






"""simple docstring"""







import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 					lowerCamelCase__					(       unittest.TestCase ):






    def 				_UpperCamelCase							( self					):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






    @property
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       1
          UpperCAmelCase							=       3
          UpperCAmelCase							=       (32, 32)

          UpperCAmelCase							=       floats_tensor((batch_size, num_channels) + sizes	,rng=random.Random(0					)					).to(A					)
          return image






    @property
    def 				_UpperCamelCase							( self					):
          torch.manual_seed(0					)
          UpperCAmelCase							=       UNetaDConditionModel(
              block_out_channels=(32, 64)	,layers_per_block=2	,sample_size=32	,in_channels=4	,out_channels=4	,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,cross_attention_dim=32	,)
          return model






    @property
    def 				_UpperCamelCase							( self					):
          torch.manual_seed(0					)
          UpperCAmelCase							=       AutoencoderKL(
              block_out_channels=[32, 64]	,in_channels=3	,out_channels=3	,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,latent_channels=4	,)
          return model






    @property
    def 				_UpperCamelCase							( self					):
          torch.manual_seed(0					)
          UpperCAmelCase							=       CLIPTextConfig(
              bos_token_id=0	,eos_token_id=2	,hidden_size=32	,intermediate_size=37	,layer_norm_eps=1e-0_5	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=1_000	,)
          return CLIPTextModel(A					)






    @property
    def 				_UpperCamelCase							( self					):
          def extract(*A	,**A					):







                class 					lowerCamelCase__					:






                    def __init__( self					):
                          UpperCAmelCase							=       torch.ones([0]					)






                    def 				_UpperCamelCase							( self	,A					):
                          self.pixel_values.to(A					)
                          return self







                return Out()

          return extract






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase							=       self.dummy_cond_unet
          UpperCAmelCase							=       DDIMScheduler(
              beta_start=0.00085	,beta_end=0.012	,beta_schedule="""scaled_linear"""	,clip_sample=A	,set_alpha_to_one=A	,)

          UpperCAmelCase							=       self.dummy_vae
          UpperCAmelCase							=       self.dummy_text_encoder
          UpperCAmelCase							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase							=       StableDiffusionPipeline(
              unet=A	,scheduler=A	,vae=A	,text_encoder=A	,tokenizer=A	,safety_checker=A	,feature_extractor=self.dummy_extractor	,)
          UpperCAmelCase							=       sd_pipe.to(A					)
          sd_pipe.set_progress_bar_config(disable=A					)

          UpperCAmelCase							=       """A painting of a squirrel eating a burger"""

          UpperCAmelCase							=       torch.Generator(device=A					).manual_seed(0					)
          UpperCAmelCase							=       sd_pipe([prompt]	,generator=A	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""					)
          UpperCAmelCase							=       output.images

          UpperCAmelCase							=       torch.Generator(device=A					).manual_seed(0					)
          UpperCAmelCase							=       sd_pipe(
              [prompt]	,generator=A	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""	,return_dict=A	,)[0]

          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase							=       np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase							=       self.dummy_cond_unet
          UpperCAmelCase							=       PNDMScheduler(skip_prk_steps=A					)
          UpperCAmelCase							=       self.dummy_vae
          UpperCAmelCase							=       self.dummy_text_encoder
          UpperCAmelCase							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase							=       StableDiffusionPipeline(
              unet=A	,scheduler=A	,vae=A	,text_encoder=A	,tokenizer=A	,safety_checker=A	,feature_extractor=self.dummy_extractor	,)
          UpperCAmelCase							=       sd_pipe.to(A					)
          sd_pipe.set_progress_bar_config(disable=A					)

          UpperCAmelCase							=       """A painting of a squirrel eating a burger"""
          UpperCAmelCase							=       torch.Generator(device=A					).manual_seed(0					)
          UpperCAmelCase							=       sd_pipe([prompt]	,generator=A	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""					)

          UpperCAmelCase							=       output.images

          UpperCAmelCase							=       torch.Generator(device=A					).manual_seed(0					)
          UpperCAmelCase							=       sd_pipe(
              [prompt]	,generator=A	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""	,return_dict=A	,)[0]

          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase							=       np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       StableDiffusionPipeline.from_pretrained(
              """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""	,safety_checker=A					)
          assert isinstance(A	,A					)
          assert isinstance(pipe.scheduler	,A					)
          assert pipe.safety_checker is None

          UpperCAmelCase							=       pipe("""example prompt"""	,num_inference_steps=2					).images[0]
          assert image is not None

          # check that there's no error when saving a pipeline with one of the models being None
          with tempfile.TemporaryDirectory() as tmpdirname:
                pipe.save_pretrained(A					)
                UpperCAmelCase							=       StableDiffusionPipeline.from_pretrained(A					)

          # sanity check that the pipeline still works
          assert pipe.safety_checker is None
          UpperCAmelCase							=       pipe("""example prompt"""	,num_inference_steps=2					).images[0]
          assert image is not None






    @unittest.skipIf(torch_device != """cuda"""	,"""This test requires a GPU"""					)
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.dummy_cond_unet
          UpperCAmelCase							=       PNDMScheduler(skip_prk_steps=A					)
          UpperCAmelCase							=       self.dummy_vae
          UpperCAmelCase							=       self.dummy_text_encoder
          UpperCAmelCase							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

          # put models in fp16
          UpperCAmelCase							=       unet.half()
          UpperCAmelCase							=       vae.half()
          UpperCAmelCase							=       bert.half()

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase							=       StableDiffusionPipeline(
              unet=A	,scheduler=A	,vae=A	,text_encoder=A	,tokenizer=A	,safety_checker=A	,feature_extractor=self.dummy_extractor	,)
          UpperCAmelCase							=       sd_pipe.to(A					)
          sd_pipe.set_progress_bar_config(disable=A					)

          UpperCAmelCase							=       """A painting of a squirrel eating a burger"""
          UpperCAmelCase							=       sd_pipe([prompt]	,num_inference_steps=2	,output_type="""np"""					).images

          assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 					lowerCamelCase__					(       unittest.TestCase ):






    def 				_UpperCamelCase							( self					):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """cuda"""
          UpperCAmelCase							=       StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					)
          UpperCAmelCase							=       pipe.to(A					)
          pipe.set_progress_bar_config(disable=A					)

          UpperCAmelCase							=       """a photo of a cat"""
          UpperCAmelCase							=       {
              """editing_prompt""": ["""sunglasses"""],
              """reverse_editing_direction""": [False],
              """edit_warmup_steps""": 10,
              """edit_guidance_scale""": 6,
              """edit_threshold""": 0.95,
              """edit_momentum_scale""": 0.5,
              """edit_mom_beta""": 0.6,
          }

          UpperCAmelCase							=       3
          UpperCAmelCase							=       7

          # no sega enabled
          UpperCAmelCase							=       torch.Generator(A					)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.34673113,
              0.38492733,
              0.37597352,
              0.34086335,
              0.35650748,
              0.35579205,
              0.3384763,
              0.34340236,
              0.3573271,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2

          # with sega enabled
          # generator = torch.manual_seed(seed)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**A	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.41887826,
              0.37728766,
              0.30138272,
              0.41416335,
              0.41664985,
              0.36283392,
              0.36191246,
              0.43364465,
              0.43001732,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """cuda"""
          UpperCAmelCase							=       StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					)
          UpperCAmelCase							=       pipe.to(A					)
          pipe.set_progress_bar_config(disable=A					)

          UpperCAmelCase							=       """an image of a crowded boulevard, realistic, 4k"""
          UpperCAmelCase							=       {
              """editing_prompt""": """crowd, crowded, people""",
              """reverse_editing_direction""": True,
              """edit_warmup_steps""": 10,
              """edit_guidance_scale""": 8.3,
              """edit_threshold""": 0.9,
              """edit_momentum_scale""": 0.5,
              """edit_mom_beta""": 0.6,
          }

          UpperCAmelCase							=       9
          UpperCAmelCase							=       7

          # no sega enabled
          UpperCAmelCase							=       torch.Generator(A					)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.43497998,
              0.91814065,
              0.7540739,
              0.55580205,
              0.8467265,
              0.5389691,
              0.62574506,
              0.58897763,
              0.50926757,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2

          # with sega enabled
          # generator = torch.manual_seed(seed)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**A	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.3089719,
              0.30500144,
              0.29016042,
              0.30630964,
              0.325687,
              0.29419225,
              0.2908091,
              0.28723598,
              0.27696294,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """cuda"""
          UpperCAmelCase							=       StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					)
          UpperCAmelCase							=       pipe.to(A					)
          pipe.set_progress_bar_config(disable=A					)

          UpperCAmelCase							=       """a castle next to a river"""
          UpperCAmelCase							=       {
              """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
              """reverse_editing_direction""": False,
              """edit_warmup_steps""": [15, 18],
              """edit_guidance_scale""": 6,
              """edit_threshold""": [0.9, 0.8],
              """edit_momentum_scale""": 0.5,
              """edit_mom_beta""": 0.6,
          }

          UpperCAmelCase							=       48
          UpperCAmelCase							=       7

          # no sega enabled
          UpperCAmelCase							=       torch.Generator(A					)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.75163555,
              0.76037145,
              0.61785,
              0.9189673,
              0.8627701,
              0.85189694,
              0.8512813,
              0.87012076,
              0.8312857,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2

          # with sega enabled
          # generator = torch.manual_seed(seed)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**A	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.73553365,
              0.7537271,
              0.74341905,
              0.66480356,
              0.6472925,
              0.63039416,
              0.64812905,
              0.6749717,
              0.6517102,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       """cuda"""
          UpperCAmelCase							=       StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,torch_dtype=torch.floataa					)
          UpperCAmelCase							=       pipe.to(A					)
          pipe.set_progress_bar_config(disable=A					)

          UpperCAmelCase							=       """a photo of a cat"""
          UpperCAmelCase							=       {
              """editing_prompt""": ["""sunglasses"""],
              """reverse_editing_direction""": [False],
              """edit_warmup_steps""": 10,
              """edit_guidance_scale""": 6,
              """edit_threshold""": 0.95,
              """edit_momentum_scale""": 0.5,
              """edit_mom_beta""": 0.6,
          }

          UpperCAmelCase							=       3
          UpperCAmelCase							=       7

          # no sega enabled
          UpperCAmelCase							=       torch.Generator(A					)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.34887695,
              0.3876953,
              0.375,
              0.34423828,
              0.3581543,
              0.35717773,
              0.3383789,
              0.34570312,
              0.359375,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2

          # with sega enabled
          # generator = torch.manual_seed(seed)
          generator.manual_seed(A					)
          UpperCAmelCase							=       pipe(
              [prompt]	,generator=A	,guidance_scale=A	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,**A	,)

          UpperCAmelCase							=       output.images
          UpperCAmelCase							=       image[0, -3:, -3:, -1]
          UpperCAmelCase							=       [
              0.42285156,
              0.36914062,
              0.29077148,
              0.42041016,
              0.41918945,
              0.35498047,
              0.3618164,
              0.4423828,
              0.43115234,
          ]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2











"""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()

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

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

_UpperCamelCase     			=  0
_UpperCamelCase     			=  0
_UpperCamelCase     			=  0
_UpperCamelCase     			=  0

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      if spm_diff == list(reversed(_snake_case							)							):
            # AAA -> AA+A vs A+AA case.
            return True
      elif len(_snake_case							) == len(_snake_case							) and fast.decode(_snake_case							) == fast.decode(_snake_case							):
            # Second order OK
            # Barrich -> Barr + ich vs Bar + rich
            return True
      UpperCAmelCase							=       slow.encode(slow.decode(_snake_case							)							)
      UpperCAmelCase							=       fast.encode(fast.decode(_snake_case							)							)
      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 (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       fast.encode_plus(_snake_case							)[0]
      UpperCAmelCase							=       enc.offsets
      UpperCAmelCase			,					UpperCAmelCase							=       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 (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      for i, (spm_id, tok_id) in enumerate(zip(_snake_case      ,       _snake_case							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(_snake_case							)      ,       reversed(_snake_case							)							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       len(_snake_case							) - i

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

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

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

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

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

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

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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      global perfect
      global imperfect
      global wrong
      global total

      UpperCAmelCase							=       slow.encode(_snake_case							)
      UpperCAmelCase							=       fast.encode(_snake_case							)

      UpperCAmelCase							=       False
      total += 1

      if slow_ids != fast_ids:
            if check_details(_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):
                  UpperCAmelCase							=       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(_snake_case							)}\n{fast.tokenize(_snake_case							)}'''

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






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

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


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

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



