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

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

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

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

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

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

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

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






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       global perfect
       global imperfect
       global wrong
       global total

       lowerCAmelCase__	      =   slow.encode(lowerCAmelCase__      )
       lowerCAmelCase__	      =   fast.encode(lowerCAmelCase__      )

       lowerCAmelCase__	      =   False
       total += 1

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

       if total % 1_0_0_0_0 == 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(lowerCAmelCase__      )}\n{fast.tokenize(lowerCAmelCase__      )}"""






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

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


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}')

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    AutoencoderKL     ,       lowercase__   :    CLIPTextModel     ,       lowercase__   :    CLIPTokenizer     ,       lowercase__   :    UNetaDConditionModel     ,       lowercase__   :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]     ,       ):







        '''simple docstring'''



        super().__init__()

        self.register_modules(vae=lowercase__     ,       text_encoder=lowercase__     ,       tokenizer=lowercase__     ,       unet=lowercase__     ,       scheduler=lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str):







        '''simple docstring'''



        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.encode(tfms.ToTensor()(lowercase__).unsqueeze(0).to(self.device) * 2 - 1)
               lowerCAmelCase__	      =   0.18_215 * latent.latent_dist.sample()
        return latent




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   (1 / 0.18_215) * latent
        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.decode(lowercase__).sample
        lowerCAmelCase__	      =   (img / 2 + 0.5).clamp(0     ,       1)
        lowerCAmelCase__	      =   img.detach().cpu().permute(0     ,       2     ,       3     ,       1).numpy()
        lowerCAmelCase__	      =   (img * 255).round().astype('uint8')
        return Image.fromarray(img[0])




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer(
            lowercase__     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(text_input.input_ids.to(self.device))[0]

        lowerCAmelCase__	      =   self.tokenizer(
            ''     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])




 def __call__(     self   :    List[Any]     ,       lowercase__   :    Image.Image     ,       lowercase__   :    str     ,       lowercase__   :    float = 0.3     ,       lowercase__   :    float = 0.6     ,       lowercase__   :    float = 0.5     ,       lowercase__   :    int = 42     ,       lowercase__   :    int = 50     ,       lowercase__   :    float = 7.5     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   steps - int(kmin * steps)
        lowerCAmelCase__	      =   steps - int(kmax * steps)

        lowerCAmelCase__	      =   self.prep_text(lowercase__)

        self.scheduler.set_timesteps(lowercase__)

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   img.size
        lowerCAmelCase__	      =   self.encode(lowercase__)

        torch.manual_seed(lowercase__)
        lowerCAmelCase__	      =   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)     ,       ).to(self.device)

        lowerCAmelCase__	      =   self.scheduler.add_noise(
            lowercase__     ,       lowercase__     ,       timesteps=self.scheduler.timesteps[tmax]     ,       )

        lowerCAmelCase__	      =   torch.cat([latents] * 2)

        lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       self.scheduler.timesteps[tmax])

        with torch.no_grad():
               lowerCAmelCase__	      =   self.unet(
                   lowercase__     ,       self.scheduler.timesteps[tmax]     ,       encoder_hidden_states=lowercase__     ,       ).sample

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
        lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       self.scheduler.timesteps[tmax]     ,       lowercase__).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
               if i > tmax:
                      if i < tmin:  # layout generation phase
                             lowerCAmelCase__	      =   self.scheduler.add_noise(
                                 lowercase__     ,       lowercase__     ,       timesteps=lowercase__     ,       )

                             lowerCAmelCase__	      =   (mix_factor * latents) + (
                                 1 - mix_factor
                             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                             lowerCAmelCase__	      =   torch.cat([input] * 2)

                      else:  # content generation phase
                             lowerCAmelCase__	      =   torch.cat([latents] * 2)

                      lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       lowercase__)

                      with torch.no_grad():
                             lowerCAmelCase__	      =   self.unet(
                                 lowercase__     ,       lowercase__     ,       encoder_hidden_states=lowercase__     ,       ).sample

                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
                      lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       lowercase__     ,       lowercase__).prev_sample

        return self.decode(lowercase__)
