
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_A						      =				logging.get_logger(__name__)


_A						      =				{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
_A						      =				{
    "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",
    },
}

_A						      =				{
    "allenai/led-base-16384": 1_63_84,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      (
        list(range(ord("!"	)				,	ord("~"	) + 1	)	) + list(range(ord("¡"	)				,	ord("¬"	) + 1	)	) + list(range(ord("®"	)				,	ord("ÿ"	) + 1	)	)
    )
    snake_case				  =      bs[:]
    snake_case				  =      0
    for b in range(2**8	):
        if b not in bs:
            bs.append(A__	)
            cs.append(2**8 + n	)
            n += 1
    snake_case				  =      [chr(A__	) for n in cs]
    return dict(zip(A__				,	A__	)	)



def 		lowercase_    (     A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      set()
    snake_case				  =      word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        snake_case				  =      char
    return pairs







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							Optional[int]     =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							List[Any]     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							List[str]     =	["input_ids", "attention_mask"]





     def __init__(self   :					List[Any]       ,					_A   :					Any       ,					_A   :					int       ,					_A   :					Tuple="replace"       ,					_A   :					str="<s>"       ,					_A   :					Dict="</s>"       ,					_A   :					Dict="</s>"       ,					_A   :					List[str]="<s>"       ,					_A   :					Union[str, Any]="<unk>"       ,					_A   :					Optional[int]="<pad>"       ,					_A   :					Dict="<mask>"       ,					_A   :					Any=False       ,					**_A   :					Optional[Any]       ,					)		->		Tuple:
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else bos_token
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else eos_token
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else sep_token
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else cls_token
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else unk_token
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else mask_token

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

         with open(_A       ,					encoding="utf-8"     ) as vocab_handle:
             snake_case				  =      json.load(_A     )
         snake_case				  =      {v: k for k, v in self.encoder.items()}
         snake_case				  =      errors  # how to handle errors in decoding
         snake_case				  =      bytes_to_unicode()
         snake_case				  =      {v: k for k, v in self.byte_encoder.items()}
         with open(_A       ,					encoding="utf-8"     ) as merges_handle:
             snake_case				  =      merges_handle.read().split("\n"     )[1:-1]
         snake_case				  =      [tuple(merge.split()     ) for merge in bpe_merges]
         snake_case				  =      dict(zip(_A       ,					range(len(_A     )     )     )     )
         snake_case				  =      {}
         snake_case				  =      add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         snake_case				  =      re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"     )





     @property
     # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
     def 	UpperCAmelCase(self   :					int     )		->		Tuple:
         return len(self.encoder     )





     def 	UpperCAmelCase(self   :					List[str]     )		->		Any:
         return dict(self.encoder       ,					**self.added_tokens_encoder     )





     def 	UpperCAmelCase(self   :					int       ,					_A   :					int     )		->		List[str]:
         if token in self.cache:
             return self.cache[token]
         snake_case				  =      tuple(_A     )
         snake_case				  =      get_pairs(_A     )

         if not pairs:
             return token

         while True:
             snake_case				  =      min(_A       ,					key=lambda _A     : self.bpe_ranks.get(_A       ,					float("inf"     )     )     )
             if bigram not in self.bpe_ranks:
                 break
             snake_case  ,							snake_case				  =      bigram
             snake_case				  =      []
             snake_case				  =      0
             while i < len(_A     ):
                 try:
                     snake_case				  =      word.index(_A       ,					_A     )
                 except ValueError:
                     new_word.extend(word[i:]     )
                     break
                 else:
                     new_word.extend(word[i:j]     )
                     snake_case				  =      j

                 if word[i] == first and i < len(_A     ) - 1 and word[i + 1] == second:
                     new_word.append(first + second     )
                     i += 2
                 else:
                     new_word.append(word[i]     )
                     i += 1
             snake_case				  =      tuple(_A     )
             snake_case				  =      new_word
             if len(_A     ) == 1:
                 break
             else:
                 snake_case				  =      get_pairs(_A     )
         snake_case				  =      " ".join(_A     )
         snake_case				  =      word
         return word





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					str     )		->		Union[str, Any]:
         snake_case				  =      []
         for token in re.findall(self.pat       ,					_A     ):
             snake_case				  =      "".join(
                 self.byte_encoder[b] for b in token.encode("utf-8"     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A     ).split(" "     )     )
         return bpe_tokens





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					Tuple     )		->		Union[str, Any]:
         return self.encoder.get(_A       ,					self.encoder.get(self.unk_token     )     )





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					Dict     )		->		Union[str, Any]:
         return self.decoder.get(_A     )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Any     )		->		int:
         snake_case				  =      "".join(_A     )
         snake_case				  =      bytearray([self.byte_decoder[c] for c in text]     ).decode("utf-8"       ,					errors=self.errors     )
         return text





     def 	UpperCAmelCase(self   :					Dict       ,					_A   :					str       ,					_A   :					Optional[str] = None     )		->		Tuple[str]:
         if not os.path.isdir(_A     ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
             return
         snake_case				  =      os.path.join(
             _A       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
         snake_case				  =      os.path.join(
             _A       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

         with open(_A       ,					"w"       ,					encoding="utf-8"     ) as f:
             f.write(json.dumps(self.encoder       ,					indent=2       ,					sort_keys=_A       ,					ensure_ascii=_A     ) + "\n"     )

         snake_case				  =      0
         with open(_A       ,					"w"       ,					encoding="utf-8"     ) as writer:
             writer.write("#version: 0.2\n"     )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,					key=lambda _A     : kv[1]     ):
                 if index != token_index:
                     logger.warning(
                         f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                         " Please check that the tokenizer is not corrupted!"     )
                     snake_case				  =      token_index
                 writer.write(" ".join(_A     ) + "\n"     )
                 index += 1

         return vocab_file, merge_file





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None     )		->		List[int]:
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         snake_case				  =      [self.cls_token_id]
         snake_case				  =      [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





     def 	UpperCAmelCase(self   :					Dict       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None       ,					_A   :					bool = False     )		->		List[int]:
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_A       ,					token_ids_a=_A       ,					already_has_special_tokens=_A     )

         if token_ids_a is None:
             return [1] + ([0] * len(_A     )) + [1]
         return [1] + ([0] * len(_A     )) + [1, 1] + ([0] * len(_A     )) + [1]





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None     )		->		List[int]:
         snake_case				  =      [self.sep_token_id]
         snake_case				  =      [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 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					int       ,					_A   :					List[Any]=False       ,					**_A   :					Union[str, Any]     )		->		Any:
         snake_case				  =      kwargs.pop("add_prefix_space"       ,					self.add_prefix_space     )
         if (is_split_into_words or add_prefix_space) and (len(_A     ) > 0 and not text[0].isspace()):
             snake_case				  =      " " + text
         return (text, kwargs)





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Union[Dict[str, EncodedInput], BatchEncoding]       ,					_A   :					Optional[int] = None       ,					_A   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[bool] = None       ,					)		->		dict:
         snake_case				  =      super()._pad(
             encoded_inputs=_A       ,					max_length=_A       ,					padding_strategy=_A       ,					pad_to_multiple_of=_A       ,					return_attention_mask=_A       ,					)

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

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

             if needs_to_be_padded:
                 snake_case				  =      len(_A     ) - 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`
                     snake_case				  =      (
                         encoded_inputs["global_attention_mask"] + [-1] * difference
                     )
                 elif self.padding_side == "left":
                     snake_case				  =      [-1] * difference + encoded_inputs[
                         "global_attention_mask"
                     ]
                 else:
                     raise ValueError("Invalid padding strategy:" + str(self.padding_side     )     )

         return encoded_inputs



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 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__				,	A__ = None				,	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      {}

    if train_file is not None:
        snake_case				  =      [train_file]
    if eval_file is not None:
        snake_case				  =      [eval_file]
    if test_file is not None:
        snake_case				  =      [test_file]

    snake_case				  =      datasets.load_dataset("csv"				,	data_files=A__	)
    snake_case				  =      list(ds[list(files.keys()	)[0]].features.keys()	)
    snake_case				  =      features_name.pop(A__	)
    snake_case				  =      list(set(ds[list(files.keys()	)[0]][label_name]	)	)
    snake_case				  =      {label: i for i, label in enumerate(A__	)}
    snake_case				  =      tokenizer.model_input_names
    snake_case				  =      {}

    if len(A__	) == 1:
        for k in files.keys():
            snake_case				  =      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():
            snake_case				  =      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]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

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

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

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

    snake_case				  =      (
        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:
        snake_case				  =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    snake_case				  =      (
        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:
        snake_case				  =      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   lowerCamelCase	:
     UpperCAmelCase__       :							int     =	field(metadata={"help": "Which column contains the label"}						)
     UpperCAmelCase__       :							str     =	field(default=A_							,				metadata={"help": "The path of the training file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the development file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the test file"}						)
     UpperCAmelCase__       :							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."
             )
         }							,				)
     UpperCAmelCase__       :							bool     =	field(
         default=A_							,				metadata={"help": "Overwrite the cached training and evaluation sets"}						)







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



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    snake_case  ,							snake_case  ,							snake_case				  =      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.

    snake_case				  =      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				,	)

    snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      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				,	)

    snake_case				  =      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():
        snake_case				  =      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__	) -> Dict:
        snake_case				  =      np.argmax(p.predictions				,	axis=1	)

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

    # Initialize our Trainer
    snake_case				  =      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
    snake_case				  =      {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"	)
        snake_case				  =      trainer.evaluate()
        snake_case				  =      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()

