded_inputs["""global_attention_mask"""] + [-1] * difference
                          )
                     elif self.padding_side == "left":
                          a_     :							Dict     						=  [-1] * difference + encoded_inputs[
                              """global_attention_mask"""
                          ]
                     else:
                          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

           return encoded_inputs
