# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
  raise ImportWarning(
      """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
  )

if version.parse(pyarrow.__version__).major < 8:
  raise ImportWarning(
      """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
      """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
  )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

SCREAMING_SNAKE_CASE :	List[Any]							     =		concatenate_datasets
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		DownloadConfig
SCREAMING_SNAKE_CASE :	Dict							     =		DownloadManager
SCREAMING_SNAKE_CASE :	Optional[Any]							     =		DownloadMode
SCREAMING_SNAKE_CASE :	List[str]							     =		DownloadConfig
SCREAMING_SNAKE_CASE :	Optional[int]							     =		DownloadMode
SCREAMING_SNAKE_CASE :	List[Any]							     =		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class    A_ (       unittest.TestCase ):

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   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

  @property
  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  VQModel(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=3     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							)
   return CLIPTextModel(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.dummy_uncond_unet
   __a       =  DDIMScheduler()
   __a       =  self.dummy_vq_model

   __a       =  LDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,							vqvae=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"       ).images

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"     ,							return_dict=__SCREAMING_SNAKE_CASE       )[0]

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

   assert image.shape == (1, 64, 64, 3)
   __a       =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance
@slow
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							output_type="numpy"       ).images

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

   assert image.shape == (1, 2_56, 2_56, 3)
   __a       =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance


