



"""simple docstring"""






import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
lowerCamelCase				:     int								=  """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
lowerCamelCase				:     int								=  """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
lowerCamelCase				:     int								=  """"""

# TODO: Add the licence for the dataset here if you can find it
lowerCamelCase				:     Optional[Any]								=  """"""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
lowerCamelCase				:     List[str]								=  {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}




class    __snake_case(					datasets.GeneratorBasedBuilder       ):
    _A       = datasets.Version('''1.1.0'''       )

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    _A       = [
        datasets.BuilderConfig(name='''first_domain'''			,			version=__A			,			description='''This part of my dataset covers a first domain'''       ),
        datasets.BuilderConfig(name='''second_domain'''			,			version=__A			,			description='''This part of my dataset covers a second domain'''       ),
    ]

    _A       = '''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.




    def   A   (   self			):





      '''simple docstring'''



      if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
        _SCREAMING_SNAKE_CASE        =			datasets.Features(
            {
                '''sentence''': datasets.Value('''string'''			),
                '''option1''': datasets.Value('''string'''			),
                '''answer''': datasets.Value('''string'''			)
                # These are the features of your dataset like images, labels ...
            }			)
      else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
        _SCREAMING_SNAKE_CASE        =			datasets.Features(
            {
                '''sentence''': datasets.Value('''string'''			),
                '''option2''': datasets.Value('''string'''			),
                '''second_domain_answer''': datasets.Value('''string'''			)
                # These are the features of your dataset like images, labels ...
            }			)
      return datasets.DatasetInfo(
          # This is the description that will appear on the datasets page.
          description=_DESCRIPTION    ,							features=A_    ,							homepage=_HOMEPAGE    ,							license=_LICENSE    ,							citation=_CITATION    ,							)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			_URLS[self.config.name]
      _SCREAMING_SNAKE_CASE        =			dl_manager.download_and_extract(A_			)
      return [
          datasets.SplitGenerator(
              name=datasets.Split.TRAIN    ,							gen_kwargs={
                  '''filepath''': os.path.join(A_    ,							'''train.jsonl'''			),
                  '''split''': '''train''',
              }    ,							),
          datasets.SplitGenerator(
              name=datasets.Split.VALIDATION    ,							gen_kwargs={
                  '''filepath''': os.path.join(A_    ,							'''dev.jsonl'''			),
                  '''split''': '''dev''',
              }    ,							),
          datasets.SplitGenerator(
              name=datasets.Split.TEST    ,							gen_kwargs={
                  '''filepath''': os.path.join(A_    ,							'''test.jsonl'''			),
                  '''split''': '''test'''
              }    ,							),
      ]




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      with open(A_    ,							encoding='''utf-8'''			) as f:
        for key, row in enumerate(A_			):
          _SCREAMING_SNAKE_CASE        =			json.loads(A_			)
          if self.config.name == "first_domain":
            # Yields examples as (key, example) tuples
            yield key, {
                "sentence": data["sentence"],
                "option1": data["option1"],
                "answer": "" if split == "test" else data["answer"],
            }
          else:
            yield key, {
                "sentence": data["sentence"],
                "option2": data["option2"],
                "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
            }





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
      return model




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
