


import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard




@slow
@require_flax
class A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[Any]:
             """simple docstring"""




             super().tearDown()
             gc.collect()






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/sd2-inpaint/init_image.png'					)
             _lowerCamelCase			:						str            =load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'					)

             _lowerCamelCase			:						int            ='xvjiarui/stable-diffusion-2-inpainting'
             _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =FlaxStableDiffusionInpaintPipeline.from_pretrained(lowercase_     ,  safety_checker=lowercase_					)

             _lowerCamelCase			:						List[Any]            ='Face of a yellow cat, high resolution, sitting on a park bench'

             _lowerCamelCase			:						int            =jax.random.PRNGKey(0					)
             _lowerCamelCase			:						Optional[Any]            =50

             _lowerCamelCase			:						int            =jax.device_count()
             _lowerCamelCase			:						int            =num_samples * [prompt]
             _lowerCamelCase			:						List[Any]            =num_samples * [init_image]
             _lowerCamelCase			:						List[str]            =num_samples * [mask_image]
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =pipeline.prepare_inputs(lowercase_     ,  lowercase_     ,  lowercase_					)

             # shard inputs and rng
             _lowerCamelCase			:						List[str]            =replicate(lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =jax.random.split(lowercase_     ,  jax.device_count()					)
             _lowerCamelCase			:						Optional[int]            =shard(lowercase_					)
             _lowerCamelCase			:						int            =shard(lowercase_					)
             _lowerCamelCase			:						Dict            =shard(lowercase_					)

             _lowerCamelCase			:						Any            =pipeline(
                 lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  jit=lowercase_					)

             _lowerCamelCase			:						Dict            =output.images.reshape(lowercase_     ,  512     ,  512     ,  3					)

             _lowerCamelCase			:						Optional[Any]            =images[0, 253:256, 253:256, -1]

             _lowerCamelCase			:						Any            =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
             _lowerCamelCase			:						Optional[Any]            =jnp.array(
                 [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]					)
             print(F'''output_slice: {output_slice}'''					)
             assert jnp.abs(output_slice - expected_slice					).max() < 1E-2






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


