"""simple docstring"""






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    """files"""   ,						[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]   ,						)
def      UpperCAmelCase__			(							lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:Optional[int]						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      tmp_path_factory.mktemp("""dset_infos_dir"""						)
      if "full:README.md" in files:
            with open(dataset_infos_dir / """README.md"""   ,						"""w"""						) as f:
                  f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""						)
      if "empty:README.md" in files:
            with open(dataset_infos_dir / """README.md"""   ,						"""w"""						) as f:
                  f.write(""""""						)
    # we want to support dataset_infos.json for backward compatibility
      if "dataset_infos.json" in files:
            with open(dataset_infos_dir / """dataset_infos.json"""   ,						"""w"""						) as f:
                  f.write("""{\"default\": {\"dataset_size\": 42}}"""						)
      lowercase								=      DatasetInfosDict.from_directory(lowerCAmelCase__						)
      assert dataset_infos
      assert dataset_infos["default"].dataset_size == 4_2


@pytest.mark.parametrize(
    """dataset_info"""   ,						[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""   ,						features=Features({"""a""": Value("""int32"""						)}						)   ,						builder_name="""builder"""   ,						config_name="""config"""   ,						version="""1.0.0"""   ,						splits=[{"""name""": """train"""}]   ,						download_size=4_2   ,						),
    ]   ,						)
def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:DatasetInfo						)					->       List[Any]:
      '''simple docstring'''


      lowercase								=      str(lowerCAmelCase__						)
      dataset_info.write_to_directory(lowerCAmelCase__						)
      lowercase								=      DatasetInfo.from_directory(lowerCAmelCase__						)
      assert dataset_info == reloaded
      assert os.path.exists(os.path.join(lowerCAmelCase__   ,						"""dataset_info.json"""						)						)


def      UpperCAmelCase__			(							)					->       Dict:
      '''simple docstring'''


      lowercase								=      DatasetInfo(
          description="""foo"""   ,						citation="""bar"""   ,						homepage="""https://foo.bar"""   ,						license="""CC0"""   ,						features=Features({"""a""": Value("""int32"""						)}						)   ,						post_processed={}   ,						supervised_keys=()   ,						task_templates=[]   ,						builder_name="""builder"""   ,						config_name="""config"""   ,						version="""1.0.0"""   ,						splits=[{"""name""": """train""", """num_examples""": 4_2}]   ,						download_checksums={}   ,						download_size=1_3_3_7   ,						post_processing_size=4_4_2   ,						dataset_size=1_2_3_4   ,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4   ,						)
      lowercase								=      dataset_info._to_yaml_dict()
      assert sorted(lowerCAmelCase__						) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML						)
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            assert key in dataset_info_yaml_dict
            assert isinstance(dataset_info_yaml_dict[key]   ,						(list, dict, int, str)						)
      lowercase								=      yaml.safe_dump(lowerCAmelCase__						)
      lowercase								=      yaml.safe_load(lowerCAmelCase__						)
      assert dataset_info_yaml_dict == reloaded


def      UpperCAmelCase__			(							)					->       Optional[int]:
      '''simple docstring'''


      lowercase								=      DatasetInfo()
      lowercase								=      dataset_info._to_yaml_dict()
      assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    """dataset_infos_dict"""   ,						[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}						),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}						),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""   ,						features=Features({"""a""": Value("""int32"""						)}						)   ,						builder_name="""builder"""   ,						config_name="""config"""   ,						version="""1.0.0"""   ,						splits=[{"""name""": """train"""}]   ,						download_size=4_2   ,						)
            }						),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=4_2						),
                """v2""": DatasetInfo(dataset_size=1_3_3_7						),
            }						),
    ]   ,						)
def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:DatasetInfosDict						)					->       Dict:
      '''simple docstring'''


      lowercase								=      str(lowerCAmelCase__						)
      dataset_infos_dict.write_to_directory(lowerCAmelCase__						)
      lowercase								=      DatasetInfosDict.from_directory(lowerCAmelCase__						)

      # the config_name of the dataset_infos_dict take over the attribute
      for config_name, dataset_info in dataset_infos_dict.items():
            lowercase								=      config_name
            # the yaml representation doesn't include fields like description or citation
            # so we just test that we can recover what we can from the yaml
            lowercase								=      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()						)
      assert dataset_infos_dict == reloaded

      if dataset_infos_dict:
            assert os.path.exists(os.path.join(lowerCAmelCase__   ,						"""README.md"""						)						)




"""simple docstring"""






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase       :			Optional[Any]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Optional[Any]:
      '''simple docstring'''


      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""   ,						lowerCAmelCase__   ,						)
      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return image
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [image]

      if isinstance(image[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      image[0].size
            lowercase				,				lowercase								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            lowercase								=      [np.array(i.resize((w, h)   ,						resample=PIL_INTERPOLATION["""lanczos"""]						)						)[None, :] for i in image]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      np.array(lowerCAmelCase__						).astype(np.floataa						) / 255.0
            lowercase								=      image.transpose(0   ,						3   ,						1   ,						2						)
            lowercase								=      2.0 * image - 1.0
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(image[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return image


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Dict:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return mask
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [mask]

      if isinstance(mask[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      mask[0].size
            lowercase				,				lowercase								=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            lowercase								=      [np.array(m.convert("""L"""						).resize((w, h)   ,						resample=PIL_INTERPOLATION["""nearest"""]						)						)[None, :] for m in mask]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      mask.astype(np.floataa						) / 255.0
            lowercase								=      0
            lowercase								=      1
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(mask[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return mask






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  UNetaDModel
      snake_case__     :  RePaintScheduler

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            self.register_modules(unet=__lowerCAmelCase						,			scheduler=__lowerCAmelCase		)







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 250						,			__lowerCAmelCase = 0.0						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			):







            """simple docstring"""



            lowercase								=      image

            lowercase								=      _preprocess_image(__lowerCAmelCase		)
            lowercase								=      original_image.to(device=self.device						,			dtype=self.unet.dtype		)
            lowercase								=      _preprocess_mask(__lowerCAmelCase		)
            lowercase								=      mask_image.to(device=self.device						,			dtype=self.unet.dtype		)

            lowercase								=      original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) and len(__lowerCAmelCase		) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(__lowerCAmelCase		)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

            lowercase								=      original_image.shape
            lowercase								=      randn_tensor(__lowerCAmelCase						,			generator=__lowerCAmelCase						,			device=self.device						,			dtype=self.unet.dtype		)

            # set step values
            self.scheduler.set_timesteps(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			self.device		)
            lowercase								=      eta

            lowercase								=      self.scheduler.timesteps[0] + 1
            lowercase								=      generator[0] if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
                  if t < t_last:
                        # predict the noise residual
                        lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample
                        # compute previous image: x_t -> x_t-1
                        lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

                  else:
                        # compute the reverse: x_t-1 -> x_t
                        lowercase								=      self.scheduler.undo_step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      t

            lowercase								=      (image / 2 + 0.5).clamp(0						,			1		)
            lowercase								=      image.cpu().permute(0						,			2						,			3						,			1		).numpy()
            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



