






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     a    ( _SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE							,       unittest.TestCase   ):



      """simple docstring"""


      a  : List[str]							       =	StableDiffusionSAGPipeline
      a  : Any							       =	TEXT_TO_IMAGE_PARAMS
      a  : Tuple							       =	TEXT_TO_IMAGE_BATCH_PARAMS
      a  : Any							       =	TEXT_TO_IMAGE_IMAGE_PARAMS
      a  : Any							       =	TEXT_TO_IMAGE_IMAGE_PARAMS
      a  : Union[str, Any]							       =	False
      def 	UpperCAmelCase      (      self					:			str					)							->      List[str]:
        torch.manual_seed(0					)
        __UpperCAmelCase							:	List[Any]           =  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		,      )
        __UpperCAmelCase							:	List[Any]           =  DDIMScheduler(
            beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""		,      clip_sample=A_		,      set_alpha_to_one=A_		,      )
        torch.manual_seed(0					)
        __UpperCAmelCase							:	Optional[int]           =  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					)
        __UpperCAmelCase							:	Tuple           =  CLIPTextConfig(
            bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      )
        __UpperCAmelCase							:	Optional[int]           =  CLIPTextModel(A_					)
        __UpperCAmelCase							:	str           =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

        __UpperCAmelCase							:	int           =  {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Dict		,      __lowercase					:			Union[str, Any]=0					)							->      Optional[int]:
        if str(A_					).startswith("""mps"""					):
          __UpperCAmelCase							:	Optional[int]           =  torch.manual_seed(A_					)
        else:
          __UpperCAmelCase							:	Optional[Any]           =  torch.Generator(device=A_					).manual_seed(A_					)
        __UpperCAmelCase							:	Dict           =  {
            """prompt""": """.""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 1.0,
            """sag_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs




      def 	UpperCAmelCase      (      self					:			int					)							->      Tuple:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


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



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			Dict					)							->      Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Union[str, Any]:
        __UpperCAmelCase							:	Union[str, Any]           =  StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""					)
        __UpperCAmelCase							:	Optional[int]           =  sag_pipe.to(A_					)
        sag_pipe.set_progress_bar_config(disable=A_					)

        __UpperCAmelCase							:	str           =  """."""
        __UpperCAmelCase							:	Tuple           =  torch.manual_seed(0					)
        __UpperCAmelCase							:	Optional[int]           =  sag_pipe(
            [prompt]		,      generator=A_		,      guidance_scale=7.5		,      sag_scale=1.0		,      num_inference_steps=20		,      output_type="""np"""					)

        __UpperCAmelCase							:	Any           =  output.images

        __UpperCAmelCase							:	Optional[Any]           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCAmelCase							:	Optional[int]           =  np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Optional[Any]:
        __UpperCAmelCase							:	Optional[int]           =  StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
        __UpperCAmelCase							:	List[Any]           =  sag_pipe.to(A_					)
        sag_pipe.set_progress_bar_config(disable=A_					)

        __UpperCAmelCase							:	Optional[Any]           =  """."""
        __UpperCAmelCase							:	List[Any]           =  torch.manual_seed(0					)
        __UpperCAmelCase							:	Any           =  sag_pipe(
            [prompt]		,      generator=A_		,      guidance_scale=7.5		,      sag_scale=1.0		,      num_inference_steps=20		,      output_type="""np"""					)

        __UpperCAmelCase							:	int           =  output.images

        __UpperCAmelCase							:	Union[str, Any]           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCAmelCase							:	Union[str, Any]           =  np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]					)

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




      def 	UpperCAmelCase      (      self					:			Tuple					)							->      List[str]:
        __UpperCAmelCase							:	Optional[Any]           =  StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""					)
        __UpperCAmelCase							:	Dict           =  sag_pipe.to(A_					)
        sag_pipe.set_progress_bar_config(disable=A_					)

        __UpperCAmelCase							:	Optional[int]           =  """."""
        __UpperCAmelCase							:	Optional[Any]           =  torch.manual_seed(0					)
        __UpperCAmelCase							:	Dict           =  sag_pipe(
            [prompt]		,      width=768		,      height=512		,      generator=A_		,      guidance_scale=7.5		,      sag_scale=1.0		,      num_inference_steps=20		,      output_type="""np"""		,      )

        __UpperCAmelCase							:	Any           =  output.images

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




import requests


def       A       (					lowercase    ,       lowercase )   ->  None:
		'''simple docstring'''

		UpperCamelCase		=     {'Content-Type': 'application/json'}
		UpperCamelCase		=     requests.post(lowercase    ,       json={'text': message_body}    ,       headers=lowercase )
		if response.status_code != 200:
				UpperCamelCase		=     (
				    'Request to slack returned an error '
				    f'''{response.status_code}, the response is:\n{response.text}'''
				)
				raise ValueError(lowercase )


if __name__ == "__main__":
					# Set the slack url to the one provided by Slack when you create the webhook at
					# https://my.slack.com/services/new/incoming-webhook/
					send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")
