






import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_A						( UpperCAmelCase_	,				UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			List[str]						=					StableDiffusionDiffEditPipeline
  lowercase_						:			List[str]						=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
  lowercase_						:			Any						=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
  lowercase_						:			Dict						=					frozenset(
      [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  lowercase_						:			List[Any]						=					frozenset([] )

  def    a					(    self						:	str			):
     """simple docstring"""

     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   ,				attention_head_dim=(2, 4)   ,				use_linear_projection=lowerCamelCase__   ,				)
     __UpperCamelCase	:			int					=					DDIMScheduler(
         beta_start=0.0_0085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_one=lowerCamelCase__   ,				)
     __UpperCamelCase	:			str					=					DDIMInverseScheduler(
         beta_start=0.0_0085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_zero=lowerCamelCase__   ,				)
     torch.manual_seed(0			)
     __UpperCamelCase	:			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   ,				sample_size=1_28   ,				)
     torch.manual_seed(0			)
     __UpperCamelCase	:			Dict					=					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=10_00   ,				hidden_act="""gelu"""   ,				projection_dim=5_12   ,				)
     __UpperCamelCase	:			List[str]					=					CLIPTextModel(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

     __UpperCamelCase	:			int					=					{
         """unet""": unet,
         """scheduler""": scheduler,
         """inverse_scheduler""": inverse_scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }

     return components

  def    a					(    self						:	str   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					floats_tensor((1, 16, 16)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     __UpperCamelCase	:			Union[str, Any]					=					floats_tensor((1, 2, 4, 16, 16)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			List[str]					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			Union[str, Any]					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			List[str]					=					{
         """prompt""": """a dog and a newt""",
         """mask_image""": mask,
         """image_latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 2,
         """inpaint_strength""": 1.0,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }

     return inputs

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     __UpperCamelCase	:			Any					=					image.cpu().permute(0   ,				2   ,				3   ,				1			)[0]
     __UpperCamelCase	:			Optional[int]					=					Image.fromarray(np.uinta(lowerCamelCase__			)			).convert("""RGB"""			)
     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			Any					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			Optional[Any]					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			str					=					{
         """image""": image,
         """source_prompt""": """a cat and a frog""",
         """target_prompt""": """a dog and a newt""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """num_maps_per_mask""": 2,
         """mask_encode_strength""": 1.0,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }

     return inputs

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     __UpperCamelCase	:			Union[str, Any]					=					image.cpu().permute(0   ,				2   ,				3   ,				1			)[0]
     __UpperCamelCase	:			Union[str, Any]					=					Image.fromarray(np.uinta(lowerCamelCase__			)			).convert("""RGB"""			)
     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			str					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			Optional[Any]					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[Any]					=					{
         """image""": image,
         """prompt""": """a cat and a frog""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """inpaint_strength""": 1.0,
         """guidance_scale""": 6.0,
         """decode_latents""": True,
         """output_type""": """numpy""",
     }
     return inputs

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     if not hasattr(self.pipeline_class   ,				"""_optional_components"""			):
        return

     __UpperCamelCase	:			str					=					self.get_dummy_components()
     __UpperCamelCase	:			str					=					self.pipeline_class(**lowerCamelCase__			)
     pipe.to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     # set all optional components to None and update pipeline config accordingly
     for optional_component in pipe._optional_components:
        setattr(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__			)
     pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

     __UpperCamelCase	:			Any					=					self.get_dummy_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			List[str]					=					pipe(**lowerCamelCase__			)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowerCamelCase__			)
        __UpperCamelCase	:			int					=					self.pipeline_class.from_pretrained(lowerCamelCase__			)
        pipe_loaded.to(lowerCamelCase__			)
        pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__			)

     for optional_component in pipe._optional_components:
        self.assertTrue(
            getattr(lowerCamelCase__   ,				lowerCamelCase__			) is None   ,				f'`{optional_component}` did not stay set to None after loading.'   ,				)

     __UpperCamelCase	:			Union[str, Any]					=					self.get_dummy_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			Dict					=					pipe_loaded(**lowerCamelCase__			)[0]

     __UpperCamelCase	:			Union[str, Any]					=					np.abs(output - output_loaded			).max()
     self.assertLess(lowerCamelCase__   ,				1e-4			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					"""cpu"""

     __UpperCamelCase	:			int					=					self.get_dummy_components()
     __UpperCamelCase	:			Tuple					=					self.pipeline_class(**lowerCamelCase__			)
     pipe.to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					self.get_dummy_mask_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					pipe.generate_mask(**lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					mask[0, -3:, -3:]

     self.assertEqual(mask.shape   ,				(1, 16, 16)			)
     __UpperCamelCase	:			Optional[int]					=					np.array([0] * 9			)
     __UpperCamelCase	:			Any					=					np.abs(mask_slice.flatten() - expected_slice			).max()
     self.assertLessEqual(lowerCamelCase__   ,				1e-3			)
     self.assertEqual(mask[0, -3, -4]   ,				0			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					"""cpu"""

     __UpperCamelCase	:			Union[str, Any]					=					self.get_dummy_components()
     __UpperCamelCase	:			Optional[Any]					=					self.pipeline_class(**lowerCamelCase__			)
     pipe.to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			List[str]					=					self.get_dummy_inversion_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			Any					=					pipe.invert(**lowerCamelCase__			).images
     __UpperCamelCase	:			List[Any]					=					image[0, -1, -3:, -3:]

     self.assertEqual(image.shape   ,				(2, 32, 32, 3)			)
     __UpperCamelCase	:			Dict					=					np.array(
         [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]   ,				)
     __UpperCamelCase	:			Optional[int]					=					np.abs(image_slice.flatten() - expected_slice			).max()
     self.assertLessEqual(lowerCamelCase__   ,				1e-3			)

  def    a					(    self						:	List[Any]			):
     """simple docstring"""

     super().test_inference_batch_single_identical(expected_max_diff=5e-3			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					"""cpu"""

     __UpperCamelCase	:			int					=					self.get_dummy_components()

     __UpperCamelCase	:			Dict					=					{"""beta_start""": 0.0_0085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
     __UpperCamelCase	:			Optional[Any]					=					DPMSolverMultistepScheduler(**lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					DPMSolverMultistepInverseScheduler(**lowerCamelCase__			)

     __UpperCamelCase	:			List[Any]					=					self.pipeline_class(**lowerCamelCase__			)
     pipe.to(lowerCamelCase__			)
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Any					=					self.get_dummy_inversion_inputs(lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					pipe.invert(**lowerCamelCase__			).images
     __UpperCamelCase	:			Optional[Any]					=					image[0, -1, -3:, -3:]

     self.assertEqual(image.shape   ,				(2, 32, 32, 3)			)
     __UpperCamelCase	:			Any					=					np.array(
         [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]   ,				)
     __UpperCamelCase	:			Union[str, Any]					=					np.abs(image_slice.flatten() - expected_slice			).max()
     self.assertLessEqual(lowerCamelCase__   ,				1e-3			)







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

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

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

  @classmethod
  def    a					(    cls						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""			)

     __UpperCamelCase	:			Optional[int]					=					raw_image.convert("""RGB"""			).resize((7_68, 7_68)			)

     __UpperCamelCase	:			Optional[int]					=					raw_image

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					torch.manual_seed(0			)

     __UpperCamelCase	:			str					=					StableDiffusionDiffEditPipeline.from_pretrained(
         """stabilityai/stable-diffusion-2-1"""   ,				safety_checker=lowerCamelCase__   ,				torch_dtype=torch.floataa			)
     __UpperCamelCase	:			int					=					DDIMScheduler.from_config(pipe.scheduler.config			)
     __UpperCamelCase	:			List[str]					=					DDIMInverseScheduler.from_config(pipe.scheduler.config			)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Any					=					"""a bowl of fruit"""
     __UpperCamelCase	:			Any					=					"""a bowl of pears"""

     __UpperCamelCase	:			Dict					=					pipe.generate_mask(
         image=self.raw_image   ,				source_prompt=lowerCamelCase__   ,				target_prompt=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				)

     __UpperCamelCase	:			str					=					pipe.invert(
         prompt=lowerCamelCase__   ,				image=self.raw_image   ,				inpaint_strength=0.7   ,				generator=lowerCamelCase__			).latents

     __UpperCamelCase	:			Union[str, Any]					=					pipe(
         prompt=lowerCamelCase__   ,				mask_image=lowerCamelCase__   ,				image_latents=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				negative_prompt=lowerCamelCase__   ,				inpaint_strength=0.7   ,				output_type="""numpy"""   ,				).images[0]

     __UpperCamelCase	:			List[Any]					=					(
         np.array(
             load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/diffedit/pears.png"""			).resize((7_68, 7_68)			)			)
         / 2_55
     )
     assert np.abs((expected_image - image).max()			) < 5e-1

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					torch.manual_seed(0			)

     __UpperCamelCase	:			Optional[int]					=					StableDiffusionDiffEditPipeline.from_pretrained(
         """stabilityai/stable-diffusion-2-1"""   ,				safety_checker=lowerCamelCase__   ,				torch_dtype=torch.floataa			)
     __UpperCamelCase	:			Union[str, Any]					=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
     __UpperCamelCase	:			Union[str, Any]					=					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Dict					=					"""a bowl of fruit"""
     __UpperCamelCase	:			Union[str, Any]					=					"""a bowl of pears"""

     __UpperCamelCase	:			Tuple					=					pipe.generate_mask(
         image=self.raw_image   ,				source_prompt=lowerCamelCase__   ,				target_prompt=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				)

     __UpperCamelCase	:			List[Any]					=					pipe.invert(
         prompt=lowerCamelCase__   ,				image=self.raw_image   ,				inpaint_strength=0.7   ,				generator=lowerCamelCase__   ,				num_inference_steps=25   ,				).latents

     __UpperCamelCase	:			Union[str, Any]					=					pipe(
         prompt=lowerCamelCase__   ,				mask_image=lowerCamelCase__   ,				image_latents=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				negative_prompt=lowerCamelCase__   ,				inpaint_strength=0.7   ,				num_inference_steps=25   ,				output_type="""numpy"""   ,				).images[0]

     __UpperCamelCase	:			Optional[int]					=					(
         np.array(
             load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/diffedit/pears.png"""			).resize((7_68, 7_68)			)			)
         / 2_55
     )
     assert np.abs((expected_image - image).max()			) < 5e-1











UpperCamelCase    							=      8.3_144_598


def 				__lowerCamelCase     (							__lowerCAmelCase							:					float					,							__lowerCAmelCase							:					float							)     ->  float:
   if temperature < 0:
      raise Exception("""Temperature cannot be less than 0 K"""							)
   if molar_mass <= 0:
      raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
   else:
      return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
       import doctest

       # run doctest
       doctest.testmod()

       # example
       UpperCamelCase    							=      300
       UpperCamelCase    							=      28
       UpperCamelCase    							=      rms_speed_of_molecule(temperature, molar_mass)
       print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")



