
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    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     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = StableDiffusionSAGPipeline
	lowerCAmelCase__         = TEXT_TO_IMAGE_PARAMS
	lowerCAmelCase__         = TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCAmelCase__         = TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase__         = TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					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=0.0_0_0_8_5						,  beta_end=0.0_1_2						,  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						,  )
					torch.manual_seed(0 )
					lowerCamelCase_						=			CLIPTextConfig(
					    bos_token_id=0						,  eos_token_id=2						,  hidden_size=32						,  intermediate_size=37						,  layer_norm_eps=1e-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  pad_token_id=1						,  vocab_size=1000						,  )
					lowerCamelCase_						=			CLIPTextModel(lowercase )
					lowerCamelCase_						=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=0 )				->       List[str]:
					if str(lowercase ).startswith("mps" ):
									lowerCamelCase_						=			torch.manual_seed(lowercase )
					else:
									lowerCamelCase_						=			torch.Generator(device=lowercase ).manual_seed(lowercase )
					lowerCamelCase_						=			{
					    "prompt": ".",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 1.0,
					    "sag_scale": 1.0,
					    "output_type": "numpy",
					}
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					super().test_inference_batch_single_identical(expected_max_diff=3e-3 )




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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
					lowerCamelCase_						=			sag_pipe.to(lowercase )
					sag_pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"."
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			sag_pipe(
					    [prompt]						,  generator=lowercase						,  guidance_scale=7.5						,  sag_scale=1.0						,  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_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9] )

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
					lowerCamelCase_						=			sag_pipe.to(lowercase )
					sag_pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"."
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			sag_pipe(
					    [prompt]						,  generator=lowercase						,  guidance_scale=7.5						,  sag_scale=1.0						,  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([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1] )

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
					lowerCamelCase_						=			sag_pipe.to(lowercase )
					sag_pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"."
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			sag_pipe(
					    [prompt]						,  width=768						,  height=512						,  generator=lowercase						,  guidance_scale=7.5						,  sag_scale=1.0						,  num_inference_steps=20						,  output_type="np"						,  )

					lowerCamelCase_						=			output.images

					assert image.shape == (1, 512, 768, 3)






def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




