
"""simple docstring"""




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A     :			int  						=			logging.get_logger(__name__)


_A     :			Optional[Any]  						=			TypeVar("""DatasetType""", Dataset, IterableDataset)


def     __magic_name__      ( __snake_case							:			List[DatasetType]	,					__snake_case							:			Optional[List[float]] = None	,					__snake_case							:			Optional[int] = None	,					__snake_case							:			Optional[DatasetInfo] = None	,					__snake_case							:			Optional[NamedSplit] = None	,					__snake_case							:			Literal["first_exhausted", "all_exhausted"] = "first_exhausted"	,					)					->							DatasetType:
       from .arrow_dataset import Dataset
       from .iterable_dataset import IterableDataset

       if not datasets:
              raise ValueError("Unable to interleave an empty list of datasets."    )
       for i, dataset in enumerate(__snake_case    ):
              if not isinstance(__snake_case	,					(Dataset, IterableDataset)    ):
                     if isinstance(__snake_case	,					(DatasetDict, IterableDatasetDict)    ):
                            if not dataset:
                                   raise ValueError(
                                       f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                       "is an empty dataset dictionary."    )
                            raise ValueError(
                                f"""Dataset at position {i} has at least one split: {list(__snake_case    )}\n"""
                                f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__snake_case    )    )}']"""    )
                     raise ValueError(
                         f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__snake_case    ).__name__}."""    )
              if i == 0:
                     lowercase						,       lowercase       :							Dict									=				(
                         (Dataset, IterableDataset) if isinstance(__snake_case	,					__snake_case    ) else (IterableDataset, Dataset)
                     )
              elif not isinstance(__snake_case	,					__snake_case    ):
                     raise ValueError(
                         f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""    )
       if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
              raise ValueError(f"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""    )
       if dataset_type is Dataset:
              return _interleave_map_style_datasets(
                  __snake_case	,					__snake_case	,					__snake_case	,					info=__snake_case	,					split=__snake_case	,					stopping_strategy=__snake_case    )
       else:
              return _interleave_iterable_datasets(
                  __snake_case	,					__snake_case	,					__snake_case	,					info=__snake_case	,					split=__snake_case	,					stopping_strategy=__snake_case    )


def     __magic_name__      ( __snake_case							:			List[DatasetType]	,					__snake_case							:			Optional[DatasetInfo] = None	,					__snake_case							:			Optional[NamedSplit] = None	,					__snake_case							:			int = 0	,					)					->							DatasetType:

       if not dsets:
              raise ValueError("Unable to concatenate an empty list of datasets."    )
       for i, dataset in enumerate(__snake_case    ):
              if not isinstance(__snake_case	,					(Dataset, IterableDataset)    ):
                     if isinstance(__snake_case	,					(DatasetDict, IterableDatasetDict)    ):
                            if not dataset:
                                   raise ValueError(
                                       f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                       "is an empty dataset dictionary."    )
                            raise ValueError(
                                f"""Dataset at position {i} has at least one split: {list(__snake_case    )}\n"""
                                f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__snake_case    )    )}']"""    )
                     raise ValueError(
                         f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__snake_case    ).__name__}."""    )
              if i == 0:
                     lowercase						,       lowercase       :							int									=				(
                         (Dataset, IterableDataset) if isinstance(__snake_case	,					__snake_case    ) else (IterableDataset, Dataset)
                     )
              elif not isinstance(__snake_case	,					__snake_case    ):
                     raise ValueError(
                         f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""    )
       if dataset_type is Dataset:
              return _concatenate_map_style_datasets(__snake_case	,					info=__snake_case	,					split=__snake_case	,					axis=__snake_case    )
       else:
              return _concatenate_iterable_datasets(__snake_case	,					info=__snake_case	,					split=__snake_case	,					axis=__snake_case    )




"""simple docstring"""




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 						a__		( unittest.TestCase		):
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				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      __magic_name__     (     self							):
          lowercase       :							List[str]									=				self.dummy_uncond_unet
          lowercase       :							Dict									=				KarrasVeScheduler()

          lowercase       :							Any									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							int									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"      ,       return_dict=_a							)[0]

          lowercase       :							Any									=				image[0, -3:, -3:, -1]
          lowercase       :							Dict									=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase       :							Tuple									=				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 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"google/ncsnpp-celebahq-256"
          lowercase       :							str									=				UNetaDModel.from_pretrained(_a							)
          lowercase       :							Optional[Any]									=				KarrasVeScheduler()

          lowercase       :							Union[str, Any]									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Dict									=				pipe(num_inference_steps=20      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							Optional[Any]									=				image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          lowercase       :							Optional[Any]									=				np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]							)

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


