





from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

_snake_case				= {
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

_snake_case				= datasets.load_dataset("""xnli""", split="""test+validation""")

_snake_case				= 0
_snake_case				= 0
_snake_case				= 0
_snake_case				= 0


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   if spm_diff == list(reversed(__magic_name__       )       ):
      # AAA -> AA+A vs A+AA case.
      return True
   elif len(__magic_name__       ) == len(__magic_name__       ) and fast.decode(__magic_name__       ) == fast.decode(__magic_name__       ):
      # Second order OK
      # Barrich -> Barr + ich vs Bar + rich
      return True
   lowercase__    =  slow.encode(slow.decode(__magic_name__       )       )
   lowercase__    =  fast.encode(fast.decode(__magic_name__       )       )
   if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
      # Type 3 error.
      # Snehagatha ->
      #       Sne, h, aga, th, a
      #       Sne, ha, gat, ha
      # Encoding the wrong with sp does not even recover what spm gave us
      # It fits tokenizer however...
      return True
   return False


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   lowercase__    =  fast.encode_plus(__magic_name__       )[0]
   lowercase__    =  enc.offsets
   lowercase__   ,							lowercase__    =  offsets[idx], offsets[idx - 1]
   if curr is not None and line[curr[0] : curr[1]] == "\u200f":
      return True
   if prev is not None and line[prev[0] : prev[1]] == "\u200f":
      return True


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   # Encoding can be the same with same result AAA -> A + AA vs AA + A
   # We can check that we use at least exactly the same number of tokens.
   for i, (spm_id, tok_id) in enumerate(zip(__magic_name__  ,					__magic_name__       )       ):
      if spm_id != tok_id:
         break
   lowercase__    =  i
   for i, (spm_id, tok_id) in enumerate(zip(reversed(__magic_name__       )  ,					reversed(__magic_name__       )       )       ):
      if spm_id != tok_id:
         break
   lowercase__    =  len(__magic_name__       ) - i

   lowercase__    =  spm_ids[first:last]
   lowercase__    =  tok_ids[first:last]

   if check_diff(__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
      return True

   if check_LTR_mark(__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
      return True

   if last - first > 5:
      # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
      lowercase__    =  Counter(spm_ids[first:last]       )
      lowercase__    =  Counter(tok_ids[first:last]       )

      lowercase__    =  {spm_ for (spm_, si) in spms.items() if toks.get(spm_  ,					0       ) == si}
      lowercase__    =  3
      for i in range(last - first - min_width       ):
         if all(spm_ids[first + i + j] in removable_tokens for j in range(__magic_name__       )       ):
            lowercase__    =  [
                k
                for k in range(last - first - min_width       )
                if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
            ]
            for j in possible_matches:
               if check_diff(spm_ids[first : first + i]  ,					tok_ids[first : first + j]  ,					__magic_name__  ,					__magic_name__       ) and check_details(
                   __magic_name__  ,					spm_ids[first + i : last]  ,					tok_ids[first + j : last]  ,					__magic_name__  ,					__magic_name__  ,					):
                  return True

   print(f'''Spm: {[fast.decode([spm_ids[i]]       ) for i in range(__magic_name__  ,					__magic_name__       )]}'''       )
   try:
      print(f'''Tok: {[fast.decode([tok_ids[i]]       ) for i in range(__magic_name__  ,					__magic_name__       )]}'''       )
   except Exception:
      pass

   lowercase__    =  fast.decode(spm_ids[:first]       )
   lowercase__    =  fast.decode(spm_ids[last:]       )
   lowercase__    =  fast.decode(spm_ids[first:last]       )
   print()
   print(__magic_name__       )
   return False


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   global perfect
   global imperfect
   global wrong
   global total

   lowercase__    =  slow.encode(__magic_name__       )
   lowercase__    =  fast.encode(__magic_name__       )

   lowercase__    =  False
   total += 1

   if slow_ids != fast_ids:
      if check_details(__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
         lowercase__    =  True
         imperfect += 1
      else:
         wrong += 1
   else:
      perfect += 1

   if total % 1_0000 == 0:
      print(f'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''       )

   if skip_assert:
      return

   assert (
       slow_ids == fast_ids
   ), f'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__magic_name__       )}\n{fast.tokenize(__magic_name__       )}'''


def _A   (    __magic_name__  ,					__magic_name__       ):
   global batch_total
   for i in range(len(__magic_name__       )       ):
      # premise, all languages
      for text in dataset[i]["premise"].values():
         test_string(__magic_name__  ,					__magic_name__  ,					__magic_name__       )

      # hypothesis, all languages
      for text in dataset[i]["hypothesis"]["translation"]:
         test_string(__magic_name__  ,					__magic_name__  ,					__magic_name__       )


if __name__ == "__main__":
  for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
    _snake_case				= list(slow_class.max_model_input_sizes.keys())
    for checkpoint in checkpoint_names:
      _snake_case				= 0
      _snake_case				= 0
      _snake_case				= 0
      _snake_case				= 0

      print(F"""========================== Checking {name}: {checkpoint} ==========================""")
      _snake_case				= slow_class.from_pretrained(checkpoint, force_download=True)
      _snake_case				= fast_class.from_pretrained(checkpoint, force_download=True)
      test_tokenizer(slow, fast)
      print(F"""Accuracy {perfect * 100 / total:.2f}""")













import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_snake_case				= logging.getLogger(__name__)





@dataclass
class 					lowerCAmelCase							:
  __lowerCamelCase		=			field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
  __lowerCamelCase		=			field(
      default=lowercase_							,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
  __lowerCamelCase		=			field(
      default=lowercase_							,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
  __lowerCamelCase		=			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.
  __lowerCamelCase		=			field(
      default=lowercase_							,							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.

   lowercase__    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

   if len(sys.argv       ) == 2 and sys.argv[1].endswith(".json"       ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowercase__   ,							lowercase__   ,							lowercase__    =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
   else:
      lowercase__   ,							lowercase__   ,							lowercase__    =  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 if training_args.local_rank in [-1, 0] else logging.WARN  ,					)
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"  ,					training_args.local_rank  ,					training_args.device  ,					training_args.n_gpu  ,					bool(training_args.local_rank != -1       )  ,					training_args.fpaa  ,					)
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank       ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info("Training/evaluation parameters %s"  ,					__magic_name__       )

   # Prepare Question-Answering task
   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   lowercase__    =  AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,					cache_dir=model_args.cache_dir  ,					)
   lowercase__    =  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  ,					use_fast=__magic_name__  ,					)
   lowercase__    =  AutoModelForQuestionAnswering.from_pretrained(
       model_args.model_name_or_path  ,					from_tf=bool(".ckpt" in model_args.model_name_or_path       )  ,					config=__magic_name__  ,					cache_dir=model_args.cache_dir  ,					)

   # Get datasets
   lowercase__    =  hasattr(model.config  ,					"lang2id"       )
   lowercase__    =  (
       SquadDataset(
           __magic_name__  ,					tokenizer=__magic_name__  ,					is_language_sensitive=__magic_name__  ,					cache_dir=model_args.cache_dir       )
       if training_args.do_train
       else None
   )
   lowercase__    =  (
       SquadDataset(
           __magic_name__  ,					tokenizer=__magic_name__  ,					mode="dev"  ,					is_language_sensitive=__magic_name__  ,					cache_dir=model_args.cache_dir  ,					)
       if training_args.do_eval
       else None
   )

   # Data collator
   lowercase__    =  DataCollatorWithPadding(__magic_name__  ,					pad_to_multiple_of=8       ) if training_args.fpaa else None

   # Initialize our Trainer
   lowercase__    =  Trainer(
       model=__magic_name__  ,					args=__magic_name__  ,					train_dataset=__magic_name__  ,					eval_dataset=__magic_name__  ,					data_collator=__magic_name__  ,					)

   # Training
   if training_args.do_train:
      trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
      trainer.save_model()
      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      if trainer.is_world_master():
         tokenizer.save_pretrained(training_args.output_dir       )


def _A   (    __magic_name__       ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






