




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->List[Any]:





           '''simple docstring'''

           torch.manual_seed(0)
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->int:





           '''simple docstring'''

           A__ 					=       self.dummy_uncond_unet
           A__ 					=       KarrasVeScheduler()

           A__ 					=       KarrasVePipeline(unet=UpperCAmelCase__   ,					scheduler=UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       torch.manual_seed(0)
           A__ 					=       pipe(num_inference_steps=2   ,					generator=UpperCAmelCase__   ,					output_type='''numpy''').images

           A__ 					=       torch.manual_seed(0)
           A__ 					=       pipe(num_inference_steps=2   ,					generator=UpperCAmelCase__   ,					output_type='''numpy'''   ,					return_dict=UpperCAmelCase__)[0]

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

           assert image.shape == (1, 32, 32, 3)
           A__ 					=       np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

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


@slow
@require_torch
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->List[str]:





           '''simple docstring'''

           A__ 					=       '''google/ncsnpp-celebahq-256'''
           A__ 					=       UNetaDModel.from_pretrained(UpperCAmelCase__)
           A__ 					=       KarrasVeScheduler()

           A__ 					=       KarrasVePipeline(unet=UpperCAmelCase__   ,					scheduler=UpperCAmelCase__)
           pipe.to(UpperCAmelCase__)
           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__ 					=       torch.manual_seed(0)
           A__ 					=       pipe(num_inference_steps=20   ,					generator=UpperCAmelCase__   ,					output_type='''numpy''').images

           A__ 					=       image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           A__ 					=       np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])

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







'''simple docstring'''





import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__a  			=						[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def 		__snake_case(							_lowerCAmelCase=True						)						->					Dict:
      if with_config:
            return [
                {
                    "testcase_name": d["dataset"] + "/" + d["config_name"],
                    "dataset": d["dataset"],
                    "config_name": d["config_name"],
                }
                for d in DATASETS_ON_HF_GCP
            ]
      else:
            return [
                {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
            ]







@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_a      )      )
class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						None
    lowercase   =						None




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	Optional[int]		):
          with TemporaryDirectory() as tmp_dir:
                snake_case__    :       Dict       		= dataset_module_factory(snake_case_					,	cache_dir=snake_case_		)

                snake_case__    :       Optional[int]       		= import_main_class(dataset_module.module_path					,	dataset=snake_case_		)

                snake_case__    :       DatasetBuilder       		= builder_cls(
                    cache_dir=snake_case_					,	config_name=snake_case_					,	hash=dataset_module.hash					,	)

                snake_case__    :       Dict       		= """/""".join(
                    [
                        HF_GCP_BASE_URL,
                        builder_instance._relative_data_dir(with_hash=snake_case_		).replace(os.sep					,	"""/"""		),
                        config.DATASET_INFO_FILENAME,
                    ]		)
                snake_case__    :       List[str]       		= cached_path(snake_case_					,	cache_dir=snake_case_		)
                self.assertTrue(os.path.exists(snake_case_		)		)





@pytest.mark.integration
def 		__snake_case(							_lowerCAmelCase						)						->					Tuple:
      snake_case__    :       Optional[int]       		= tmp_path_factory.mktemp("""test_hf_gcp"""						) / """test_wikipedia_simple"""
      snake_case__    :       Dict       		= dataset_module_factory("""wikipedia"""	,  cache_dir=_lowerCAmelCase						)
      snake_case__    :       Dict       		= import_main_class(dataset_module.module_path						)
      snake_case__    :       DatasetBuilder       		= builder_cls(
          cache_dir=_lowerCAmelCase	,  config_name="""20220301.frr"""	,  hash=dataset_module.hash	,  )
      # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
      snake_case__    :       Any       		= None
      builder_instance.download_and_prepare()
      snake_case__    :       Union[str, Any]       		= builder_instance.as_dataset()
      assert ds





@pytest.mark.integration
def 		__snake_case(							_lowerCAmelCase						)						->					List[str]:
      snake_case__    :       Optional[int]       		= dataset_module_factory("""wikipedia"""	,  cache_dir=_lowerCAmelCase						)
      snake_case__    :       List[str]       		= import_main_class(dataset_module.module_path	,  dataset=_lowerCAmelCase						)
      snake_case__    :       DatasetBuilder       		= builder_cls(
          cache_dir=_lowerCAmelCase	,  config_name="""20220301.frr"""	,  hash=dataset_module.hash	,  )
      snake_case__    :       Any       		= builder_instance.as_streaming_dataset()
      assert ds
      assert isinstance(_lowerCAmelCase	,  _lowerCAmelCase						)
      assert "train" in ds
      assert isinstance(ds["""train"""]	,  _lowerCAmelCase						)
      assert next(iter(ds["""train"""]						)						)
