




"""simple docstring"""




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}""")












import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =filter(lambda __UpperCamelCase     : p.requires_grad,    model.parameters()     )
    SCREAMING_SNAKE_CASE__       =sum([np.prod(p.size()     ) for p in model_parameters]     )
    return params


lowerCamelCase_  						=       logging.getLogger(__name__)
class 				__a (			pl.Callback					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ={f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
         pl_module.logger.log_metrics(_UpperCamelCase						)




     @rank_zero_only
     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Tuple=True						)		->       None:


         '''simple docstring'''





         logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
         SCREAMING_SNAKE_CASE__       =trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}						)
         # Log results
         SCREAMING_SNAKE_CASE__       =Path(pl_module.hparams.output_dir						)
         if type_path == "test":
             SCREAMING_SNAKE_CASE__       =od / """test_results.txt"""
             SCREAMING_SNAKE_CASE__       =od / """test_generations.txt"""
         else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             SCREAMING_SNAKE_CASE__       =od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
             SCREAMING_SNAKE_CASE__       =od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
             results_file.parent.mkdir(exist_ok=_UpperCamelCase						)
             generations_file.parent.mkdir(exist_ok=_UpperCamelCase						)
         with open(_UpperCamelCase    ,"""a+"""						) as writer:
             for key in sorted(_UpperCamelCase						):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 SCREAMING_SNAKE_CASE__       =metrics[key]
                 if isinstance(_UpperCamelCase    ,torch.Tensor						):
                     SCREAMING_SNAKE_CASE__       =val.item()
                 SCREAMING_SNAKE_CASE__       =f"""{key}: {val:.6f}\n"""
                 writer.write(_UpperCamelCase						)

         if not save_generations:
             return

         if "preds" in metrics:
             SCREAMING_SNAKE_CASE__       ="""\n""".join(metrics["""preds"""]						)
             generations_file.open("""w+"""						).write(_UpperCamelCase						)




     @rank_zero_only
     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Dict						)		->       Dict:


         '''simple docstring'''





         try:
             SCREAMING_SNAKE_CASE__       =pl_module.model.model.num_parameters()
         except AttributeError:
             SCREAMING_SNAKE_CASE__       =pl_module.model.num_parameters()

         SCREAMING_SNAKE_CASE__       =count_trainable_parameters(_UpperCamelCase						)
         # mp stands for million parameters
         trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}						)




     @rank_zero_only
     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule						)		->       Any:


         '''simple docstring'''





         save_json(pl_module.metrics    ,pl_module.metrics_save_path						)
         return self._write_logs(_UpperCamelCase    ,_UpperCamelCase    ,"""test"""						)




     @rank_zero_only
     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			List[Any]						)		->       Dict:


         '''simple docstring'''





         save_json(pl_module.metrics    ,pl_module.metrics_save_path						)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")






def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=1,    __UpperCamelCase=False     ):
    if metric == "rouge2":
        SCREAMING_SNAKE_CASE__       ="""{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        SCREAMING_SNAKE_CASE__       ="""{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "loss":
        SCREAMING_SNAKE_CASE__       ="""{val_avg_loss:.4f}-{step_count}"""
    else:
        raise NotImplementedError(
            f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
            """ this function."""     )

    SCREAMING_SNAKE_CASE__       =ModelCheckpoint(
        dirpath=__UpperCamelCase,    filename=__UpperCamelCase,    monitor=f"""val_{metric}""",    mode="""min""" if """loss""" in metric else """max""",    save_top_k=__UpperCamelCase,    )
    return checkpoint_callback




def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    return EarlyStopping(
        monitor=f"""val_{metric}""",    mode="""min""" if """loss""" in metric else """max""",    patience=__UpperCamelCase,    verbose=__UpperCamelCase,    )






