import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 	lowerCamelCase       (UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__	,   unittest.TestCase       ):
  """simple docstring"""







  lowerCamelCase__  =    StableUnCLIPPipeline
  lowerCamelCase__  =    TEXT_TO_IMAGE_PARAMS
  lowerCamelCase__  =    TEXT_TO_IMAGE_BATCH_PARAMS
  lowerCamelCase__  =    TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCamelCase__  =    TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  lowerCamelCase__  =    False







  def  __A  (				self			:							Any )	->						str:
        SCREAMING_SNAKE_CASE_    =	32
        SCREAMING_SNAKE_CASE_    =	embedder_hidden_size

        # prior components

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	CLIPTextModelWithProjection(
            CLIPTextConfig(
                bos_token_id=0	,  eos_token_id=2	,  hidden_size=_UpperCAmelCase	,  projection_dim=_UpperCAmelCase	,  intermediate_size=37	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_000	,  ) )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	PriorTransformer(
            num_attention_heads=2	,  attention_head_dim=12	,  embedding_dim=_UpperCAmelCase	,  num_layers=1	,  )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	DDPMScheduler(
            variance_type="fixed_small_log"	,  prediction_type="sample"	,  num_train_timesteps=1_000	,  clip_sample=_UpperCAmelCase	,  clip_sample_range=5.0	,  beta_schedule="squaredcos_cap_v2"	,  )

        # regular denoising components

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase )
        SCREAMING_SNAKE_CASE_    =	DDPMScheduler(beta_schedule="squaredcos_cap_v2" )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0	,  eos_token_id=2	,  hidden_size=_UpperCAmelCase	,  projection_dim=32	,  intermediate_size=37	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_000	,  ) )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	UNetaDConditionModel(
            sample_size=32	,  in_channels=4	,  out_channels=4	,  down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")	,  up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")	,  block_out_channels=(32, 64)	,  attention_head_dim=(2, 4)	,  class_embed_type="projection"	,  projection_class_embeddings_input_dim=embedder_projection_dim * 2	,  cross_attention_dim=_UpperCAmelCase	,  layers_per_block=1	,  upcast_attention=_UpperCAmelCase	,  use_linear_projection=_UpperCAmelCase	,  )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	DDIMScheduler(
            beta_schedule="scaled_linear"	,  beta_start=0.0_0085	,  beta_end=0.012	,  prediction_type="v_prediction"	,  set_alpha_to_one=_UpperCAmelCase	,  steps_offset=1	,  )

        torch.manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	AutoencoderKL()

        SCREAMING_SNAKE_CASE_    =	{
            # prior components
            "prior_tokenizer": prior_tokenizer,
            "prior_text_encoder": prior_text_encoder,
            "prior": prior,
            "prior_scheduler": prior_scheduler,
            # image noising components
            "image_normalizer": image_normalizer,
            "image_noising_scheduler": image_noising_scheduler,
            # regular denoising components
            "tokenizer": tokenizer,
            "text_encoder": text_encoder,
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
        }

        return components







  def  __A  (				self			:							Dict	,  __magic_name__			:							Tuple	,  __magic_name__			:							List[str]=0 )	->						List[str]:


        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_    =	{
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "prior_num_inference_steps": 2,
            "output_type": "numpy",
        }
        return inputs







  def  __A  (				self			:							List[str] )	->						Optional[Any]:
        SCREAMING_SNAKE_CASE_    =	torch_device == "cpu"

        self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase )







  def  __A  (				self			:							List[Any] )	->						Optional[int]:
        SCREAMING_SNAKE_CASE_    =	torch_device in ["cpu", "mps"]

        self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase )



@slow
@require_torch_gpu
class 	lowerCamelCase       (unittest.TestCase       ):
  """simple docstring"""







  def  __A  (				self			:							Dict )	->						Optional[int]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def  __A  (				self			:							Union[str, Any] )	->						Dict:
        SCREAMING_SNAKE_CASE_    =	load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )

        SCREAMING_SNAKE_CASE_    =	StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"	,  torch_dtype=torch.floataa )
        pipe.to(_UpperCAmelCase )
        pipe.set_progress_bar_config(disable=_UpperCAmelCase )
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        SCREAMING_SNAKE_CASE_    =	torch.Generator(device="cpu" ).manual_seed(0 )
        SCREAMING_SNAKE_CASE_    =	pipe("anime turle"	,  generator=_UpperCAmelCase	,  output_type="np" )

        SCREAMING_SNAKE_CASE_    =	output.images[0]

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

        assert_mean_pixel_difference(_UpperCAmelCase	,  _UpperCAmelCase )







  def  __A  (				self			:							Any )	->						Optional[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        SCREAMING_SNAKE_CASE_    =	StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"	,  torch_dtype=torch.floataa )
        SCREAMING_SNAKE_CASE_    =	pipe.to(_UpperCAmelCase )
        pipe.set_progress_bar_config(disable=_UpperCAmelCase )
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        SCREAMING_SNAKE_CASE_    =	pipe(
            "anime turtle"	,  prior_num_inference_steps=2	,  num_inference_steps=2	,  output_type="np"	,  )

        SCREAMING_SNAKE_CASE_    =	torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9






from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""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()

