code: 
  
  
  """simple docstring"""
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      EulerAncestralDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionModelEditingPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  @skip_mps
  class  __snake_case      (lowerCamelCase ,		lowerCamelCase ,		lowerCamelCase ,		unittest.TestCase     ):
  							__a				   =					StableDiffusionModelEditingPipeline
  							__a				   =					TEXT_TO_IMAGE_PARAMS
  							__a				   =					TEXT_TO_IMAGE_BATCH_PARAMS
  							__a				   =					TEXT_TO_IMAGE_IMAGE_PARAMS
  							__a				   =					TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										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()
  										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=10_00    ,       )
  										__lowerCamelCase        =			CLIPTextModel(A_      )
  										__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      __a      (    self:						str    ,       A_:						Optional[int]    ,       A_:						str=0      ):
  										__lowerCamelCase        =			torch.manual_seed(A_      )
  										__lowerCamelCase        =			{
  										    """prompt""": """A field of roses""",
  										    """generator""": generator,
  										    # Setting height and width to None to prevent OOMs on CPU.
  										    """height""": None,
  										    """width""": None,
  										    """num_inference_steps""": 2,
  										    """guidance_scale""": 6.0,
  										    """output_type""": """numpy""",
  										}
  										return inputs
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  										__lowerCamelCase        =			self.get_dummy_components()
  										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
  										__lowerCamelCase        =			sd_pipe.to(A_      )
  										sd_pipe.set_progress_bar_config(disable=A_      )
  
  										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
  										__lowerCamelCase        =			sd_pipe(**A_      ).images
  										__lowerCamelCase        =			image[0, -3:, -3:, -1]
  										assert image.shape == (1, 64, 64, 3)
  
  										__lowerCamelCase        =			np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]      )
  
  										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  										__lowerCamelCase        =			self.get_dummy_components()
  										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
  										__lowerCamelCase        =			sd_pipe.to(A_      )
  										sd_pipe.set_progress_bar_config(disable=A_      )
  
  										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
  										__lowerCamelCase        =			"""french fries"""
  										__lowerCamelCase        =			sd_pipe(**A_    ,       negative_prompt=A_      )
  										__lowerCamelCase        =			output.images
  										__lowerCamelCase        =			image[0, -3:, -3:, -1]
  
  										assert image.shape == (1, 64, 64, 3)
  
  										__lowerCamelCase        =			np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]      )
  
  										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  
  
  							def      __a      (    self:						List[str]      ):
  										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  										__lowerCamelCase        =			self.get_dummy_components()
  										__lowerCamelCase        =			EulerAncestralDiscreteScheduler(
  										    beta_start=0.00_085    ,       beta_end=0.012    ,       beta_schedule="""scaled_linear"""      )
  										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
  										__lowerCamelCase        =			sd_pipe.to(A_      )
  										sd_pipe.set_progress_bar_config(disable=A_      )
  
  										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
  										__lowerCamelCase        =			sd_pipe(**A_      ).images
  										__lowerCamelCase        =			image[0, -3:, -3:, -1]
  
  										assert image.shape == (1, 64, 64, 3)
  
  										__lowerCamelCase        =			np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]      )
  
  										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
  
  
  
  
  							def      __a      (    self:						str      ):
  										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  										__lowerCamelCase        =			self.get_dummy_components()
  										__lowerCamelCase        =			PNDMScheduler()
  										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
  										__lowerCamelCase        =			sd_pipe.to(A_      )
  										sd_pipe.set_progress_bar_config(disable=A_      )
  
  										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
  										# the pipeline does not expect pndm so test if it raises error.
  										with self.assertRaises(A_      ):
  													__lowerCamelCase        =			sd_pipe(**A_      ).images
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										super().test_inference_batch_single_identical(expected_max_diff=5E-3      )
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										super().test_attention_slicing_forward_pass(expected_max_diff=5E-3      )
  
  
  
  
  @slow
  @require_torch_gpu
  class  __snake_case      (unittest.TestCase     ):
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										super().tearDown()
  										gc.collect()
  										torch.cuda.empty_cache()
  
  
  
  
  							def      __a      (    self:						Union[str, Any]    ,       A_:						Optional[Any]=0      ):
  										__lowerCamelCase        =			torch.manual_seed(A_      )
  										__lowerCamelCase        =			{
  										    """prompt""": """A field of roses""",
  										    """generator""": generator,
  										    """num_inference_steps""": 3,
  										    """guidance_scale""": 7.5,
  										    """output_type""": """numpy""",
  										}
  										return inputs
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			"""CompVis/stable-diffusion-v1-4"""
  										__lowerCamelCase        =			StableDiffusionModelEditingPipeline.from_pretrained(A_    ,       safety_checker=A_      )
  										pipe.to(A_      )
  										pipe.set_progress_bar_config(disable=A_      )
  										pipe.enable_attention_slicing()
  
  										__lowerCamelCase        =			self.get_inputs()
  										__lowerCamelCase        =			pipe(**A_      ).images
  										__lowerCamelCase        =			image[0, -3:, -3:, -1].flatten()
  
  										assert image.shape == (1, 5_12, 5_12, 3)
  
  										__lowerCamelCase        =			np.array(
  										    [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]      )
  
  										assert np.abs(expected_slice - image_slice      ).max() < 1E-2
  
  										# make sure image changes after editing
  										pipe.edit_model("""A pack of roses"""    ,       """A pack of blue roses"""      )
  
  										__lowerCamelCase        =			pipe(**A_      ).images
  										__lowerCamelCase        =			image[0, -3:, -3:, -1].flatten()
  
  										assert image.shape == (1, 5_12, 5_12, 3)
  
  										assert np.abs(expected_slice - image_slice      ).max() > 1E-1
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										torch.cuda.empty_cache()
  										torch.cuda.reset_max_memory_allocated()
  										torch.cuda.reset_peak_memory_stats()
  
  										__lowerCamelCase        =			"""CompVis/stable-diffusion-v1-4"""
  										__lowerCamelCase        =			DDIMScheduler.from_pretrained(A_    ,       subfolder="""scheduler"""      )
  										__lowerCamelCase        =			StableDiffusionModelEditingPipeline.from_pretrained(
  										    A_    ,       scheduler=A_    ,       safety_checker=A_      )
  										__lowerCamelCase        =			pipe.to(A_      )
  										pipe.set_progress_bar_config(disable=A_      )
  										pipe.enable_attention_slicing(1      )
  										pipe.enable_sequential_cpu_offload()
  
  										__lowerCamelCase        =			self.get_inputs()
  										__lowerCamelCase        =			pipe(**A_      )
  
  										__lowerCamelCase        =			torch.cuda.max_memory_allocated()
  										# make sure that less than 4.4 GB is allocated
  										assert mem_bytes < 4.4 * 10**9
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  
  
  
  class  __snake_case      (unittest.TestCase ,		lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			load_tool("""translation"""      )
  										self.tool.setup()
  										__lowerCamelCase        =			load_tool("""translation"""    ,       remote=A_      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.remote_tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			self.tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase        =			self.remote_tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										# There is a single output
  										if len(self.tool.outputs      ) == 1:
  													__lowerCamelCase        =			[outputs]
  
  										self.assertListEqual(output_types(A_      )    ,       self.tool.outputs      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  										for output, output_type in zip(A_    ,       self.tool.outputs      ):
  													__lowerCamelCase        =			AGENT_TYPE_MAPPING[output_type]
  													self.assertTrue(isinstance(A_    ,       A_      )      )
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  
  										__lowerCamelCase        =			[]
  
  										for _input, input_type in zip(A_    ,       self.tool.inputs      ):
  													if isinstance(A_    ,       A_      ):
  																_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
  													else:
  																_inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )
  
          # Should not raise an error
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  
  
  
  