'''simple docstring'''





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_:					int  =namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Union[str, Any]      )   ->				int:


     '''simple docstring'''




     return (abs(source - target      ) / target) < 0.01






@pytest.mark.integration
def      lowerCAmelCase_    ( snake_case_	:			Optional[int]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       _TestCommandArgs(dataset=snake_case_ ,   all_configs=snake_case_ ,   save_infos=snake_case_      )
     UpperCAmelCase_	   =       TestCommand(*snake_case_      )
     test_command.run()
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   "README.md"      )
     assert os.path.exists(snake_case_      )
     UpperCAmelCase_	   =       DatasetInfosDict.from_directory(snake_case_      )
     UpperCAmelCase_	   =       DatasetInfosDict(
         {
             "default": DatasetInfo(
                 features=Features(
                     {
                         "tokens": Sequence(Value("string"      )      ),
                         "ner_tags": Sequence(
                             ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]      )      ),
                         "langs": Sequence(Value("string"      )      ),
                         "spans": Sequence(Value("string"      )      ),
                     }      ) ,   splits=[
                     {
                         "name": "train",
                         "num_bytes": 2_35_15_63,
                         "num_examples": 1_00_00,
                     },
                     {
                         "name": "validation",
                         "num_bytes": 23_84_18,
                         "num_examples": 10_00,
                     },
                 ] ,   download_size=3_94_06_80 ,   dataset_size=2_58_99_81 ,   )
         }      )
     assert dataset_infos.keys() == expected_dataset_infos.keys()
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          UpperCAmelCase_   ,			UpperCAmelCase_	   =       getattr(dataset_infos["default"] ,   snake_case_      ), getattr(expected_dataset_infos["default"] ,   snake_case_      )
          if key == "num_bytes":
               assert is_apercent_close(snake_case_ ,   snake_case_      )
          elif key == "splits":
               assert list(snake_case_      ) == list(snake_case_      )
               for split in result:
                    assert result[split].name == expected[split].name
                    assert result[split].num_examples == expected[split].num_examples
                    assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes      )
          else:
               result == expected





'''simple docstring'''





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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).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(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




