

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class     __magic_name__     (							snake_case__						,    snake_case__						,    unittest.TestCase     ):
 UpperCAmelCase				     =    StableDiffusionParadigmsPipeline
 UpperCAmelCase				     =    TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase				     =    TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase				     =    TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase				     =    TEXT_TO_IMAGE_IMAGE_PARAMS


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   int:
   torch.manual_seed(0     )
   UpperCAmelCase          =	UNetaDConditionModel(
       block_out_channels=(3_2, 6_4)      ,    layers_per_block=2      ,    sample_size=3_2      ,    in_channels=4      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    cross_attention_dim=3_2      ,    attention_head_dim=(2, 4)      ,    use_linear_projection=_SCREAMING_SNAKE_CASE      ,    )
   UpperCAmelCase          =	DDIMParallelScheduler(
       beta_start=0.00_085      ,    beta_end=0.012      ,    beta_schedule="scaled_linear"      ,    clip_sample=_SCREAMING_SNAKE_CASE      ,    set_alpha_to_one=_SCREAMING_SNAKE_CASE      ,    )
   torch.manual_seed(0     )
   UpperCAmelCase          =	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      ,    sample_size=1_2_8      ,    )
   torch.manual_seed(0     )
   UpperCAmelCase          =	CLIPTextConfig(
       bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=3_2      ,    intermediate_size=3_7      ,    layer_norm_eps=1e-0_5      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=1_0_0_0      ,    hidden_act="gelu"      ,    projection_dim=5_1_2      ,    )
   UpperCAmelCase          =	CLIPTextModel(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   UpperCAmelCase          =	{
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "safety_checker": None,
       "feature_extractor": None,
   }
   return components


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Tuple=0     )     ->   Any:
   if str(_SCREAMING_SNAKE_CASE     ).startswith("mps"     ):
     UpperCAmelCase          =	torch.manual_seed(_SCREAMING_SNAKE_CASE     )
   else:
     UpperCAmelCase          =	torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	{
       "prompt": "a photograph of an astronaut riding a horse",
       "generator": generator,
       "num_inference_steps": 1_0,
       "guidance_scale": 6.0,
       "output_type": "numpy",
       "parallel": 3,
       "debug": True,
   }
   return inputs


 def _UpperCamelCase						(    self		:			Any     )     ->   Tuple:
   UpperCAmelCase          =	"cpu"  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase          =	self.get_dummy_components()
   UpperCAmelCase          =	StableDiffusionParadigmsPipeline(**_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
   sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	sd_pipe(**_SCREAMING_SNAKE_CASE     ).images
   UpperCAmelCase          =	image[0, -3:, -3:, -1]
   assert image.shape == (1, 6_4, 6_4, 3)

   UpperCAmelCase          =	np.array([0.4_773, 0.5_417, 0.4_723, 0.4_925, 0.5_631, 0.4_752, 0.5_240, 0.4_935, 0.5_023]     )

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


 def _UpperCamelCase						(    self		:			List[Any]     )     ->   List[Any]:
   UpperCAmelCase          =	"cpu"  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase          =	self.get_dummy_components()
   torch.manual_seed(0     )
   UpperCAmelCase          =	DDPMParallelScheduler()
   torch.manual_seed(0     )
   UpperCAmelCase          =	StableDiffusionParadigmsPipeline(**_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
   sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	sd_pipe(**_SCREAMING_SNAKE_CASE     ).images
   UpperCAmelCase          =	image[0, -3:, -3:, -1]
   assert image.shape == (1, 6_4, 6_4, 3)

   UpperCAmelCase          =	np.array([0.3_573, 0.4_420, 0.4_960, 0.4_799, 0.3_796, 0.3_879, 0.4_819, 0.4_365, 0.4_468]     )

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


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   Any:
   super().test_inference_batch_consistent(batch_sizes=[1, 2]     )


 def _UpperCamelCase						(    self		:			Dict     )     ->   Dict:
   super().test_inference_batch_single_identical(batch_size=2      ,    expected_max_diff=3e-3     )


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   int:
   UpperCAmelCase          =	"cpu"  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase          =	self.get_dummy_components()
   UpperCAmelCase          =	StableDiffusionParadigmsPipeline(**_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
   sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

   UpperCAmelCase          =	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	"french fries"
   UpperCAmelCase          =	sd_pipe(**_SCREAMING_SNAKE_CASE      ,    negative_prompt=_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	output.images
   UpperCAmelCase          =	image[0, -3:, -3:, -1]

   assert image.shape == (1, 6_4, 6_4, 3)

   UpperCAmelCase          =	np.array([0.4_771, 0.5_420, 0.4_683, 0.4_918, 0.5_636, 0.4_725, 0.5_230, 0.4_923, 0.5_015]     )

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






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


 def _UpperCamelCase						(    self		:			Tuple     )     ->   List[Any]:
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			Tuple=0     )     ->   str:
   UpperCAmelCase          =	torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase          =	{
       "prompt": "a photograph of an astronaut riding a horse",
       "generator": generator,
       "num_inference_steps": 1_0,
       "guidance_scale": 7.5,
       "output_type": "numpy",
       "parallel": 3,
       "debug": True,
   }
   return inputs


 def _UpperCamelCase						(    self		:			Any     )     ->   Dict:
   UpperCAmelCase          =	"stabilityai/stable-diffusion-2-base"
   UpperCAmelCase          =	DDIMParallelScheduler.from_pretrained(_SCREAMING_SNAKE_CASE      ,    subfolder="scheduler"     )
   UpperCAmelCase          =	StableDiffusionParadigmsPipeline.from_pretrained(_SCREAMING_SNAKE_CASE      ,    scheduler=_SCREAMING_SNAKE_CASE      ,    safety_checker=_SCREAMING_SNAKE_CASE     )
   pipe.to(_SCREAMING_SNAKE_CASE     )
   pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )
   pipe.enable_attention_slicing()

   UpperCAmelCase          =	self.get_inputs()
   UpperCAmelCase          =	pipe(**_SCREAMING_SNAKE_CASE     ).images
   UpperCAmelCase          =	image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

   UpperCAmelCase          =	np.array([0.9_622, 0.9_602, 0.9_748, 0.9_591, 0.9_630, 0.9_691, 0.9_661, 0.9_631, 0.9_741]     )

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











def   snake_case		(				UpperCAmelCase						:							set,    UpperCAmelCase						:							set ):
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_a={set_a}] is not a set'
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_b={set_b}] is not a set'

				return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()





