




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 				__a (			__lowerCamelCase							,					unittest.TestCase					):


     """simple docstring"""


     _A    :   List[Any]     =  PaintByExamplePipeline
     _A    :   Dict     =  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     _A    :   List[Any]     =  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _A    :   Any     =  frozenset([]					)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




     def   __A    (     self      :			Union[str, Any]						)		->       List[str]:


         '''simple docstring'''





         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)    ,layers_per_block=2    ,sample_size=3_2    ,in_channels=9    ,out_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,cross_attention_dim=3_2    ,)
         SCREAMING_SNAKE_CASE__       =PNDMScheduler(skip_prk_steps=_UpperCamelCase						)
         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =AutoencoderKL(
             block_out_channels=[3_2, 6_4]    ,in_channels=3    ,out_channels=3    ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,latent_channels=4    ,)
         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =CLIPVisionConfig(
             hidden_size=3_2    ,projection_dim=3_2    ,intermediate_size=3_7    ,layer_norm_eps=1e-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,image_size=3_2    ,patch_size=4    ,)
         SCREAMING_SNAKE_CASE__       =PaintByExampleImageEncoder(_UpperCamelCase    ,proj_size=3_2						)
         SCREAMING_SNAKE_CASE__       =CLIPImageProcessor(crop_size=3_2    ,size=3_2						)

         SCREAMING_SNAKE_CASE__       ={
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """image_encoder""": image_encoder,
             """safety_checker""": None,
             """feature_extractor""": feature_extractor,
         }
         return components




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =np.array(image.convert("""RGB"""						)						)
         SCREAMING_SNAKE_CASE__       =image[None].transpose(0    ,3    ,1    ,2						)
         SCREAMING_SNAKE_CASE__       =torch.from_numpy(_UpperCamelCase						).to(dtype=torch.floataa						) / 127.5 - 1.0
         return image




     def   __A    (     self      :			List[Any]    ,_UpperCamelCase      :			Any="cpu"    ,_UpperCamelCase      :			int=0						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =floats_tensor((1, 3, 3_2, 3_2)    ,rng=random.Random(_UpperCamelCase						)						).to(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =image.cpu().permute(0    ,2    ,3    ,1						)[0]
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						).resize((6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						).resize((3_2, 3_2)						)

         if str(_UpperCamelCase						).startswith("""mps"""						):
             SCREAMING_SNAKE_CASE__       =torch.manual_seed(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       ={
             """example_image""": example_image,
             """image""": init_image,
             """mask_image""": mask_image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A    (     self      :			Union[str, Any]						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.get_dummy_components()

         # make sure here that pndm scheduler skips prk
         SCREAMING_SNAKE_CASE__       =PaintByExamplePipeline(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =pipe.to("""cpu"""						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =self.get_dummy_inputs()
         SCREAMING_SNAKE_CASE__       =pipe(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =output.images

         SCREAMING_SNAKE_CASE__       =image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         SCREAMING_SNAKE_CASE__       =np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]						)

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




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""cpu"""
         SCREAMING_SNAKE_CASE__       =self.get_dummy_inputs()
         inputs.pop("""mask_image"""						)
         SCREAMING_SNAKE_CASE__       =self.convert_to_pt(inputs.pop("""image"""						)						)
         SCREAMING_SNAKE_CASE__       =image.clamp(0    ,1						) / 2

         # make sure here that pndm scheduler skips prk
         SCREAMING_SNAKE_CASE__       =PaintByExamplePipeline(**self.get_dummy_components()						)
         SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =pipe(image=_UpperCamelCase    ,mask_image=mask_image[:, 0]    ,**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =output.images

         SCREAMING_SNAKE_CASE__       =image.cpu().permute(0    ,2    ,3    ,1						)[0]
         SCREAMING_SNAKE_CASE__       =mask_image.cpu().permute(0    ,2    ,3    ,1						)[0]

         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						)
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						)

         SCREAMING_SNAKE_CASE__       =pipe(**self.get_dummy_inputs()						)
         SCREAMING_SNAKE_CASE__       =output.images

         assert out_a.shape == (1, 6_4, 6_4, 3)
         assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5e-2




     def   __A    (     self      :			List[str]						)		->       List[str]:


         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3						)






@slow
@require_torch_gpu
class 				__a (			unittest.TestCase					):


     """simple docstring"""


     def   __A    (     self      :			List[str]						)		->       Optional[int]:


         '''simple docstring'''





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/dog_in_bucket.png"""						)
         SCREAMING_SNAKE_CASE__       =load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/mask.png"""						)
         SCREAMING_SNAKE_CASE__       =load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/panda.jpg"""						)

         SCREAMING_SNAKE_CASE__       =PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""						)
         SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =torch.manual_seed(3_2_1						)
         SCREAMING_SNAKE_CASE__       =pipe(
             image=_UpperCamelCase    ,mask_image=_UpperCamelCase    ,example_image=_UpperCamelCase    ,generator=_UpperCamelCase    ,guidance_scale=5.0    ,num_inference_steps=5_0    ,output_type="""np"""    ,)

         SCREAMING_SNAKE_CASE__       =output.images

         SCREAMING_SNAKE_CASE__       =image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         SCREAMING_SNAKE_CASE__       =np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]						)

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












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}""")






