






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   ( _lowercase,   _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



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


    '''simple docstring'''



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


    '''simple docstring'''



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

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

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

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

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

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

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

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


    '''simple docstring'''



    global perfect
    global imperfect
    global wrong
    global total

    _lowercase     			=						slow.encode(_lowercase   )
    _lowercase     			=						fast.encode(_lowercase   )

    _lowercase     			=						False
    total += 1

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

    if total % 10000 == 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(_lowercase   )}\n{fast.tokenize(_lowercase   )}'''
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



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

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


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 random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPTextConfig(
           bos_token_id=0						, eos_token_id=2						, hidden_size=self.text_embedder_hidden_size						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, layer_norm_eps=1E-05						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=10_00						, )
       return CLIPTextModelWithProjection(_UpperCAmelCase   )



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

       snake_case_			= {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       snake_case_			= PriorTransformer(**_UpperCAmelCase   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       snake_case_			= nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size						, image_size=2_24						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_channels=3						, num_hidden_layers=5						, patch_size=14						, )

       snake_case_			= CLIPVisionModelWithProjection(_UpperCAmelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPImageProcessor(
           crop_size=2_24						, do_center_crop=_UpperCAmelCase						, do_normalize=_UpperCAmelCase						, do_resize=_UpperCAmelCase						, image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]						, image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]						, resample=3						, size=2_24						, )

       return image_processor



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

       snake_case_			= UnCLIPScheduler(
           variance_type='''fixed_small_log'''						, prediction_type='''sample'''						, num_train_timesteps=10_00						, clip_sample=_UpperCAmelCase						, clip_sample_range=10.0						, )

       snake_case_			= {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

       snake_case_			= {
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''strength''': 0.5,
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**_UpperCAmelCase   )
       snake_case_			= pipe.to(_UpperCAmelCase   )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(_UpperCAmelCase   )						, return_dict=_UpperCAmelCase						, )[0]

       snake_case_			= image[0, -10:]
       snake_case_			= image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       snake_case_			= np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]   )

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



 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

       self._test_inference_batch_single_identical(
           test_max_difference=_UpperCAmelCase						, relax_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

       self._test_attention_slicing_forward_pass(
           test_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )