import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


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






 '''simple docstring'''






 UpperCAmelCase_     			= AltDiffusionPipeline
 UpperCAmelCase_     			= TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase_     			= TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase_     			= TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase_     			= TEXT_TO_IMAGE_IMAGE_PARAMS




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__					     = UNetaDConditionModel(
            block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=4      ,   out_channels=4      ,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,   cross_attention_dim=32      ,   )
        lowerCAmelCase__					     = DDIMScheduler(
            beta_start=0.00_085      ,   beta_end=0.012      ,   beta_schedule='scaled_linear'      ,   clip_sample=lowercase__      ,   set_alpha_to_one=lowercase__      ,   )
        torch.manual_seed(0)
        lowerCAmelCase__					     = AutoencoderKL(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,   latent_channels=4      ,   )

        # TODO: address the non-deterministic text encoder (fails for save-load tests)
        # torch.manual_seed(0)
        # text_encoder_config = RobertaSeriesConfig(
        #     hidden_size=32,
        #     project_dim=32,
        #     intermediate_size=37,
        #     layer_norm_eps=1e-05,
        #     num_attention_heads=4,
        #     num_hidden_layers=5,
        #     vocab_size=5002,
        # )
        # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

        torch.manual_seed(0)
        lowerCAmelCase__					     = CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   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=5_002      ,   )
        lowerCAmelCase__					     = CLIPTextModel(lowercase__)

        lowerCAmelCase__					     = XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
        lowerCAmelCase__					     = 77

        lowerCAmelCase__					     = {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  Any      ,   lowercase__ :  Any=0):







        '''simple docstring'''



        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__					     = torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__					     = torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__					     = {
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = 'cpu'  # ensure determinism for the device-dependent torch.Generator

        lowerCAmelCase__					     = self.get_dummy_components()
        torch.manual_seed(0)
        lowerCAmelCase__					     = RobertaSeriesConfig(
            hidden_size=32      ,   project_dim=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   vocab_size=5_002      ,   )
        # TODO: remove after fixing the non-deterministic text encoder
        lowerCAmelCase__					     = RobertaSeriesModelWithTransformation(lowercase__)
        lowerCAmelCase__					     = text_encoder

        lowerCAmelCase__					     = AltDiffusionPipeline(**lowercase__)
        lowerCAmelCase__					     = alt_pipe.to(lowercase__)
        alt_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__					     = self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__					     = 'A photo of an astronaut'
        lowerCAmelCase__					     = alt_pipe(**lowercase__)
        lowerCAmelCase__					     = output.images
        lowerCAmelCase__					     = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCAmelCase__					     = np.array(
            [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093])

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




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = 'cpu'  # ensure determinism for the device-dependent torch.Generator

        lowerCAmelCase__					     = self.get_dummy_components()
        lowerCAmelCase__					     = PNDMScheduler(skip_prk_steps=lowercase__)
        torch.manual_seed(0)
        lowerCAmelCase__					     = RobertaSeriesConfig(
            hidden_size=32      ,   project_dim=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   vocab_size=5_002      ,   )
        # TODO: remove after fixing the non-deterministic text encoder
        lowerCAmelCase__					     = RobertaSeriesModelWithTransformation(lowercase__)
        lowerCAmelCase__					     = text_encoder
        lowerCAmelCase__					     = AltDiffusionPipeline(**lowercase__)
        lowerCAmelCase__					     = alt_pipe.to(lowercase__)
        alt_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__					     = self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__					     = alt_pipe(**lowercase__)
        lowerCAmelCase__					     = output.images
        lowerCAmelCase__					     = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCAmelCase__					     = np.array(
            [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237])

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






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






 '''simple docstring'''






 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'      ,   safety_checker=lowercase__)
        lowerCAmelCase__					     = alt_pipe.to(lowercase__)
        alt_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__					     = 'A painting of a squirrel eating a burger'
        lowerCAmelCase__					     = torch.manual_seed(0)
        lowerCAmelCase__					     = alt_pipe([prompt]      ,   generator=lowercase__      ,   guidance_scale=6.0      ,   num_inference_steps=20      ,   output_type='np')

        lowerCAmelCase__					     = output.images

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

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase__					     = np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586])

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




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = DDIMScheduler.from_pretrained('BAAI/AltDiffusion'      ,   subfolder='scheduler')

        lowerCAmelCase__					     = AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'      ,   scheduler=lowercase__      ,   safety_checker=lowercase__)
        lowerCAmelCase__					     = alt_pipe.to(lowercase__)
        alt_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__					     = 'A painting of a squirrel eating a burger'
        lowerCAmelCase__					     = torch.manual_seed(0)

        lowerCAmelCase__					     = alt_pipe([prompt]      ,   generator=lowercase__      ,   num_inference_steps=2      ,   output_type='numpy')
        lowerCAmelCase__					     = output.images

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

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase__					     = np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323])

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

from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase__     						=			300  # TEMPERATURE (unit = K)






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  ):

       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()
