
"""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     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      List[Any]:






				"""simple docstring"""




				UpperCAmelCase									=					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}}' )
				UpperCAmelCase									=					DatasetInfosDict.from_directory(A_ )
				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     lowercase__						(					lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					int ) ->      int:






				"""simple docstring"""




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




def     lowercase__						(					) ->      Optional[Any]:






				"""simple docstring"""




				UpperCAmelCase									=					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=1_337 ,				post_processing_size=442 ,				dataset_size=1_234 ,				size_in_bytes=1_337 + 442 + 1_234 ,				)
				UpperCAmelCase									=					dataset_info._to_yaml_dict()
				assert sorted(A_ ) == 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) )
				UpperCAmelCase									=					yaml.safe_dump(A_ )
				UpperCAmelCase									=					yaml.safe_load(A_ )
				assert dataset_info_yaml_dict == reloaded




def     lowercase__						(					) ->      str:






				"""simple docstring"""




				UpperCAmelCase									=					DatasetInfo()
				UpperCAmelCase									=					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=1_337 ),
            } ),
    ] ,				)
def     lowercase__						(					lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					int ) ->      Any:






				"""simple docstring"""




				UpperCAmelCase									=					str(A_ )
				dataset_infos_dict.write_to_directory(A_ )
				UpperCAmelCase									=					DatasetInfosDict.from_directory(A_ )

				# the config_name of the dataset_infos_dict take over the attribute
				for config_name, dataset_info in dataset_infos_dict.items():
								UpperCAmelCase									=					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
								UpperCAmelCase									=					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(A_ ,				'README.md' ) )





"""simple docstring"""

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


SCREAMING_SNAKE_CASE_    					=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return image
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[image]

    UpperCAmelCase									=					[trans(img.convert('RGB' ) ) for img in image]
    UpperCAmelCase									=					torch.stack(lowerCAmelCase )
    return image







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          UpperCAmelCase									=					DDIMScheduler.from_config(scheduler.config							)

          self.register_modules(unet=lowercase_   ,       scheduler=lowercase_							)




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          if strength < 0 or strength > 1:
              raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          UpperCAmelCase									=					min(int(num_inference_steps * strength							)   ,       lowercase_							)

          UpperCAmelCase									=					max(num_inference_steps - init_timestep   ,       0							)
          UpperCAmelCase									=					self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          if not isinstance(lowercase_   ,       (torch.Tensor, PIL.Image.Image, list)							):
              raise ValueError(
                  F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_							)}"							)

          UpperCAmelCase									=					image.to(device=lowercase_   ,       dtype=lowercase_							)

          if isinstance(lowercase_   ,       lowercase_							) and len(lowercase_							) != batch_size:
              raise ValueError(
                  F"You have passed a list of generators of length {len(lowercase_							)}, but requested an effective batch"
                  F" size of {batch_size}. Make sure the batch size matches the length of the generators."							)

          UpperCAmelCase									=					init_latents.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)

          # get latents
          print('add noise to latents at timestep'   ,       lowercase_							)
          UpperCAmelCase									=					self.scheduler.add_noise(lowercase_   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					init_latents

          return latents




      @torch.no_grad()
      def __call__(							self   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

          # 3. set timesteps
          self.scheduler.set_timesteps(lowercase_   ,       device=self.device							)
          UpperCAmelCase			,							UpperCAmelCase									=					self.get_timesteps(lowercase_   ,       lowercase_   ,       self.device							)
          UpperCAmelCase									=					timesteps[:1].repeat(lowercase_							)

          # 4. Prepare latent variables
          UpperCAmelCase									=					self.prepare_latents(lowercase_   ,       lowercase_   ,       lowercase_   ,       self.unet.dtype   ,       self.device   ,       lowercase_							)
          UpperCAmelCase									=					latents

          # 5. Denoising loop
          for t in self.progress_bar(lowercase_							):
              # 1. predict noise model_output
              UpperCAmelCase									=					self.unet(lowercase_   ,       lowercase_							).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       eta=lowercase_   ,       use_clipped_model_output=lowercase_   ,       generator=lowercase_   ,       ).prev_sample

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

          if not return_dict:
              return (image, latent_timestep.item())

          return ImagePipelineOutput(images=lowercase_							)



