
import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       str				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      @property
      def 		snake_case_			(      self		:       str				):
          __lowercase		:			Dict	    =       1
          __lowercase		:			Any	    =       3
          __lowercase		:			Dict	    =       (32, 32)

          __lowercase		:			int	    =       floats_tensor((batch_size, num_channels) + sizes				,       rng=random.Random(0				)				).to(_snake_case				)
          return image




      @property
      def 		snake_case_			(      self		:       List[Any]				):
          torch.manual_seed(0				)
          __lowercase		:			Union[str, 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				,       )
          return model




      @property
      def 		snake_case_			(      self		:       Optional[int]				):
          torch.manual_seed(0				)
          __lowercase		:			str	    =       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				,       )
          return model




      @property
      def 		snake_case_			(      self		:       Any				):
          torch.manual_seed(0				)
          __lowercase		:			Any	    =       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				,       )
          return CLIPTextModel(_snake_case				)




      @property
      def 		snake_case_			(      self		:       Optional[int]				):
          def extract(*_snake_case		:       str				,       **_snake_case		:       Dict				):


              class 						__lowerCAmelCase	:



                    """simple docstring"""







                    def __init__(      self		:       Optional[Any]				):
                        __lowercase		:			List[str]	    =       torch.ones([0]				)




                    def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       Tuple				):
                        self.pixel_values.to(_snake_case				)
                        return self






              return Out()

          return extract




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Optional[int]	    =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __lowercase		:			Optional[int]	    =       self.dummy_cond_unet
          __lowercase		:			Tuple	    =       DDIMScheduler(
              beta_start=0.0_00_85				,       beta_end=0.0_12				,       beta_schedule='''scaled_linear'''				,       clip_sample=_snake_case				,       set_alpha_to_one=_snake_case				,       )

          __lowercase		:			Optional[int]	    =       self.dummy_vae
          __lowercase		:			Dict	    =       self.dummy_text_encoder
          __lowercase		:			Tuple	    =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

          # make sure here that pndm scheduler skips prk
          __lowercase		:			Dict	    =       StableDiffusionPipeline(
              unet=_snake_case				,       scheduler=_snake_case				,       vae=_snake_case				,       text_encoder=_snake_case				,       tokenizer=_snake_case				,       safety_checker=_snake_case				,       feature_extractor=self.dummy_extractor				,       )
          __lowercase		:			Optional[Any]	    =       sd_pipe.to(_snake_case				)
          sd_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       '''A painting of a squirrel eating a burger'''

          __lowercase		:			Tuple	    =       torch.Generator(device=_snake_case				).manual_seed(0				)
          __lowercase		:			Optional[Any]	    =       sd_pipe([prompt]				,       generator=_snake_case				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='''np'''				)
          __lowercase		:			Optional[Any]	    =       output.images

          __lowercase		:			Union[str, Any]	    =       torch.Generator(device=_snake_case				).manual_seed(0				)
          __lowercase		:			List[str]	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='''np'''				,       return_dict=_snake_case				,       )[0]

          __lowercase		:			List[Any]	    =       image[0, -3:, -3:, -1]
          __lowercase		:			List[str]	    =       image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          __lowercase		:			int	    =       np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]				)

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




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			Any	    =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __lowercase		:			int	    =       self.dummy_cond_unet
          __lowercase		:			Union[str, Any]	    =       PNDMScheduler(skip_prk_steps=_snake_case				)
          __lowercase		:			Tuple	    =       self.dummy_vae
          __lowercase		:			Optional[int]	    =       self.dummy_text_encoder
          __lowercase		:			Any	    =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

          # make sure here that pndm scheduler skips prk
          __lowercase		:			Optional[int]	    =       StableDiffusionPipeline(
              unet=_snake_case				,       scheduler=_snake_case				,       vae=_snake_case				,       text_encoder=_snake_case				,       tokenizer=_snake_case				,       safety_checker=_snake_case				,       feature_extractor=self.dummy_extractor				,       )
          __lowercase		:			Optional[Any]	    =       sd_pipe.to(_snake_case				)
          sd_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Union[str, Any]	    =       '''A painting of a squirrel eating a burger'''
          __lowercase		:			List[str]	    =       torch.Generator(device=_snake_case				).manual_seed(0				)
          __lowercase		:			Tuple	    =       sd_pipe([prompt]				,       generator=_snake_case				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='''np'''				)

          __lowercase		:			Union[str, Any]	    =       output.images

          __lowercase		:			str	    =       torch.Generator(device=_snake_case				).manual_seed(0				)
          __lowercase		:			List[Any]	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='''np'''				,       return_dict=_snake_case				,       )[0]

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

          assert image.shape == (1, 64, 64, 3)
          __lowercase		:			Tuple	    =       np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]				)

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




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       StableDiffusionPipeline.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''				,       safety_checker=_snake_case				)
          assert isinstance(_snake_case				,       _snake_case				)
          assert isinstance(pipe.scheduler				,       _snake_case				)
          assert pipe.safety_checker is None

          __lowercase		:			Dict	    =       pipe('''example prompt'''				,       num_inference_steps=2				).images[0]
          assert image is not None

          # check that there's no error when saving a pipeline with one of the models being None
          with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			int	    =       StableDiffusionPipeline.from_pretrained(_snake_case				)

          # sanity check that the pipeline still works
          assert pipe.safety_checker is None
          __lowercase		:			Union[str, Any]	    =       pipe('''example prompt'''				,       num_inference_steps=2				).images[0]
          assert image is not None




      @unittest.skipIf(torch_device != '''cuda'''				,       '''This test requires a GPU'''				)
      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			Any	    =       self.dummy_cond_unet
          __lowercase		:			Union[str, Any]	    =       PNDMScheduler(skip_prk_steps=_snake_case				)
          __lowercase		:			Union[str, Any]	    =       self.dummy_vae
          __lowercase		:			Union[str, Any]	    =       self.dummy_text_encoder
          __lowercase		:			Optional[Any]	    =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

          # put models in fp16
          __lowercase		:			Union[str, Any]	    =       unet.half()
          __lowercase		:			Dict	    =       vae.half()
          __lowercase		:			Optional[Any]	    =       bert.half()

          # make sure here that pndm scheduler skips prk
          __lowercase		:			Any	    =       StableDiffusionPipeline(
              unet=_snake_case				,       scheduler=_snake_case				,       vae=_snake_case				,       text_encoder=_snake_case				,       tokenizer=_snake_case				,       safety_checker=_snake_case				,       feature_extractor=self.dummy_extractor				,       )
          __lowercase		:			Union[str, Any]	    =       sd_pipe.to(_snake_case				)
          sd_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Tuple	    =       '''A painting of a squirrel eating a burger'''
          __lowercase		:			str	    =       sd_pipe([prompt]				,       num_inference_steps=2				,       output_type='''np'''				).images

          assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Union[str, Any]				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[Any]	    =       StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				,       safety_checker=_snake_case				)
          __lowercase		:			int	    =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config				)
          __lowercase		:			List[str]	    =       sd_pipe.to(_snake_case				)
          sd_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			int	    =       (
              '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
              ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
              ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
              ''' children from bahnhof zoo, detailed '''
          )
          __lowercase		:			Any	    =       40_0366_0346
          __lowercase		:			List[Any]	    =       7

          # without safety guidance (sld_guidance_scale = 0)
          __lowercase		:			int	    =       torch.manual_seed(_snake_case				)
          __lowercase		:			Optional[Any]	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=_snake_case				,       num_inference_steps=50				,       output_type='''np'''				,       width=512				,       height=512				,       sld_guidance_scale=0				,       )

          __lowercase		:			Dict	    =       output.images
          __lowercase		:			Optional[int]	    =       image[0, -3:, -3:, -1]
          __lowercase		:			Any	    =       [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

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

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

          # without safety guidance (strong configuration)
          __lowercase		:			Tuple	    =       torch.manual_seed(_snake_case				)
          __lowercase		:			Union[str, Any]	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=_snake_case				,       num_inference_steps=50				,       output_type='''np'''				,       width=512				,       height=512				,       sld_guidance_scale=2000				,       sld_warmup_steps=7				,       sld_threshold=0.0_25				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

          __lowercase		:			Tuple	    =       output.images
          __lowercase		:			Optional[int]	    =       image[0, -3:, -3:, -1]
          __lowercase		:			int	    =       [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

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

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




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			Tuple	    =       StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				,       safety_checker=_snake_case				)
          __lowercase		:			str	    =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config				)
          __lowercase		:			Any	    =       sd_pipe.to(_snake_case				)
          sd_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Optional[int]	    =       '''padme amidala taking a bath artwork, safe for work, no nudity'''
          __lowercase		:			Union[str, Any]	    =       27_3497_1755
          __lowercase		:			str	    =       7

          __lowercase		:			Optional[Any]	    =       torch.manual_seed(_snake_case				)
          __lowercase		:			Tuple	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=_snake_case				,       num_inference_steps=50				,       output_type='''np'''				,       width=512				,       height=512				,       sld_guidance_scale=0				,       )

          __lowercase		:			Dict	    =       output.images
          __lowercase		:			List[Any]	    =       image[0, -3:, -3:, -1]
          __lowercase		:			Union[str, Any]	    =       [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

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

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

          __lowercase		:			List[Any]	    =       torch.manual_seed(_snake_case				)
          __lowercase		:			str	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=_snake_case				,       num_inference_steps=50				,       output_type='''np'''				,       width=512				,       height=512				,       sld_guidance_scale=2000				,       sld_warmup_steps=7				,       sld_threshold=0.0_25				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

          __lowercase		:			List[str]	    =       output.images
          __lowercase		:			Any	    =       image[0, -3:, -3:, -1]
          __lowercase		:			Optional[Any]	    =       [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

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

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




      def 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			int	    =       StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
          __lowercase		:			Dict	    =       sd_pipe.to(_snake_case				)
          sd_pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Any	    =       (
              '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
              ''' leyendecker'''
          )
          __lowercase		:			Optional[Any]	    =       10_4435_5234
          __lowercase		:			Tuple	    =       12

          __lowercase		:			List[Any]	    =       torch.manual_seed(_snake_case				)
          __lowercase		:			Any	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=_snake_case				,       num_inference_steps=50				,       output_type='''np'''				,       width=512				,       height=512				,       sld_guidance_scale=0				,       )

          __lowercase		:			Tuple	    =       output.images
          __lowercase		:			List[str]	    =       image[0, -3:, -3:, -1]
          __lowercase		:			int	    =       np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]				)

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

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-7

          __lowercase		:			Tuple	    =       torch.manual_seed(_snake_case				)
          __lowercase		:			Union[str, Any]	    =       sd_pipe(
              [prompt]				,       generator=_snake_case				,       guidance_scale=_snake_case				,       num_inference_steps=50				,       output_type='''np'''				,       width=512				,       height=512				,       sld_guidance_scale=2000				,       sld_warmup_steps=7				,       sld_threshold=0.0_25				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

          __lowercase		:			Union[str, Any]	    =       output.images
          __lowercase		:			Optional[Any]	    =       image[0, -3:, -3:, -1]
          __lowercase		:			Dict	    =       np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]				)
          assert image.shape == (1, 512, 512, 3)

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


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Union[str, Any]	    =       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
          __lowercase		:			Any	    =       get_activation('''gelu'''				)
          self.assertTrue(torch.allclose(gelu_python(_snake_case				)				,       torch_builtin(_snake_case				)				)				)
          self.assertFalse(torch.allclose(gelu_python(_snake_case				)				,       gelu_new(_snake_case				)				)				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Any	    =       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
          __lowercase		:			List[str]	    =       get_activation('''gelu'''				)
          __lowercase		:			Optional[Any]	    =       get_activation('''gelu_10'''				)

          __lowercase		:			Tuple	    =       torch_builtin(_snake_case				)
          __lowercase		:			List[Any]	    =       geluaa(_snake_case				)

          __lowercase		:			Tuple	    =       torch.where(y_gelu_aa < 10.0				,       1				,       0				)

          self.assertTrue(torch.max(_snake_case				).item() == 10.0				)
          self.assertTrue(torch.allclose(y_gelu * clipped_mask				,       y_gelu_aa * clipped_mask				)				)




      def 		snake_case_			(      self		:       Any				):
          get_activation('''gelu'''				)
          get_activation('''gelu_10'''				)
          get_activation('''gelu_fast'''				)
          get_activation('''gelu_new'''				)
          get_activation('''gelu_python'''				)
          get_activation('''gelu_pytorch_tanh'''				)
          get_activation('''linear'''				)
          get_activation('''mish'''				)
          get_activation('''quick_gelu'''				)
          get_activation('''relu'''				)
          get_activation('''sigmoid'''				)
          get_activation('''silu'''				)
          get_activation('''swish'''				)
          get_activation('''tanh'''				)
          with self.assertRaises(_snake_case				):
              get_activation('''bogus'''				)
          with self.assertRaises(_snake_case				):
              get_activation(_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Union[str, Any]	    =       get_activation('''gelu'''				)
          __lowercase		:			List[str]	    =       1
          __lowercase		:			Union[str, Any]	    =       get_activation('''gelu'''				)
          self.assertEqual(acta.a				,       1				)
          with self.assertRaises(_snake_case				):
              __lowercase		:			Tuple	    =       acta.a


