






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 			snake_case__   (							lowercase_	,    lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				str 				= StableDiffusionDiffEditPipeline
       lowerCamelCase							:				Tuple 				= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
       lowerCamelCase							:				Union[str, Any] 				= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
       lowerCamelCase							:				List[Any] 				= frozenset(
           [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       lowerCamelCase							:				int 				= frozenset([])



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            torch.manual_seed(0    )
            __snake_case			:Optional[int]          =						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=a__			,						)
            __snake_case			:Optional[Any]          =						DDIMScheduler(
                beta_start=0.0_00_85			,						beta_end=0.0_12			,						beta_schedule="""scaled_linear"""			,						clip_sample=a__			,						set_alpha_to_one=a__			,						)
            __snake_case			:Dict          =						DDIMInverseScheduler(
                beta_start=0.0_00_85			,						beta_end=0.0_12			,						beta_schedule="""scaled_linear"""			,						clip_sample=a__			,						set_alpha_to_zero=a__			,						)
            torch.manual_seed(0    )
            __snake_case			: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			,						sample_size=1_28			,						)
            torch.manual_seed(0    )
            __snake_case			:Tuple          =						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			,						)
            __snake_case			:str          =						CLIPTextModel(a__    )
            __snake_case			:int          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

            __snake_case			:Tuple          =						{
                """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 					__lowercase						(							self			,						a__			,						a__=0    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						floats_tensor((1, 16, 16)			,						rng=random.Random(a__    )    ).to(a__    )
            __snake_case			:str          =						floats_tensor((1, 2, 4, 16, 16)			,						rng=random.Random(a__    )    ).to(a__    )
            if str(a__    ).startswith("""mps"""    ):
                 __snake_case			:str          =						torch.manual_seed(a__    )
            else:
                 __snake_case			:int          =						torch.Generator(device=a__    ).manual_seed(a__    )
            __snake_case			:List[Any]          =						{
                """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 					__lowercase						(							self			,						a__			,						a__=0    ) ->      int:






            '''simple docstring'''





            __snake_case			:List[Any]          =						floats_tensor((1, 3, 32, 32)			,						rng=random.Random(a__    )    ).to(a__    )
            __snake_case			:List[Any]          =						image.cpu().permute(0			,						2			,						3			,						1    )[0]
            __snake_case			:Any          =						Image.fromarray(np.uinta(a__    )    ).convert("""RGB"""    )
            if str(a__    ).startswith("""mps"""    ):
                 __snake_case			:List[str]          =						torch.manual_seed(a__    )
            else:
                 __snake_case			:Any          =						torch.Generator(device=a__    ).manual_seed(a__    )
            __snake_case			:Optional[Any]          =						{
                """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 					__lowercase						(							self			,						a__			,						a__=0    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						floats_tensor((1, 3, 32, 32)			,						rng=random.Random(a__    )    ).to(a__    )
            __snake_case			:int          =						image.cpu().permute(0			,						2			,						3			,						1    )[0]
            __snake_case			:Union[str, Any]          =						Image.fromarray(np.uinta(a__    )    ).convert("""RGB"""    )
            if str(a__    ).startswith("""mps"""    ):
                 __snake_case			:List[str]          =						torch.manual_seed(a__    )
            else:
                 __snake_case			:List[Any]          =						torch.Generator(device=a__    ).manual_seed(a__    )
            __snake_case			:Dict          =						{
                """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 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





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

            __snake_case			:str          =						self.get_dummy_components()
            __snake_case			:Tuple          =						self.pipeline_class(**a__    )
            pipe.to(a__    )
            pipe.set_progress_bar_config(disable=a__    )

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

            __snake_case			:Optional[int]          =						self.get_dummy_inputs(a__    )
            __snake_case			:List[str]          =						pipe(**a__    )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(a__    )
                 __snake_case			:Tuple          =						self.pipeline_class.from_pretrained(a__    )
                 pipe_loaded.to(a__    )
                 pipe_loaded.set_progress_bar_config(disable=a__    )

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

            __snake_case			:Optional[Any]          =						self.get_dummy_inputs(a__    )
            __snake_case			:List[str]          =						pipe_loaded(**a__    )[0]

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



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





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

            __snake_case			:str          =						self.get_dummy_components()
            __snake_case			:Optional[int]          =						self.pipeline_class(**a__    )
            pipe.to(a__    )
            pipe.set_progress_bar_config(disable=a__    )

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

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



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Dict          =						"""cpu"""

            __snake_case			:Union[str, Any]          =						self.get_dummy_components()
            __snake_case			:Tuple          =						self.pipeline_class(**a__    )
            pipe.to(a__    )
            pipe.set_progress_bar_config(disable=a__    )

            __snake_case			:Union[str, Any]          =						self.get_dummy_inversion_inputs(a__    )
            __snake_case			:int          =						pipe.invert(**a__    ).images
            __snake_case			:str          =						image[0, -1, -3:, -3:]

            self.assertEqual(image.shape			,						(2, 32, 32, 3)    )
            __snake_case			:str          =						np.array(
                [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]			,						)
            __snake_case			:str          =						np.abs(image_slice.flatten() - expected_slice    ).max()
            self.assertLessEqual(a__			,						1e-3    )



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Any          =						"""cpu"""

            __snake_case			:int          =						self.get_dummy_components()

            __snake_case			:Dict          =						{"""beta_start""": 0.0_00_85, """beta_end""": 0.0_12, """beta_schedule""": """scaled_linear"""}
            __snake_case			:Optional[Any]          =						DPMSolverMultistepScheduler(**a__    )
            __snake_case			:List[Any]          =						DPMSolverMultistepInverseScheduler(**a__    )

            __snake_case			:Tuple          =						self.pipeline_class(**a__    )
            pipe.to(a__    )
            pipe.set_progress_bar_config(disable=a__    )

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

            self.assertEqual(image.shape			,						(2, 32, 32, 3)    )
            __snake_case			:Union[str, Any]          =						np.array(
                [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]			,						)
            __snake_case			:Dict          =						np.abs(image_slice.flatten() - expected_slice    ).max()
            self.assertLessEqual(a__			,						1e-3    )




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

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





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



       @classmethod
       def 					__lowercase						(							cls    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""    )

            __snake_case			:Union[str, Any]          =						raw_image.convert("""RGB"""    ).resize((7_68, 7_68)    )

            __snake_case			:Dict          =						raw_image



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						torch.manual_seed(0    )

            __snake_case			:Dict          =						StableDiffusionDiffEditPipeline.from_pretrained(
                """stabilityai/stable-diffusion-2-1"""			,						safety_checker=a__			,						torch_dtype=torch.floataa    )
            __snake_case			:Optional[int]          =						DDIMScheduler.from_config(pipe.scheduler.config    )
            __snake_case			:Any          =						DDIMInverseScheduler.from_config(pipe.scheduler.config    )
            pipe.enable_model_cpu_offload()
            pipe.set_progress_bar_config(disable=a__    )

            __snake_case			:List[Any]          =						"""a bowl of fruit"""
            __snake_case			:Any          =						"""a bowl of pears"""

            __snake_case			:Any          =						pipe.generate_mask(
                image=self.raw_image			,						source_prompt=a__			,						target_prompt=a__			,						generator=a__			,						)

            __snake_case			:Union[str, Any]          =						pipe.invert(
                prompt=a__			,						image=self.raw_image			,						inpaint_strength=0.7			,						generator=a__    ).latents

            __snake_case			:Optional[int]          =						pipe(
                prompt=a__			,						mask_image=a__			,						image_latents=a__			,						generator=a__			,						negative_prompt=a__			,						inpaint_strength=0.7			,						output_type="""numpy"""			,						).images[0]

            __snake_case			:Tuple          =						(
                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 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						torch.manual_seed(0    )

            __snake_case			:List[str]          =						StableDiffusionDiffEditPipeline.from_pretrained(
                """stabilityai/stable-diffusion-2-1"""			,						safety_checker=a__			,						torch_dtype=torch.floataa    )
            __snake_case			:Dict          =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
            __snake_case			:str          =						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
            pipe.enable_model_cpu_offload()
            pipe.set_progress_bar_config(disable=a__    )

            __snake_case			:List[str]          =						"""a bowl of fruit"""
            __snake_case			:Tuple          =						"""a bowl of pears"""

            __snake_case			:List[Any]          =						pipe.generate_mask(
                image=self.raw_image			,						source_prompt=a__			,						target_prompt=a__			,						generator=a__			,						)

            __snake_case			:Dict          =						pipe.invert(
                prompt=a__			,						image=self.raw_image			,						inpaint_strength=0.7			,						generator=a__			,						num_inference_steps=25			,						).latents

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

            __snake_case			:Union[str, 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










from __future__ import annotations

import time

import numpy as np

lowerCamelCase__							=     [8, 5, 9, 7]
lowerCamelCase__							=     [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowerCamelCase__							=     [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__			,						) ->      None:






            '''simple docstring'''





            __snake_case			:Dict          =						claim_vector
            __snake_case			:Optional[int]          =						allocated_resources_table
            __snake_case			:Optional[int]          =						maximum_claim_table



       def 					__lowercase						(							self    ) ->      list[int]:






            '''simple docstring'''





            return [
                sum(p_item[i] for p_item in self.__allocated_resources_table    )
                for i in range(len(self.__allocated_resources_table[0]    )    )
            ]



       def 					__lowercase						(							self    ) ->      list[int]:






            '''simple docstring'''





            return np.array(self.__claim_vector    ) - np.array(
                self.__processes_resource_summation()    )



       def 					__lowercase						(							self    ) ->      list[list[int]]:






            '''simple docstring'''





            return [
                list(np.array(self.__maximum_claim_table[i]    ) - np.array(a__    )    )
                for i, allocated_resource in enumerate(self.__allocated_resources_table    )
            ]



       def 					__lowercase						(							self    ) ->      dict[int, list[int]]:






            '''simple docstring'''





            return {self.__need().index(a__    ): i for i in self.__need()}



       def 					__lowercase						(							self			,						**a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.__need()
            __snake_case			:List[Any]          =						self.__allocated_resources_table
            __snake_case			:str          =						self.__available_resources()
            __snake_case			:List[Any]          =						self.__need_index_manager()
            for kw, val in kwargs.items():
                 if kw and val is True:
                      self.__pretty_data()
            print("""_""" * 50 + """\n"""    )
            while need_list:
                 __snake_case			:Dict          =						False
                 for each_need in need_list:
                      __snake_case			:Dict          =						True
                      for index, need in enumerate(a__    ):
                           if need > available_resources[index]:
                                __snake_case			:Dict          =						False
                                break
                      if execution:
                           __snake_case			:Any          =						True
                           # get the original index of the process from ind_ctrl db
                           for original_need_index, need_clone in need_index_manager.items():
                                if each_need == need_clone:
                                     __snake_case			:List[str]          =						original_need_index
                           print(F'''Process {process_number + 1} is executing.'''    )
                           # remove the process run from stack
                           need_list.remove(a__    )
                           # update available/freed resources stack
                           __snake_case			:Tuple          =						np.array(a__    ) + np.array(
                               alloc_resources_table[process_number]    )
                           print(
                               """Updated available resource stack for processes: """
                               + """ """.join([str(a__    ) for x in available_resources]    )    )
                           break
                 if safe:
                      print("""The process is in a safe state.\n"""    )
                 else:
                      print("""System in unsafe state. Aborting...\n"""    )
                      break



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            print(""" """ * 9 + """Allocated Resource Table"""    )
            for item in self.__allocated_resources_table:
                 print(
                     F'''P{self.__allocated_resources_table.index(a__    ) + 1}'''
                     + """ """.join(F'''{it:>8}''' for it in item    )
                     + """\n"""    )
            print(""" """ * 9 + """System Resource Table"""    )
            for item in self.__maximum_claim_table:
                 print(
                     F'''P{self.__maximum_claim_table.index(a__    ) + 1}'''
                     + """ """.join(F'''{it:>8}''' for it in item    )
                     + """\n"""    )
            print(
                """Current Usage by Active Processes: """
                + """ """.join(str(a__    ) for x in self.__claim_vector    )    )
            print(
                """Initial Available Resources:       """
                + """ """.join(str(a__    ) for x in self.__available_resources()    )    )
            time.sleep(1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


