




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase=0.999,    __UpperCamelCase="cosine",    ):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCamelCase     ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__UpperCamelCase     ):
            return math.exp(t * -12.0     )

    else:
        raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

    SCREAMING_SNAKE_CASE__       =[]
    for i in range(__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__       =i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE__       =(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__UpperCamelCase     ) / alpha_bar_fn(__UpperCamelCase     ),    __UpperCamelCase     )     )
    return torch.tensor(__UpperCamelCase,    dtype=torch.floataa     )




class 				__a (			__lowerCamelCase							,					__lowerCamelCase					):


     """simple docstring"""


     _A    :   List[str]     =  [e.name for e in KarrasDiffusionSchedulers]
     _A    :   Union[str, Any]     =  2




     @register_to_config
     def __init__(     self      :			Tuple    ,_UpperCamelCase      :			int = 1_0_0_0    ,_UpperCamelCase      :			float = 0.0_0085    ,_UpperCamelCase      :			float = 0.012    ,_UpperCamelCase      :			str = "linear"    ,_UpperCamelCase      :			Optional[Union[np.ndarray, List[float]]] = None    ,_UpperCamelCase      :			str = "epsilon"    ,_UpperCamelCase      :			str = "linspace"    ,_UpperCamelCase      :			int = 0    ,)		->       Tuple:


         '''simple docstring'''





         if trained_betas is not None:
             SCREAMING_SNAKE_CASE__       =torch.tensor(_UpperCamelCase    ,dtype=torch.floataa						)
         elif beta_schedule == "linear":
             SCREAMING_SNAKE_CASE__       =torch.linspace(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,dtype=torch.floataa						)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             SCREAMING_SNAKE_CASE__       =(
                 torch.linspace(beta_start**0.5    ,beta_end**0.5    ,_UpperCamelCase    ,dtype=torch.floataa						) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             SCREAMING_SNAKE_CASE__       =betas_for_alpha_bar(_UpperCamelCase						)
         else:
             raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

         SCREAMING_SNAKE_CASE__       =1.0 - self.betas
         SCREAMING_SNAKE_CASE__       =torch.cumprod(self.alphas    ,dim=0						)

         #  set all values
         self.set_timesteps(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)




     def   __A    (     self      :			int    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			Dict=None						)		->       Tuple:


         '''simple docstring'''





         if schedule_timesteps is None:
             SCREAMING_SNAKE_CASE__       =self.timesteps

         SCREAMING_SNAKE_CASE__       =(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter						) == 0:
             SCREAMING_SNAKE_CASE__       =1 if len(_UpperCamelCase						) > 1 else 0
         else:
             SCREAMING_SNAKE_CASE__       =timestep.cpu().item() if torch.is_tensor(_UpperCamelCase						) else timestep
             SCREAMING_SNAKE_CASE__       =self._index_counter[timestep_int]

         return indices[pos].item()




     @property
     def   __A    (     self      :			Any						)		->       Dict:


         '''simple docstring'''





         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			torch.FloatTensor    ,_UpperCamelCase      :			Union[float, torch.FloatTensor]    ,)		->       torch.FloatTensor:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.index_for_timestep(_UpperCamelCase						)

         if self.state_in_first_order:
             SCREAMING_SNAKE_CASE__       =self.sigmas[step_index]
         else:
             SCREAMING_SNAKE_CASE__       =self.sigmas_interpol[step_index - 1]

         SCREAMING_SNAKE_CASE__       =sample / ((sigma**2 + 1) ** 0.5)
         return sample




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Union[str, torch.device] = None    ,_UpperCamelCase      :			Optional[int] = None    ,)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =num_inference_steps

         SCREAMING_SNAKE_CASE__       =num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             SCREAMING_SNAKE_CASE__       =np.linspace(0    ,num_train_timesteps - 1    ,_UpperCamelCase    ,dtype=_UpperCamelCase						)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             SCREAMING_SNAKE_CASE__       =num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             SCREAMING_SNAKE_CASE__       =(np.arange(0    ,_UpperCamelCase						) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase						)
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             SCREAMING_SNAKE_CASE__       =num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             SCREAMING_SNAKE_CASE__       =(np.arange(_UpperCamelCase    ,0    ,-step_ratio						)).round().copy().astype(_UpperCamelCase						)
             timesteps -= 1
         else:
             raise ValueError(
                 f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""						)

         SCREAMING_SNAKE_CASE__       =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
         SCREAMING_SNAKE_CASE__       =torch.from_numpy(np.log(_UpperCamelCase						)						).to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =np.interp(_UpperCamelCase    ,np.arange(0    ,len(_UpperCamelCase						)						)    ,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
         SCREAMING_SNAKE_CASE__       =torch.from_numpy(_UpperCamelCase						).to(device=_UpperCamelCase						)

         # compute up and down sigmas
         SCREAMING_SNAKE_CASE__       =sigmas.roll(-1						)
         SCREAMING_SNAKE_CASE__       =0.0
         SCREAMING_SNAKE_CASE__       =(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
         SCREAMING_SNAKE_CASE__       =(sigmas_next**2 - sigmas_up**2) ** 0.5
         SCREAMING_SNAKE_CASE__       =0.0

         # compute interpolated sigmas
         SCREAMING_SNAKE_CASE__       =sigmas.log().lerp(sigmas_down.log()    ,0.5						).exp()
         SCREAMING_SNAKE_CASE__       =0.0

         # set sigmas
         SCREAMING_SNAKE_CASE__       =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
         SCREAMING_SNAKE_CASE__       =torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)
         SCREAMING_SNAKE_CASE__       =torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2						), sigmas_up[-1:]]						)
         SCREAMING_SNAKE_CASE__       =torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2						), sigmas_down[-1:]]						)

         if str(_UpperCamelCase						).startswith("""mps"""						):
             # mps does not support float64
             SCREAMING_SNAKE_CASE__       =torch.from_numpy(_UpperCamelCase						).to(_UpperCamelCase    ,dtype=torch.floataa						)
         else:
             SCREAMING_SNAKE_CASE__       =torch.from_numpy(_UpperCamelCase						).to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =self.sigma_to_t(_UpperCamelCase						).to(_UpperCamelCase    ,dtype=timesteps.dtype						)
         SCREAMING_SNAKE_CASE__       =torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])    ,dim=-1						).flatten()

         SCREAMING_SNAKE_CASE__       =torch.cat([timesteps[:1], interleaved_timesteps]						)

         SCREAMING_SNAKE_CASE__       =None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         SCREAMING_SNAKE_CASE__       =defaultdict(_UpperCamelCase						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =sigma.log()

         # get distribution
         SCREAMING_SNAKE_CASE__       =log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         SCREAMING_SNAKE_CASE__       =dists.ge(0						).cumsum(dim=0						).argmax(dim=0						).clamp(max=self.log_sigmas.shape[0] - 2						)
         SCREAMING_SNAKE_CASE__       =low_idx + 1

         SCREAMING_SNAKE_CASE__       =self.log_sigmas[low_idx]
         SCREAMING_SNAKE_CASE__       =self.log_sigmas[high_idx]

         # interpolate sigmas
         SCREAMING_SNAKE_CASE__       =(low - log_sigma) / (low - high)
         SCREAMING_SNAKE_CASE__       =w.clamp(0    ,1						)

         # transform interpolation to time range
         SCREAMING_SNAKE_CASE__       =(1 - w) * low_idx + w * high_idx
         SCREAMING_SNAKE_CASE__       =t.view(sigma.shape						)
         return t




     @property
     def   __A    (     self      :			Optional[int]						)		->       List[Any]:


         '''simple docstring'''





         return self.sample is None




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			Union[torch.FloatTensor, np.ndarray]    ,_UpperCamelCase      :			Union[float, torch.FloatTensor]    ,_UpperCamelCase      :			Union[torch.FloatTensor, np.ndarray]    ,_UpperCamelCase      :			Optional[torch.Generator] = None    ,_UpperCamelCase      :			bool = True    ,)		->       Union[SchedulerOutput, Tuple]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.index_for_timestep(_UpperCamelCase						)

         # advance index counter by 1
         SCREAMING_SNAKE_CASE__       =timestep.cpu().item() if torch.is_tensor(_UpperCamelCase						) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
             SCREAMING_SNAKE_CASE__       =self.sigmas[step_index]
             SCREAMING_SNAKE_CASE__       =self.sigmas_interpol[step_index]
             SCREAMING_SNAKE_CASE__       =self.sigmas_up[step_index]
             SCREAMING_SNAKE_CASE__       =self.sigmas_down[step_index - 1]
         else:
             # 2nd order / KPDM2's method
             SCREAMING_SNAKE_CASE__       =self.sigmas[step_index - 1]
             SCREAMING_SNAKE_CASE__       =self.sigmas_interpol[step_index - 1]
             SCREAMING_SNAKE_CASE__       =self.sigmas_up[step_index - 1]
             SCREAMING_SNAKE_CASE__       =self.sigmas_down[step_index - 1]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         SCREAMING_SNAKE_CASE__       =0
         SCREAMING_SNAKE_CASE__       =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         SCREAMING_SNAKE_CASE__       =model_output.device
         SCREAMING_SNAKE_CASE__       =randn_tensor(model_output.shape    ,dtype=model_output.dtype    ,device=_UpperCamelCase    ,generator=_UpperCamelCase						)

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             SCREAMING_SNAKE_CASE__       =sigma_hat if self.state_in_first_order else sigma_interpol
             SCREAMING_SNAKE_CASE__       =sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             SCREAMING_SNAKE_CASE__       =sigma_hat if self.state_in_first_order else sigma_interpol
             SCREAMING_SNAKE_CASE__       =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             raise NotImplementedError("""prediction_type not implemented yet: sample"""						)
         else:
             raise ValueError(
                 f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

         if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             SCREAMING_SNAKE_CASE__       =(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             SCREAMING_SNAKE_CASE__       =sigma_interpol - sigma_hat

             # store for 2nd order step
             SCREAMING_SNAKE_CASE__       =sample
             SCREAMING_SNAKE_CASE__       =dt
             SCREAMING_SNAKE_CASE__       =sample + derivative * dt
         else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             SCREAMING_SNAKE_CASE__       =(sample - pred_original_sample) / sigma_interpol
             # 3. delta timestep
             SCREAMING_SNAKE_CASE__       =sigma_down - sigma_hat

             SCREAMING_SNAKE_CASE__       =self.sample
             SCREAMING_SNAKE_CASE__       =None

             SCREAMING_SNAKE_CASE__       =sample + derivative * dt
             SCREAMING_SNAKE_CASE__       =prev_sample + noise * sigma_up

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=_UpperCamelCase						)




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			torch.FloatTensor    ,_UpperCamelCase      :			torch.FloatTensor    ,_UpperCamelCase      :			torch.FloatTensor    ,)		->       torch.FloatTensor:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.sigmas.to(device=original_samples.device    ,dtype=original_samples.dtype						)
         if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase						):
             # mps does not support float64
             SCREAMING_SNAKE_CASE__       =self.timesteps.to(original_samples.device    ,dtype=torch.floataa						)
             SCREAMING_SNAKE_CASE__       =timesteps.to(original_samples.device    ,dtype=torch.floataa						)
         else:
             SCREAMING_SNAKE_CASE__       =self.timesteps.to(original_samples.device						)
             SCREAMING_SNAKE_CASE__       =timesteps.to(original_samples.device						)

         SCREAMING_SNAKE_CASE__       =[self.index_for_timestep(_UpperCamelCase    ,_UpperCamelCase						) for t in timesteps]

         SCREAMING_SNAKE_CASE__       =sigmas[step_indices].flatten()
         while len(sigma.shape						) < len(original_samples.shape						):
             SCREAMING_SNAKE_CASE__       =sigma.unsqueeze(-1						)

         SCREAMING_SNAKE_CASE__       =original_samples + noise * sigma
         return noisy_samples




     def __len__(     self      :			Optional[Any]						)		->       List[Any]:


         '''simple docstring'''





         return self.config.num_train_timesteps












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}""")






