

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        lowerCAmelCase__  (					__magic_name__				,							__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )
             __A    =UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4) ,   layers_per_block=2 ,   sample_size=3_2 ,   in_channels=4 ,   out_channels=4 ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,   cross_attention_dim=3_2 ,   attention_head_dim=(2, 4) ,   use_linear_projection=lowercase__ ,   )
             __A    =DDIMScheduler(
                 beta_start=0.0_0085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   clip_sample=lowercase__ ,   set_alpha_to_one=lowercase__ ,   )
             __A    =DDIMInverseScheduler(
                 beta_start=0.0_0085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   clip_sample=lowercase__ ,   set_alpha_to_zero=lowercase__ ,   )
             torch.manual_seed(0     )
             __A    =AutoencoderKL(
                 block_out_channels=[3_2, 6_4] ,   in_channels=3 ,   out_channels=3 ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,   latent_channels=4 ,   sample_size=1_2_8 ,   )
             torch.manual_seed(0     )
             __A    =CLIPTextConfig(
                 bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=3_2 ,   intermediate_size=3_7 ,   layer_norm_eps=1E-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1_0_0_0 ,   hidden_act='''gelu''' ,   projection_dim=5_1_2 ,   )
             __A    =CLIPTextModel(lowercase__     )
             __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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =floats_tensor((1, 1_6, 1_6) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             __A    =floats_tensor((1, 2, 4, 1_6, 1_6) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             if str(lowercase__     ).startswith('''mps'''     ):
                   __A    =torch.manual_seed(lowercase__     )
             else:
                   __A    =torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
             __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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =floats_tensor((1, 3, 3_2, 3_2) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             __A    =image.cpu().permute(0 ,   2 ,   3 ,   1     )[0]
             __A    =Image.fromarray(np.uinta(lowercase__     )     ).convert('''RGB'''     )
             if str(lowercase__     ).startswith('''mps'''     ):
                   __A    =torch.manual_seed(lowercase__     )
             else:
                   __A    =torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
             __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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =floats_tensor((1, 3, 3_2, 3_2) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             __A    =image.cpu().permute(0 ,   2 ,   3 ,   1     )[0]
             __A    =Image.fromarray(np.uinta(lowercase__     )     ).convert('''RGB'''     )
             if str(lowercase__     ).startswith('''mps'''     ):
                   __A    =torch.manual_seed(lowercase__     )
             else:
                   __A    =torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
             __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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

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

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

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

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(lowercase__     )
                   __A    =self.pipeline_class.from_pretrained(lowercase__     )
                   pipe_loaded.to(lowercase__     )
                   pipe_loaded.set_progress_bar_config(disable=lowercase__     )

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

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

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''

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

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

             self.assertEqual(mask.shape ,   (1, 1_6, 1_6)     )
             __A    =np.array([0] * 9     )
             __A    =np.abs(mask_slice.flatten() - expected_slice     ).max()
             self.assertLessEqual(lowercase__ ,   1E-3     )
             self.assertEqual(mask[0, -3, -4] ,   0     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''

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

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

             self.assertEqual(image.shape ,   (2, 3_2, 3_2, 3)     )
             __A    =np.array(
                 [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,   )
             __A    =np.abs(image_slice.flatten() - expected_slice     ).max()
             self.assertLessEqual(lowercase__ ,   1E-3     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''

             __A    =self.get_dummy_components()

             __A    ={'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
             __A    =DPMSolverMultistepScheduler(**lowercase__     )
             __A    =DPMSolverMultistepInverseScheduler(**lowercase__     )

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

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

             self.assertEqual(image.shape ,   (2, 3_2, 3_2, 3)     )
             __A    =np.array(
                 [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] ,   )
             __A    =np.abs(image_slice.flatten() - expected_slice     ).max()
             self.assertLessEqual(lowercase__ ,   1E-3     )





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



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @classmethod
       def     __UpperCamelCase     (							cls     ):







             '''simple docstring'''



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

             __A    =raw_image.convert('''RGB'''     ).resize((7_6_8, 7_6_8)     )

             __A    =raw_image

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =torch.manual_seed(0     )

             __A    =StableDiffusionDiffEditPipeline.from_pretrained(
                 '''stabilityai/stable-diffusion-2-1''' ,   safety_checker=lowercase__ ,   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=lowercase__     )

             __A    ='''a bowl of fruit'''
             __A    ='''a bowl of pears'''

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

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

             __A    =pipe(
                 prompt=lowercase__ ,   mask_image=lowercase__ ,   image_latents=lowercase__ ,   generator=lowercase__ ,   negative_prompt=lowercase__ ,   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((7_6_8, 7_6_8)     )     )
                 / 2_5_5
             )
             assert np.abs((expected_image - image).max()     ) < 5E-1


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =torch.manual_seed(0     )

             __A    =StableDiffusionDiffEditPipeline.from_pretrained(
                 '''stabilityai/stable-diffusion-2-1''' ,   safety_checker=lowercase__ ,   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=lowercase__     )

             __A    ='''a bowl of fruit'''
             __A    ='''a bowl of pears'''

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

             __A    =pipe.invert(
                 prompt=lowercase__ ,   image=self.raw_image ,   inpaint_strength=0.7 ,   generator=lowercase__ ,   num_inference_steps=2_5 ,   ).latents

             __A    =pipe(
                 prompt=lowercase__ ,   mask_image=lowercase__ ,   image_latents=lowercase__ ,   generator=lowercase__ ,   negative_prompt=lowercase__ ,   inpaint_strength=0.7 ,   num_inference_steps=2_5 ,   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((7_6_8, 7_6_8)     )     )
                 / 2_5_5
             )
             assert np.abs((expected_image - image).max()     ) < 5E-1





from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowerCamelCase      :					Tuple					    =      {'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
   from .tokenization_byta import ByTaTokenizer
else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


