


import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class  UpperCamelCase__ ( __lowerCamelCase							,						unittest.TestCase			):
      a__   :     int								=						RePaintPipeline
      a__   :     int								=						IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      a__   :     Any								=						PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      a__   :     Optional[Any]								=						IMAGE_INPAINTING_BATCH_PARAMS
      a__   :     str								=						False






      def 	__lowercase(     self					:   Any   )  -> List[str]:
           torch.manual_seed(0   )
           torch.manual_seed(0   )
           UpperCamelCase__							:  Dict								=     UNetaDModel(
               block_out_channels=(32, 64),		layers_per_block=2,		sample_size=32,		in_channels=3,		out_channels=3,		down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),		up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),		)
           UpperCamelCase__							:  Tuple								=     RePaintScheduler()
           UpperCamelCase__							:  Optional[Any]								=     {'''unet''': unet, '''scheduler''': scheduler}
           return components






      def 	__lowercase(     self					:   Optional[int],		__lowerCamelCase					:   Tuple,		__lowerCamelCase					:   List[Any]=0   )  -> Dict:
           if str(__lowerCamelCase   ).startswith('''mps'''   ):
                UpperCamelCase__							:  Union[str, Any]								=     torch.manual_seed(__lowerCamelCase   )
           else:
                UpperCamelCase__							:  Dict								=     torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
           UpperCamelCase__							:  Optional[Any]								=     np.random.RandomState(__lowerCamelCase   ).standard_normal((1, 3, 32, 32)   )
           UpperCamelCase__							:  Optional[int]								=     torch.from_numpy(__lowerCamelCase   ).to(device=__lowerCamelCase,		dtype=torch.floataa   )
           UpperCamelCase__							:  str								=     (image > 0).to(device=__lowerCamelCase,		dtype=torch.floataa   )
           UpperCamelCase__							:  Any								=     {
               '''image''': image,
               '''mask_image''': mask,
               '''generator''': generator,
               '''num_inference_steps''': 5,
               '''eta''': 0.0,
               '''jump_length''': 2,
               '''jump_n_sample''': 2,
               '''output_type''': '''numpy''',
           }
           return inputs






      def 	__lowercase(     self					:   Optional[Any]   )  -> List[Any]:
           UpperCamelCase__							:  Optional[Any]								=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__							:  List[str]								=     self.get_dummy_components()
           UpperCamelCase__							:  Dict								=     RePaintPipeline(**__lowerCamelCase   )
           UpperCamelCase__							:  Any								=     sd_pipe.to(__lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

           UpperCamelCase__							:  int								=     self.get_dummy_inputs(__lowerCamelCase   )
           UpperCamelCase__							:  Tuple								=     sd_pipe(**__lowerCamelCase   ).images
           UpperCamelCase__							:  Optional[int]								=     image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           UpperCamelCase__							:  Optional[int]								=     np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3






      @skip_mps
      def 	__lowercase(     self					:   List[str]   )  -> Dict:
           return super().test_save_load_local()






      @unittest.skip('''non-deterministic pipeline'''   )
      def 	__lowercase(     self					:   Optional[int]   )  -> str:
           return super().test_inference_batch_single_identical()






      @skip_mps
      def 	__lowercase(     self					:   Tuple   )  -> str:
           return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def 	__lowercase(     self					:   Dict   )  -> Any:
           return super().test_save_load_optional_components()






      @skip_mps
      def 	__lowercase(     self					:   Tuple   )  -> int:
           return super().test_attention_slicing_forward_pass()

@nightly
@require_torch_gpu
class  UpperCamelCase__ ( unittest.TestCase			):






      def 	__lowercase(     self					:   Optional[Any]   )  -> Dict:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






      def 	__lowercase(     self					:   List[Any]   )  -> str:
           UpperCamelCase__							:  int								=     load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
               '''repaint/celeba_hq_256.png'''   )
           UpperCamelCase__							:  Optional[int]								=     load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''   )
           UpperCamelCase__							:  List[Any]								=     load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
               '''repaint/celeba_hq_256_result.npy'''   )

           UpperCamelCase__							:  str								=     '''google/ddpm-ema-celebahq-256'''
           UpperCamelCase__							:  Dict								=     UNetaDModel.from_pretrained(__lowerCamelCase   )
           UpperCamelCase__							:  Dict								=     RePaintScheduler.from_pretrained(__lowerCamelCase   )

           UpperCamelCase__							:  Optional[Any]								=     RePaintPipeline(unet=__lowerCamelCase,		scheduler=__lowerCamelCase   ).to(__lowerCamelCase   )
           repaint.set_progress_bar_config(disable=__lowerCamelCase   )
           repaint.enable_attention_slicing()

           UpperCamelCase__							:  List[str]								=     torch.manual_seed(0   )
           UpperCamelCase__							:  str								=     repaint(
               __lowerCamelCase,		__lowerCamelCase,		num_inference_steps=2_50,		eta=0.0,		jump_length=10,		jump_n_sample=10,		generator=__lowerCamelCase,		output_type='''np''',		)
           UpperCamelCase__							:  int								=     output.images[0]

           assert image.shape == (2_56, 2_56, 3)
           assert np.abs(expected_image - image   ).mean() < 1e-2








import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


_SCREAMING_SNAKE_CASE :					Optional[int]			=	2 << 20  # 2mb
_SCREAMING_SNAKE_CASE :					Optional[Any]			=	get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def 	__lowercase(     self					:   List[str]   )  -> List[Any]:
           UpperCamelCase__							:  int								=     self._options.get_all_options().get('''runner'''   )
           return runner in [None, "DirectRunner", "PortableRunner"]







def 						_lowercase       (     __lowerCamelCase			:					List[str]		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[str]=False			)  ->   Optional[int]:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     FileSystems
     if fs.exists(__lowerCamelCase			):
          if force_upload:
               logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'			)
          else:
               logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'			)
               return
     with fs.create(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''rb'''			) as local_file:
               UpperCamelCase__							:  List[Any]								=     local_file.read(__lowerCamelCase			)
               while chunk:
                    remote_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Union[str, Any]								=     local_file.read(__lowerCamelCase			)







def 						_lowercase       (     __lowerCamelCase			:					Any		,__lowerCamelCase			:					int		,__lowerCamelCase			:					str=False			)  ->   Tuple:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     FileSystems
     if os.path.exists(__lowerCamelCase			):
          if force_download:
               logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'			)
          else:
               logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'			)
               return
     with fs.open(__lowerCamelCase			) as remote_file:
          with open(__lowerCamelCase		,'''wb'''			) as local_file:
               UpperCamelCase__							:  Any								=     remote_file.read(__lowerCamelCase			)
               while chunk:
                    local_file.write(__lowerCamelCase			)
                    UpperCamelCase__							:  Dict								=     remote_file.read(__lowerCamelCase			)




