import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





   '''simple docstring'''






   _snake_case    		=       KandinskyVaaControlnetImgaImgPipeline
   _snake_case    		=       ['image_embeds', 'negative_image_embeds', 'image', 'hint']
   _snake_case    		=       ['image_embeds', 'negative_image_embeds', 'image', 'hint']
   _snake_case    		=       [
       'generator',
       'height',
       'width',
       'strength',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   _snake_case    		=       False



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


       """simple docstring"""


       return 32



   @property
   def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


       """simple docstring"""


       return 32



   @property
   def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->int:


       """simple docstring"""


       return self.time_input_dim



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


       """simple docstring"""


       return self.time_input_dim * 4



   @property
   def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->str:


       """simple docstring"""


       return 100



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


       """simple docstring"""


       torch.manual_seed(0)

       _lowerCamelCase     :		Optional[Any] 						=						{
           """in_channels""": 8,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """image_hint""",
           """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""": """image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       _lowerCamelCase     :		List[Any] 						=						UNetaDConditionModel(**_UpperCamelCase)
       return model



   @property
   def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->List[Any]:


       """simple docstring"""


       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }



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


       """simple docstring"""


       torch.manual_seed(0)
       _lowerCamelCase     :		List[Any] 						=						VQModel(**self.dummy_movq_kwargs)
       return model



   def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


       """simple docstring"""


       _lowerCamelCase     :		Union[str, Any] 						=						self.dummy_unet
       _lowerCamelCase     :		Dict 						=						self.dummy_movq

       _lowerCamelCase     :		List[str] 						=						{
           """num_train_timesteps""": 1000,
           """beta_schedule""": """linear""",
           """beta_start""": 0.0_0_0_8_5,
           """beta_end""": 0.0_1_2,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       _lowerCamelCase     :		Dict 						=						DDIMScheduler(**_UpperCamelCase)

       _lowerCamelCase     :		Optional[Any] 						=						{
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components



   def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Any=0)				->str:


       """simple docstring"""


       _lowerCamelCase     :		Union[str, Any] 						=						floats_tensor((1, self.text_embedder_hidden_size)    ,  rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)
       _lowerCamelCase     :		str 						=						floats_tensor((1, self.text_embedder_hidden_size)    ,  rng=random.Random(seed + 1)).to(
           _UpperCamelCase)
       # create init_image
       _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)
       _lowerCamelCase     :		Optional[Any] 						=						image.cpu().permute(0    ,  2    ,  3    ,  1)[0]
       _lowerCamelCase     :		int 						=						Image.fromarray(np.uinta(_UpperCamelCase)).convert("""RGB""").resize((256, 256))
       # create hint
       _lowerCamelCase     :		List[Any] 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(_UpperCamelCase)).to(_UpperCamelCase)

       if str(_UpperCamelCase).startswith("""mps"""):
           _lowerCamelCase     :		Any 						=						torch.manual_seed(_UpperCamelCase)
       else:
           _lowerCamelCase     :		Any 						=						torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)
       _lowerCamelCase     :		List[Any] 						=						{
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """hint""": hint,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs




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


       """simple docstring"""


       _lowerCamelCase     :		str 						=						"""cpu"""

       _lowerCamelCase     :		Tuple 						=						self.get_dummy_components()

       _lowerCamelCase     :		Union[str, Any] 						=						self.pipeline_class(**_UpperCamelCase)
       _lowerCamelCase     :		str 						=						pipe.to(_UpperCamelCase)

       pipe.set_progress_bar_config(disable=_UpperCamelCase)

       _lowerCamelCase     :		List[str] 						=						pipe(**self.get_dummy_inputs(_UpperCamelCase))
       _lowerCamelCase     :		Any 						=						output.images

       _lowerCamelCase     :		Dict 						=						pipe(
           **self.get_dummy_inputs(_UpperCamelCase)    ,  return_dict=_UpperCamelCase    ,  )[0]

       _lowerCamelCase     :		Optional[int] 						=						image[0, -3:, -3:, -1]
       _lowerCamelCase     :		int 						=						image_from_tuple[0, -3:, -3:, -1]

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

       _lowerCamelCase     :		str 						=						np.array(
           [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6])
       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()}"""





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





   '''simple docstring'''






   def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


       """simple docstring"""


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




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


       """simple docstring"""


       _lowerCamelCase     :		Union[str, Any] 						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""")

       _lowerCamelCase     :		Any 						=						load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
       _lowerCamelCase     :		Optional[int] 						=						init_image.resize((512, 512))

       _lowerCamelCase     :		Dict 						=						load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinskyv22/hint_image_cat.png""")
       _lowerCamelCase     :		int 						=						torch.from_numpy(np.array(_UpperCamelCase)).float() / 255.0
       _lowerCamelCase     :		Dict 						=						hint.permute(2    ,  0    ,  1).unsqueeze(0)

       _lowerCamelCase     :		Tuple 						=						"""A robot, 4k photo"""

       _lowerCamelCase     :		Optional[int] 						=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-2-prior"""    ,  torch_dtype=torch.floataa)
       pipe_prior.to(_UpperCamelCase)

       _lowerCamelCase     :		str 						=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-2-controlnet-depth"""    ,  torch_dtype=torch.floataa)
       _lowerCamelCase     :		List[str] 						=						pipeline.to(_UpperCamelCase)

       pipeline.set_progress_bar_config(disable=_UpperCamelCase)

       _lowerCamelCase     :		str 						=						torch.Generator(device="""cpu""").manual_seed(0)

       _lowerCamelCase     :		Union[str, Any] 						=						pipe_prior(
           _UpperCamelCase    ,  image=_UpperCamelCase    ,  strength=0.8_5    ,  generator=_UpperCamelCase    ,  negative_prompt=""""""    ,  ).to_tuple()

       _lowerCamelCase     :		Optional[Any] 						=						pipeline(
           image=_UpperCamelCase    ,  image_embeds=_UpperCamelCase    ,  negative_image_embeds=_UpperCamelCase    ,  hint=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=100    ,  height=512    ,  width=512    ,  strength=0.5    ,  output_type="""np"""    ,  )

       _lowerCamelCase     :		List[Any] 						=						output.images[0]

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

       assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




