




'''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				:  List[Any] ,						_lowerCamelCase				:  List[Any]						)-> int:




     '''simple docstring'''





     __snake_case            =	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}}'''						)
     __snake_case            =	DatasetInfosDict.from_directory(_lowerCamelCase						)
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42







@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=42 ,						),
    ] ,						)
def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  DatasetInfo						)-> Any:




     '''simple docstring'''





     __snake_case            =	str(_lowerCamelCase						)
     dataset_info.write_to_directory(_lowerCamelCase						)
     __snake_case            =	DatasetInfo.from_directory(_lowerCamelCase						)
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(_lowerCamelCase ,						'''dataset_info.json'''						)						)







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




     '''simple docstring'''





     __snake_case            =	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''': 42}] ,						download_checksums={} ,						download_size=13_37 ,						post_processing_size=4_42 ,						dataset_size=12_34 ,						size_in_bytes=13_37 + 4_42 + 12_34 ,						)
     __snake_case            =	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)						)
     __snake_case            =	yaml.safe_dump(_lowerCamelCase						)
     __snake_case            =	yaml.safe_load(_lowerCamelCase						)
     assert dataset_info_yaml_dict == reloaded







def       _UpperCamelCase			()-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	DatasetInfo()
     __snake_case            =	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=42 ,						)
            }						),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42						),
                '''v2''': DatasetInfo(dataset_size=13_37						),
            }						),
    ] ,						)
def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  DatasetInfosDict						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	str(_lowerCamelCase						)
     dataset_infos_dict.write_to_directory(_lowerCamelCase						)
     __snake_case            =	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():
          __snake_case            =	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
          __snake_case            =	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


UpperCAmelCase_							:	Tuple	     =		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						):
          __snake_case            =	[image]

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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Union[List, PIL.Image.Image, torch.Tensor]						)-> Optional[Any]:




     '''simple docstring'''





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

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







class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   UNetaDModel
       __lowercase	:   RePaintScheduler


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




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




       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 250     ,	__SCREAMING_SNAKE_CASE = 0.0     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = 10     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = "pil"     ,	__SCREAMING_SNAKE_CASE = True     ,	)   ->	Union[ImagePipelineOutput, Tuple]:



            '''simple docstring'''




            __snake_case            =	image

            __snake_case            =	_preprocess_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	original_image.to(device=self.device     ,	dtype=self.unet.dtype	)
            __snake_case            =	_preprocess_mask(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	mask_image.to(device=self.device     ,	dtype=self.unet.dtype	)

            __snake_case            =	original_image.shape[0]

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

            __snake_case            =	original_image.shape
            __snake_case            =	randn_tensor(__SCREAMING_SNAKE_CASE     ,	generator=__SCREAMING_SNAKE_CASE     ,	device=self.device     ,	dtype=self.unet.dtype	)

            # set step values
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	self.device	)
            __snake_case            =	eta

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

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

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE	)






