
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 (      UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







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






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




           """simple docstring"""







           torch.manual_seed(0  )
           UpperCamelCase__    :   Optional[int]					=				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=__SCREAMING_SNAKE_CASE						,						)
           UpperCamelCase__    :   Tuple					=				DDIMScheduler(
               beta_start=0.00085						,						beta_end=0.012						,						beta_schedule='''scaled_linear'''						,						clip_sample=__SCREAMING_SNAKE_CASE						,						set_alpha_to_one=__SCREAMING_SNAKE_CASE						,						)
           UpperCamelCase__    :   Optional[int]					=				DDIMInverseScheduler(
               beta_start=0.00085						,						beta_end=0.012						,						beta_schedule='''scaled_linear'''						,						clip_sample=__SCREAMING_SNAKE_CASE						,						set_alpha_to_zero=__SCREAMING_SNAKE_CASE						,						)
           torch.manual_seed(0  )
           UpperCamelCase__    :   Dict					=				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  )
           UpperCamelCase__    :   Any					=				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						,						)
           UpperCamelCase__    :   List[Any]					=				CLIPTextModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

           UpperCamelCase__    :   List[str]					=				{
               '''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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=0  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				floats_tensor((1, 1_6, 1_6)						,						rng=random.Random(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				floats_tensor((1, 2, 4, 1_6, 1_6)						,						rng=random.Random(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )
           if str(__SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
                 UpperCamelCase__    :   Tuple					=				torch.manual_seed(__SCREAMING_SNAKE_CASE  )
           else:
                 UpperCamelCase__    :   Dict					=				torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				{
               '''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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=0  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				floats_tensor((1, 3, 3_2, 3_2)						,						rng=random.Random(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Any					=				image.cpu().permute(0						,						2						,						3						,						1  )[0]
           UpperCamelCase__    :   Union[str, Any]					=				Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE  )  ).convert('''RGB'''  )
           if str(__SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
                 UpperCamelCase__    :   int					=				torch.manual_seed(__SCREAMING_SNAKE_CASE  )
           else:
                 UpperCamelCase__    :   Optional[int]					=				torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=0  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				floats_tensor((1, 3, 3_2, 3_2)						,						rng=random.Random(__SCREAMING_SNAKE_CASE  )  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				image.cpu().permute(0						,						2						,						3						,						1  )[0]
           UpperCamelCase__    :   Any					=				Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE  )  ).convert('''RGB'''  )
           if str(__SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
                 UpperCamelCase__    :   int					=				torch.manual_seed(__SCREAMING_SNAKE_CASE  )
           else:
                 UpperCamelCase__    :   Union[str, Any]					=				torch.Generator(device=__SCREAMING_SNAKE_CASE  ).manual_seed(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







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

           UpperCamelCase__    :   Any					=				self.get_dummy_components()
           UpperCamelCase__    :   int					=				self.pipeline_class(**__SCREAMING_SNAKE_CASE  )
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

           UpperCamelCase__    :   int					=				self.get_dummy_inputs(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				pipe(**__SCREAMING_SNAKE_CASE  )[0]

           with tempfile.TemporaryDirectory() as tmpdir:
                 pipe.save_pretrained(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   str					=				self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 pipe_loaded.to(__SCREAMING_SNAKE_CASE  )
                 pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

           UpperCamelCase__    :   List[str]					=				self.get_dummy_inputs(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				pipe_loaded(**__SCREAMING_SNAKE_CASE  )[0]

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				'''cpu'''

           UpperCamelCase__    :   Any					=				self.get_dummy_components()
           UpperCamelCase__    :   Optional[int]					=				self.pipeline_class(**__SCREAMING_SNAKE_CASE  )
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				self.get_dummy_mask_inputs(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				pipe.generate_mask(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				mask[0, -3:, -3:]

           self.assertEqual(mask.shape						,						(1, 1_6, 1_6)  )
           UpperCamelCase__    :   int					=				np.array([0] * 9  )
           UpperCamelCase__    :   List[str]					=				np.abs(mask_slice.flatten() - expected_slice  ).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE						,						1e-3  )
           self.assertEqual(mask[0, -3, -4]						,						0  )






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




           """simple docstring"""







           UpperCamelCase__    :   int					=				'''cpu'''

           UpperCamelCase__    :   Any					=				self.get_dummy_components()
           UpperCamelCase__    :   Optional[int]					=				self.pipeline_class(**__SCREAMING_SNAKE_CASE  )
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

           self.assertEqual(image.shape						,						(2, 3_2, 3_2, 3)  )
           UpperCamelCase__    :   List[Any]					=				np.array(
               [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]						,						)
           UpperCamelCase__    :   Tuple					=				np.abs(image_slice.flatten() - expected_slice  ).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE						,						1e-3  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				'''cpu'''

           UpperCamelCase__    :   Tuple					=				self.get_dummy_components()

           UpperCamelCase__    :   List[str]					=				{'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
           UpperCamelCase__    :   Dict					=				DPMSolverMultistepScheduler(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				DPMSolverMultistepInverseScheduler(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				self.pipeline_class(**__SCREAMING_SNAKE_CASE  )
           pipe.to(__SCREAMING_SNAKE_CASE  )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

           self.assertEqual(image.shape						,						(2, 3_2, 3_2, 3)  )
           UpperCamelCase__    :   Any					=				np.array(
               [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]						,						)
           UpperCamelCase__    :   List[Any]					=				np.abs(image_slice.flatten() - expected_slice  ).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE						,						1e-3  )




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





     """simple docstring"""







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




           """simple docstring"""







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






     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							cls  )  ->   str:




           """simple docstring"""







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

           UpperCamelCase__    :   Optional[Any]					=				raw_image.convert('''RGB'''  ).resize((7_6_8, 7_6_8)  )

           UpperCamelCase__    :   Optional[Any]					=				raw_image






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




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				torch.manual_seed(0  )

           UpperCamelCase__    :   List[str]					=				StableDiffusionDiffEditPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2-1'''						,						safety_checker=__SCREAMING_SNAKE_CASE						,						torch_dtype=torch.floataa  )
           UpperCamelCase__    :   Tuple					=				DDIMScheduler.from_config(pipe.scheduler.config  )
           UpperCamelCase__    :   Any					=				DDIMInverseScheduler.from_config(pipe.scheduler.config  )
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]					=				'''a bowl of fruit'''
           UpperCamelCase__    :   str					=				'''a bowl of pears'''

           UpperCamelCase__    :   List[str]					=				pipe.generate_mask(
               image=self.raw_image						,						source_prompt=__SCREAMING_SNAKE_CASE						,						target_prompt=__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						)

           UpperCamelCase__    :   Any					=				pipe.invert(
               prompt=__SCREAMING_SNAKE_CASE						,						image=self.raw_image						,						inpaint_strength=0.7						,						generator=__SCREAMING_SNAKE_CASE  ).latents

           UpperCamelCase__    :   Any					=				pipe(
               prompt=__SCREAMING_SNAKE_CASE						,						mask_image=__SCREAMING_SNAKE_CASE						,						image_latents=__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						negative_prompt=__SCREAMING_SNAKE_CASE						,						inpaint_strength=0.7						,						output_type='''numpy'''						,						).images[0]

           UpperCamelCase__    :   List[str]					=				(
               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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				torch.manual_seed(0  )

           UpperCamelCase__    :   Optional[int]					=				StableDiffusionDiffEditPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2-1'''						,						safety_checker=__SCREAMING_SNAKE_CASE						,						torch_dtype=torch.floataa  )
           UpperCamelCase__    :   Tuple					=				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=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]					=				'''a bowl of fruit'''
           UpperCamelCase__    :   Optional[Any]					=				'''a bowl of pears'''

           UpperCamelCase__    :   Tuple					=				pipe.generate_mask(
               image=self.raw_image						,						source_prompt=__SCREAMING_SNAKE_CASE						,						target_prompt=__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						)

           UpperCamelCase__    :   Union[str, Any]					=				pipe.invert(
               prompt=__SCREAMING_SNAKE_CASE						,						image=self.raw_image						,						inpaint_strength=0.7						,						generator=__SCREAMING_SNAKE_CASE						,						num_inference_steps=2_5						,						).latents

           UpperCamelCase__    :   int					=				pipe(
               prompt=__SCREAMING_SNAKE_CASE						,						mask_image=__SCREAMING_SNAKE_CASE						,						image_latents=__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						negative_prompt=__SCREAMING_SNAKE_CASE						,						inpaint_strength=0.7						,						num_inference_steps=2_5						,						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_6_8, 7_6_8)  )  )
               / 2_5_5
           )
           assert np.abs((expected_image - image).max()  ) < 5e-1




import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    lowerCamelCase      =argparse.ArgumentParser()
    parser.add_argument("--user", type=str, default="ubuntu")
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--key_path", type=str, default=None)
    parser.add_argument("--instance", type=str, default="V100:1")
    parser.add_argument("--provider", type=str, default="cheapest")
    parser.add_argument("--use_spot", type=bool, default=False)
    parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
    lowerCamelCase   ,     lowerCamelCase      =parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError("Cannot specify both BYO and on-demand cluster args")
        lowerCamelCase      =rh.cluster(
            name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
        )
    else:
        lowerCamelCase      =rh.cluster(
            name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    lowerCamelCase      =args.example.rsplit("/", 1)[0]

    # Set up remote environment
    cluster.install_packages(["pip:./"])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
    cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([F'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)


