


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     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      StableDiffusionDiffEditPipeline
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
     _lowerCamelCase   =      frozenset(
         []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _lowerCamelCase   =      frozenset([]							)


     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  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  ,       )
        __a					=  DDIMScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_one=__A  ,       )
        __a					=  DDIMInverseScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_zero=__A  ,       )
        torch.manual_seed(0    )
        __a					=  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=128  ,       )
        torch.manual_seed(0    )
        __a					=  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=1000  ,       hidden_act="""gelu"""  ,       projection_dim=512  ,       )
        __a					=  CLIPTextModel(__A    )
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

        __a					=  {
            """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 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        __a					=  floats_tensor((1, 16, 16)  ,       rng=random.Random(__A    )    ).to(__A    )
        __a					=  floats_tensor((1, 2, 4, 16, 16)  ,       rng=random.Random(__A    )    ).to(__A    )
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )
        __a					=  {
            """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 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        __a					=  floats_tensor((1, 3, 32, 32)  ,       rng=random.Random(__A    )    ).to(__A    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__A    )    ).convert("""RGB"""    )
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )
        __a					=  {
            """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 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        __a					=  floats_tensor((1, 3, 32, 32)  ,       rng=random.Random(__A    )    ).to(__A    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__A    )    ).convert("""RGB"""    )
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )
        __a					=  {
            """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 	snake_case_  (	self    ):
        if not hasattr(self.pipeline_class  ,       """_optional_components"""    ):
           return

        __a					=  self.get_dummy_components()
        __a					=  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}    )

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  pipe(**__A    )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(__A    )
           __a					=  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.'''  ,       )

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  pipe_loaded(**__A    )[0]

        __a					=  np.abs(output - output_loaded    ).max()
        self.assertLess(__A  ,       1E-4    )


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""

        __a					=  self.get_dummy_components()
        __a					=  self.pipeline_class(**__A    )
        pipe.to(__A    )
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_dummy_mask_inputs(__A    )
        __a					=  pipe.generate_mask(**__A    )
        __a					=  mask[0, -3:, -3:]

        self.assertEqual(mask.shape  ,       (1, 16, 16)    )
        __a					=  np.array([0] * 9    )
        __a					=  np.abs(mask_slice.flatten() - expected_slice    ).max()
        self.assertLessEqual(__A  ,       1E-3    )
        self.assertEqual(mask[0, -3, -4]  ,       0    )


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""

        __a					=  self.get_dummy_components()
        __a					=  self.pipeline_class(**__A    )
        pipe.to(__A    )
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_dummy_inversion_inputs(__A    )
        __a					=  pipe.invert(**__A    ).images
        __a					=  image[0, -1, -3:, -3:]

        self.assertEqual(image.shape  ,       (2, 32, 32, 3)    )
        __a					=  np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]  ,       )
        __a					=  np.abs(image_slice.flatten() - expected_slice    ).max()
        self.assertLessEqual(__A  ,       1E-3    )


     def 	snake_case_  (	self    ):
        super().test_inference_batch_single_identical(expected_max_diff=5E-3    )


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""

        __a					=  self.get_dummy_components()

        __a					=  {"""beta_start""": 0.00085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
        __a					=  DPMSolverMultistepScheduler(**__A    )
        __a					=  DPMSolverMultistepInverseScheduler(**__A    )

        __a					=  self.pipeline_class(**__A    )
        pipe.to(__A    )
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  self.get_dummy_inversion_inputs(__A    )
        __a					=  pipe.invert(**__A    ).images
        __a					=  image[0, -1, -3:, -3:]

        self.assertEqual(image.shape  ,       (2, 32, 32, 3)    )
        __a					=  np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]  ,       )
        __a					=  np.abs(image_slice.flatten() - expected_slice    ).max()
        self.assertLessEqual(__A  ,       1E-3    )







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


     """simple docstring"""


     def 	snake_case_  (	self    ):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


     @classmethod
     def 	snake_case_  (	cls    ):
        __a					=  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""    )

        __a					=  raw_image.convert("""RGB"""    ).resize((768, 768)    )

        __a					=  raw_image


     def 	snake_case_  (	self    ):
        __a					=  torch.manual_seed(0    )

        __a					=  StableDiffusionDiffEditPipeline.from_pretrained(
            """stabilityai/stable-diffusion-2-1"""  ,       safety_checker=__A  ,       torch_dtype=torch.floataa    )
        __a					=  DDIMScheduler.from_config(pipe.scheduler.config    )
        __a					=  DDIMInverseScheduler.from_config(pipe.scheduler.config    )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  """a bowl of fruit"""
        __a					=  """a bowl of pears"""

        __a					=  pipe.generate_mask(
            image=self.raw_image  ,       source_prompt=__A  ,       target_prompt=__A  ,       generator=__A  ,       )

        __a					=  pipe.invert(
            prompt=__A  ,       image=self.raw_image  ,       inpaint_strength=0.7  ,       generator=__A    ).latents

        __a					=  pipe(
            prompt=__A  ,       mask_image=__A  ,       image_latents=__A  ,       generator=__A  ,       negative_prompt=__A  ,       inpaint_strength=0.7  ,       output_type="""numpy"""  ,       ).images[0]

        __a					=  (
            np.array(
                load_image(
                    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                    """/diffedit/pears.png"""    ).resize((768, 768)    )    )
            / 255
        )
        assert np.abs((expected_image - image).max()    ) < 5E-1


     def 	snake_case_  (	self    ):
        __a					=  torch.manual_seed(0    )

        __a					=  StableDiffusionDiffEditPipeline.from_pretrained(
            """stabilityai/stable-diffusion-2-1"""  ,       safety_checker=__A  ,       torch_dtype=torch.floataa    )
        __a					=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
        __a					=  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  """a bowl of fruit"""
        __a					=  """a bowl of pears"""

        __a					=  pipe.generate_mask(
            image=self.raw_image  ,       source_prompt=__A  ,       target_prompt=__A  ,       generator=__A  ,       )

        __a					=  pipe.invert(
            prompt=__A  ,       image=self.raw_image  ,       inpaint_strength=0.7  ,       generator=__A  ,       num_inference_steps=25  ,       ).latents

        __a					=  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]

        __a					=  (
            np.array(
                load_image(
                    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                    """/diffedit/pears.png"""    ).resize((768, 768)    )    )
            / 255
        )
        assert np.abs((expected_image - image).max()    ) < 5E-1





"""simple docstring"""






# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 				a__					:



     def __init__(							self :List[str]      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :bool = True      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=scheduler
      UpperCamelCase_     :					Any   	=optimizers if isinstance(_lowerCamelCase      ,  (list, tuple)   ) else [optimizers]
      UpperCamelCase_     :					Dict   	=split_batches
      UpperCamelCase_     :					str   	=step_with_optimizer
      UpperCamelCase_     :					Dict   	=GradientState()



     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      if not self.step_with_optimizer:
       # No link between scheduler and optimizer -> just step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
       return

      # Otherwise, first make sure the optimizer was stepped.
      if not self.gradient_state.sync_gradients:
       if self.gradient_state.adjust_scheduler:
        self.scheduler._step_count += 1
       return

      for opt in self.optimizers:
       if opt.step_was_skipped:
        return
      if self.split_batches:
       # Split batches -> the training dataloader batch size is not changed so one step per training step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
      else:
       # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
       # num_processes steps per training step
       UpperCamelCase_     :					Optional[Any]   	=AcceleratorState().num_processes
       for _ in range(_lowerCamelCase   ):
        # Special case when using OneCycle and `drop_last` was not used
        if hasattr(self.scheduler      ,  'total_steps'   ):
         if self.scheduler._step_count <= self.scheduler.total_steps:
          self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
        else:
         self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      return self.scheduler.get_last_lr()



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      return self.scheduler.state_dict()



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      self.scheduler.load_state_dict(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      return self.scheduler.get_lr()




     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :Union[str, Any]      ,  **_lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      return self.scheduler.print_lr(*_lowerCamelCase      ,  **_lowerCamelCase   )
