



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  KandinskyInpaintPipeline
       UpperCamelCase		=  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
       UpperCamelCase		=  [
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
           '''mask_image''',
       ]
       UpperCamelCase		=  [
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase		=  False



       @property
       def   lowercase__       (							self    :						Any					)				->		Union[str, Any]:


             '''simple docstring'''




             return 32



       @property
       def   lowercase__       (							self    :						Tuple					)				->		str:


             '''simple docstring'''




             return 32



       @property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Optional[int]:


             '''simple docstring'''




             return self.time_input_dim



       @property
       def   lowercase__       (							self    :						Optional[int]					)				->		Optional[int]:


             '''simple docstring'''




             return self.time_input_dim * 4



       @property
       def   lowercase__       (							self    :						List[str]					)				->		Optional[int]:


             '''simple docstring'''




             return 100



       @property
       def   lowercase__       (							self    :						Tuple					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"					)
             return tokenizer



       @property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		List[Any]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							MCLIPConfig(
                 numDims=self.cross_attention_dim	,       transformerDimensions=self.text_embedder_hidden_size	,       hidden_size=self.text_embedder_hidden_size	,       intermediate_size=37	,       num_attention_heads=4	,       num_hidden_layers=5	,       vocab_size=1005	,       )

             UpperCAmelCase_    				=							MultilingualCLIP(_UpperCAmelCase					)
             UpperCAmelCase_    				=							text_encoder.eval()

             return text_encoder



       @property
       def   lowercase__       (							self    :						Any					)				->		Tuple:


             '''simple docstring'''




             torch.manual_seed(0					)

             UpperCAmelCase_    				=							{
                 "in_channels": 9,
                 # Out channels is double in channels because predicts mean and variance
                 "out_channels": 8,
                 "addition_embed_type": "text_image",
                 "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
                 "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
                 "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
                 "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                 "layers_per_block": 1,
                 "encoder_hid_dim": self.text_embedder_hidden_size,
                 "encoder_hid_dim_type": "text_image_proj",
                 "cross_attention_dim": self.cross_attention_dim,
                 "attention_head_dim": 4,
                 "resnet_time_scale_shift": "scale_shift",
                 "class_embed_type": None,
             }

             UpperCAmelCase_    				=							UNetaDConditionModel(**_UpperCAmelCase					)
             return model



       @property
       def   lowercase__       (							self    :						Tuple					)				->		Union[str, Any]:


             '''simple docstring'''




             return {
                 "block_out_channels": [32, 64],
                 "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                 "in_channels": 3,
                 "latent_channels": 4,
                 "layers_per_block": 1,
                 "norm_num_groups": 8,
                 "norm_type": "spatial",
                 "num_vq_embeddings": 12,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }



       @property
       def   lowercase__       (							self    :						Optional[int]					)				->		Union[str, Any]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							VQModel(**self.dummy_movq_kwargs					)
             return model



       def   lowercase__       (							self    :						Any					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.dummy_text_encoder
             UpperCAmelCase_    				=							self.dummy_tokenizer
             UpperCAmelCase_    				=							self.dummy_unet
             UpperCAmelCase_    				=							self.dummy_movq

             UpperCAmelCase_    				=							DDIMScheduler(
                 num_train_timesteps=1000	,       beta_schedule="linear"	,       beta_start=0.0_0085	,       beta_end=0.012	,       clip_sample=_UpperCAmelCase	,       set_alpha_to_one=_UpperCAmelCase	,       steps_offset=1	,       prediction_type="epsilon"	,       thresholding=_UpperCAmelCase	,       )

             UpperCAmelCase_    				=							{
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components



       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						int=0					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
             UpperCAmelCase_    				=							floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(seed + 1					)					).to(_UpperCAmelCase					)
             # create init_image
             UpperCAmelCase_    				=							floats_tensor((1, 3, 64, 64)	,       rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
             UpperCAmelCase_    				=							image.cpu().permute(0	,       2	,       3	,       1					)[0]
             UpperCAmelCase_    				=							Image.fromarray(np.uinta(_UpperCAmelCase					)					).convert("RGB"					).resize((256, 256)					)
             # create mask
             UpperCAmelCase_    				=							np.ones((64, 64)	,       dtype=np.floataa					)
             UpperCAmelCase_    				=							0

             if str(_UpperCAmelCase					).startswith("mps"					):
                   UpperCAmelCase_    				=							torch.manual_seed(_UpperCAmelCase					)
             else:
                   UpperCAmelCase_    				=							torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)
             UpperCAmelCase_    				=							{
                 "prompt": "horse",
                 "image": init_image,
                 "mask_image": mask,
                 "image_embeds": image_embeds,
                 "negative_image_embeds": negative_image_embeds,
                 "generator": generator,
                 "height": 64,
                 "width": 64,
                 "num_inference_steps": 2,
                 "guidance_scale": 4.0,
                 "output_type": "np",
             }
             return inputs



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


             '''simple docstring'''




             UpperCAmelCase_    				=							"cpu"

             UpperCAmelCase_    				=							self.get_dummy_components()

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

             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							pipe(**self.get_dummy_inputs(_UpperCAmelCase					)					)
             UpperCAmelCase_    				=							output.images

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

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

             print(F"""image.shape {image.shape}"""					)

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

             UpperCAmelCase_    				=							np.array(
                 [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

             assert (
                 np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
             ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
             assert (
                 np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
             ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







       def   lowercase__       (							self    :						List[Any]					)				->		int:


             '''simple docstring'''




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



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







       '''simple docstring'''


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


             '''simple docstring'''




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







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


             '''simple docstring'''




             UpperCAmelCase_    				=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"					)

             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"					)
             UpperCAmelCase_    				=							np.ones((768, 768)	,       dtype=np.floataa					)
             UpperCAmelCase_    				=							0

             UpperCAmelCase_    				=							"a hat"

             UpperCAmelCase_    				=							KandinskyPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-1-prior"	,       torch_dtype=torch.floataa					)
             pipe_prior.to(_UpperCAmelCase					)

             UpperCAmelCase_    				=							KandinskyInpaintPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-1-inpaint"	,       torch_dtype=torch.floataa					)
             UpperCAmelCase_    				=							pipeline.to(_UpperCAmelCase					)
             pipeline.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							torch.Generator(device="cpu"					).manual_seed(0					)
             UpperCAmelCase_				,					UpperCAmelCase_    				=							pipe_prior(
                 _UpperCAmelCase	,       generator=_UpperCAmelCase	,       num_inference_steps=5	,       negative_prompt=""	,       ).to_tuple()

             UpperCAmelCase_    				=							pipeline(
                 _UpperCAmelCase	,       image=_UpperCAmelCase	,       mask_image=_UpperCAmelCase	,       image_embeds=_UpperCAmelCase	,       negative_image_embeds=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       num_inference_steps=100	,       height=768	,       width=768	,       output_type="np"	,       )

             UpperCAmelCase_    				=							output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(_UpperCAmelCase	,       _UpperCAmelCase					)









"""simple docstring"""





lowerCamelCase      =		{
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
            UpperCAmelCase_    				=							(
                f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                f"""Valid values are: {', '.join(lowerCAmelCase__   )}"""
            )
            raise ValueError(lowerCAmelCase__   )
      return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




