




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase_  						=       logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowerCamelCase_  						=       {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase_  						=       {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase_  						=       sorted(arg_to_scheduler.keys())
lowerCamelCase_  						=       "{" + ", ".join(arg_to_scheduler_choices) + "}"
class 				__a (			pl.LightningModule					):


					"""simple docstring"""


					def __init__(     self      :			Union[str, Any]    ,_UpperCamelCase      :			argparse.Namespace    ,_UpperCamelCase      :			List[str]=None    ,_UpperCamelCase      :			Union[str, Any]="base"    ,_UpperCamelCase      :			Optional[int]=None    ,_UpperCamelCase      :			List[str]=None    ,_UpperCamelCase      :			List[str]=None    ,**_UpperCamelCase      :			List[Any]    ,)		->       Union[str, Any]:


									'''simple docstring'''





									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(_UpperCamelCase						)
									SCREAMING_SNAKE_CASE__       =0
									SCREAMING_SNAKE_CASE__       =Path(self.hparams.output_dir						)
									SCREAMING_SNAKE_CASE__       =self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
													SCREAMING_SNAKE_CASE__       =AutoConfig.from_pretrained(
													    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,**({"""num_labels""": num_labels} if num_labels is not None else {})    ,cache_dir=_UpperCamelCase    ,**_UpperCamelCase    ,)
									else:
													SCREAMING_SNAKE_CASE__       =config

									SCREAMING_SNAKE_CASE__       =('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
									for p in extra_model_params:
													if getattr(self.hparams    ,_UpperCamelCase    ,_UpperCamelCase						):
																	assert hasattr(self.config    ,_UpperCamelCase						), f"""model config doesn\'t have a `{p}` attribute"""
																	setattr(self.config    ,_UpperCamelCase    ,getattr(self.hparams    ,_UpperCamelCase						)						)

									if tokenizer is None:
													SCREAMING_SNAKE_CASE__       =AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,cache_dir=_UpperCamelCase    ,)
									else:
													SCREAMING_SNAKE_CASE__       =tokenizer
									SCREAMING_SNAKE_CASE__       =MODEL_MODES[mode]
									if model is None:
													SCREAMING_SNAKE_CASE__       =self.model_type.from_pretrained(
													    self.hparams.model_name_or_path    ,from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path						)    ,config=self.config    ,cache_dir=_UpperCamelCase    ,)
									else:
													SCREAMING_SNAKE_CASE__       =model




					def   __A    (     self      :			str    ,*_UpperCamelCase      :			Any    ,**_UpperCamelCase      :			Optional[int]						)		->       Dict:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.model_type.from_pretrained(*_UpperCamelCase    ,**_UpperCamelCase						)




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


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =arg_to_scheduler[self.hparams.lr_scheduler]
									SCREAMING_SNAKE_CASE__       =get_schedule_func(
									    self.opt    ,num_warmup_steps=self.hparams.warmup_steps    ,num_training_steps=self.total_steps()						)
									SCREAMING_SNAKE_CASE__       ={'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
									return scheduler




					def   __A    (     self      :			Any						)		->       Optional[Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.model
									SCREAMING_SNAKE_CASE__       =['bias', 'LayerNorm.weight']
									SCREAMING_SNAKE_CASE__       =[
									    {
									        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
									        'weight_decay': self.hparams.weight_decay,
									    },
									    {
									        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
									        'weight_decay': 0.0,
									    },
									]
									if self.hparams.adafactor:
													SCREAMING_SNAKE_CASE__       =Adafactor(
													    _UpperCamelCase    ,lr=self.hparams.learning_rate    ,scale_parameter=_UpperCamelCase    ,relative_step=_UpperCamelCase						)

									else:
													SCREAMING_SNAKE_CASE__       =AdamW(
													    _UpperCamelCase    ,lr=self.hparams.learning_rate    ,eps=self.hparams.adam_epsilon						)
									SCREAMING_SNAKE_CASE__       =optimizer

									SCREAMING_SNAKE_CASE__       =self.get_lr_scheduler()

									return [optimizer], [scheduler]




					def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			int						)		->       List[Any]:


									'''simple docstring'''





									return self.validation_step(_UpperCamelCase    ,_UpperCamelCase						)




					def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any						)		->       Optional[Any]:


									'''simple docstring'''





									return self.validation_end(_UpperCamelCase						)




					def   __A    (     self      :			Optional[int]						)		->       int:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =max(1    ,self.hparams.gpus						)  # TODO: consider num_tpu_cores
									SCREAMING_SNAKE_CASE__       =self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




					def   __A    (     self      :			List[Any]    ,_UpperCamelCase      :			Optional[Any]						)		->       str:


									'''simple docstring'''





									if stage == "test":
													SCREAMING_SNAKE_CASE__       =len(self.test_dataloader().dataset						)
									else:
													SCREAMING_SNAKE_CASE__       =self.get_dataloader("""train"""    ,self.hparams.train_batch_size    ,shuffle=_UpperCamelCase						)
													SCREAMING_SNAKE_CASE__       =len(self.train_dataloader().dataset						)




					def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			bool = False						)		->       Optional[Any]:


									'''simple docstring'''





									raise NotImplementedError("""You must implement this for your task"""						)




					def   __A    (     self      :			List[str]						)		->       List[Any]:


									'''simple docstring'''





									return self.train_loader




					def   __A    (     self      :			Tuple						)		->       Optional[Any]:


									'''simple docstring'''





									return self.get_dataloader("""dev"""    ,self.hparams.eval_batch_size    ,shuffle=_UpperCamelCase						)




					def   __A    (     self      :			Any						)		->       Optional[Any]:


									'''simple docstring'''





									return self.get_dataloader("""test"""    ,self.hparams.eval_batch_size    ,shuffle=_UpperCamelCase						)




					def   __A    (     self      :			str    ,_UpperCamelCase      :			Tuple						)		->       List[Any]:


									'''simple docstring'''





									return os.path.join(
									    self.hparams.data_dir    ,"""cached_{}_{}_{}""".format(
									        _UpperCamelCase    ,list(filter(_UpperCamelCase    ,self.hparams.model_name_or_path.split("""/"""						)						)						).pop()    ,str(self.hparams.max_seq_length						)    ,)    ,)




					@pl.utilities.rank_zero_only
					def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			Dict[str, Any]						)		->       None:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.output_dir.joinpath("""best_tfmr"""						)
									SCREAMING_SNAKE_CASE__       =self.step_count
									self.model.save_pretrained(_UpperCamelCase						)
									self.tokenizer.save_pretrained(_UpperCamelCase						)




					@staticmethod
					def   __A    (     _UpperCamelCase      :			str    ,_UpperCamelCase      :			int						)		->       Optional[Any]:


									'''simple docstring'''





									parser.add_argument(
									    """--model_name_or_path"""    ,default=_UpperCamelCase    ,type=_UpperCamelCase    ,required=_UpperCamelCase    ,help="""Path to pretrained model or model identifier from huggingface.co/models"""    ,)
									parser.add_argument(
									    """--config_name"""    ,default=""""""    ,type=_UpperCamelCase    ,help="""Pretrained config name or path if not the same as model_name"""						)
									parser.add_argument(
									    """--tokenizer_name"""    ,default=_UpperCamelCase    ,type=_UpperCamelCase    ,help="""Pretrained tokenizer name or path if not the same as model_name"""    ,)
									parser.add_argument(
									    """--cache_dir"""    ,default=""""""    ,type=_UpperCamelCase    ,help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""    ,)
									parser.add_argument(
									    """--encoder_layerdrop"""    ,type=_UpperCamelCase    ,help="""Encoder layer dropout probability (Optional). Goes into model.config"""    ,)
									parser.add_argument(
									    """--decoder_layerdrop"""    ,type=_UpperCamelCase    ,help="""Decoder layer dropout probability (Optional). Goes into model.config"""    ,)
									parser.add_argument(
									    """--dropout"""    ,type=_UpperCamelCase    ,help="""Dropout probability (Optional). Goes into model.config"""    ,)
									parser.add_argument(
									    """--attention_dropout"""    ,type=_UpperCamelCase    ,help="""Attention dropout probability (Optional). Goes into model.config"""    ,)
									parser.add_argument("""--learning_rate"""    ,default=5e-5    ,type=_UpperCamelCase    ,help="""The initial learning rate for Adam."""						)
									parser.add_argument(
									    """--lr_scheduler"""    ,default="""linear"""    ,choices=_UpperCamelCase    ,metavar=_UpperCamelCase    ,type=_UpperCamelCase    ,help="""Learning rate scheduler"""    ,)
									parser.add_argument("""--weight_decay"""    ,default=0.0    ,type=_UpperCamelCase    ,help="""Weight decay if we apply some."""						)
									parser.add_argument("""--adam_epsilon"""    ,default=1e-8    ,type=_UpperCamelCase    ,help="""Epsilon for Adam optimizer."""						)
									parser.add_argument("""--warmup_steps"""    ,default=0    ,type=_UpperCamelCase    ,help="""Linear warmup over warmup_steps."""						)
									parser.add_argument("""--num_workers"""    ,default=4    ,type=_UpperCamelCase    ,help="""kwarg passed to DataLoader"""						)
									parser.add_argument("""--num_train_epochs"""    ,dest="""max_epochs"""    ,default=3    ,type=_UpperCamelCase						)
									parser.add_argument("""--train_batch_size"""    ,default=3_2    ,type=_UpperCamelCase						)
									parser.add_argument("""--eval_batch_size"""    ,default=3_2    ,type=_UpperCamelCase						)
									parser.add_argument("""--adafactor"""    ,action="""store_true"""						)






class 				__a (			pl.Callback					):


					"""simple docstring"""


					def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			Union[str, Any]						)		->       List[str]:


									'''simple docstring'''





									if (
									    trainer.is_global_zero and trainer.global_rank == 0
									):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
													pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 				__a (			pl.Callback					):


					"""simple docstring"""


					def   __A    (     self      :			str    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			List[Any]						)		->       int:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =trainer.lr_schedulers[0]['scheduler']
									SCREAMING_SNAKE_CASE__       ={f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
									pl_module.logger.log_metrics(_UpperCamelCase						)




					def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule						)		->       Union[str, Any]:


									'''simple docstring'''





									rank_zero_info("""***** Validation results *****"""						)
									SCREAMING_SNAKE_CASE__       =trainer.callback_metrics
									# Log results
									for key in sorted(_UpperCamelCase						):
													if key not in ["log", "progress_bar"]:
																	rank_zero_info("""{} = {}\n""".format(_UpperCamelCase    ,str(metrics[key]						)						)						)




					def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule						)		->       Any:


									'''simple docstring'''





									rank_zero_info("""***** Test results *****"""						)
									SCREAMING_SNAKE_CASE__       =trainer.callback_metrics
									# Log and save results to file
									SCREAMING_SNAKE_CASE__       =os.path.join(pl_module.hparams.output_dir    ,"""test_results.txt"""						)
									with open(_UpperCamelCase    ,"""w"""						) as writer:
													for key in sorted(_UpperCamelCase						):
																	if key not in ["log", "progress_bar"]:
																					rank_zero_info("""{} = {}\n""".format(_UpperCamelCase    ,str(metrics[key]						)						)						)
																					writer.write("""{} = {}\n""".format(_UpperCamelCase    ,str(metrics[key]						)						)						)






def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
				parser.add_argument(
				    """--output_dir""",    default=__UpperCamelCase,    type=__UpperCamelCase,    required=__UpperCamelCase,    help="""The output directory where the model predictions and checkpoints will be written.""",    )
				parser.add_argument(
				    """--fp16""",    action="""store_true""",    help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""",    )

				parser.add_argument(
				    """--fp16_opt_level""",    type=__UpperCamelCase,    default="""O2""",    help=(
				        """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
				        """See details at https://nvidia.github.io/apex/amp.html"""
				    ),    )
				parser.add_argument("""--n_tpu_cores""",    dest="""tpu_cores""",    type=__UpperCamelCase     )
				parser.add_argument("""--max_grad_norm""",    dest="""gradient_clip_val""",    default=1.0,    type=__UpperCamelCase,    help="""Max gradient norm"""     )
				parser.add_argument("""--do_train""",    action="""store_true""",    help="""Whether to run training."""     )
				parser.add_argument("""--do_predict""",    action="""store_true""",    help="""Whether to run predictions on the test set."""     )
				parser.add_argument(
				    """--gradient_accumulation_steps""",    dest="""accumulate_grad_batches""",    type=__UpperCamelCase,    default=1,    help="""Number of updates steps to accumulate before performing a backward/update pass.""",    )
				parser.add_argument("""--seed""",    type=__UpperCamelCase,    default=42,    help="""random seed for initialization"""     )
				parser.add_argument(
				    """--data_dir""",    default=__UpperCamelCase,    type=__UpperCamelCase,    required=__UpperCamelCase,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""",    )




def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=None,    __UpperCamelCase=True,    __UpperCamelCase=None,    __UpperCamelCase=[],    __UpperCamelCase=None,    __UpperCamelCase=None,    **__UpperCamelCase,    ):
				pl.seed_everything(args.seed     )

				# init model
				SCREAMING_SNAKE_CASE__       =Path(model.hparams.output_dir     )
				odir.mkdir(exist_ok=__UpperCamelCase     )

				# add custom checkpoints
				if checkpoint_callback is None:
								SCREAMING_SNAKE_CASE__       =pl.callbacks.ModelCheckpoint(
								    filepath=args.output_dir,    prefix="""checkpoint""",    monitor="""val_loss""",    mode="""min""",    save_top_k=1     )
				if early_stopping_callback:
								extra_callbacks.append(__UpperCamelCase     )
				if logging_callback is None:
								SCREAMING_SNAKE_CASE__       =LoggingCallback()

				SCREAMING_SNAKE_CASE__       ={}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								SCREAMING_SNAKE_CASE__       =16
								# train_params["amp_level"] = args.fp16_opt_level

				if args.gpus > 1:
								SCREAMING_SNAKE_CASE__       ='auto'  # "ddp"
								SCREAMING_SNAKE_CASE__       ='ddp'

				SCREAMING_SNAKE_CASE__       =args.accumulate_grad_batches
				SCREAMING_SNAKE_CASE__       =None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
				SCREAMING_SNAKE_CASE__       ='auto'

				SCREAMING_SNAKE_CASE__       =pl.Trainer.from_argparse_args(
				    __UpperCamelCase,    weights_summary=__UpperCamelCase,    callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()],    logger=__UpperCamelCase,    **__UpperCamelCase,    )

				if args.do_train:
								trainer.fit(__UpperCamelCase     )

				return trainer












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()

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

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

lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    if spm_diff == list(reversed(__UpperCamelCase     )     ):
        # AAA -> AA+A vs A+AA case.
        return True
    elif len(__UpperCamelCase     ) == len(__UpperCamelCase     ) and fast.decode(__UpperCamelCase     ) == fast.decode(__UpperCamelCase     ):
        # Second order OK
        # Barrich -> Barr + ich vs Bar + rich
        return True
    SCREAMING_SNAKE_CASE__       =slow.encode(slow.decode(__UpperCamelCase     )     )
    SCREAMING_SNAKE_CASE__       =fast.encode(fast.decode(__UpperCamelCase     )     )
    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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =fast.encode_plus(__UpperCamelCase     )[0]
    SCREAMING_SNAKE_CASE__       =enc.offsets
    SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    # 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(__UpperCamelCase,    __UpperCamelCase     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(__UpperCamelCase     ),    reversed(__UpperCamelCase     )     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     ) - i

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

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

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

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

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

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

    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[:first]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[last:]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[first:last]     )
    print()
    print(__UpperCamelCase     )
    return False



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    global perfect
    global imperfect
    global wrong
    global total

    SCREAMING_SNAKE_CASE__       =slow.encode(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =fast.encode(__UpperCamelCase     )

    SCREAMING_SNAKE_CASE__       =False
    total += 1

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

    if total % 10_000 == 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(__UpperCamelCase     )}\n{fast.tokenize(__UpperCamelCase     )}"""



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

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


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

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






