
'''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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  List[str]		)       ->  int:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				List[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 __lowercase       (       self   :  int		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				str      =       KarrasVeScheduler()

      lowerCAmelCase_      :				List[str]      =       KarrasVePipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Optional[Any]      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       pipe(num_inference_steps=2   ,						generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Tuple      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pipe(num_inference_steps=2   ,						generator=lowerCamelCase   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Dict      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				List[str]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[Any]      =       """google/ncsnpp-celebahq-256"""
      lowerCAmelCase_      :				str      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				List[Any]      =       KarrasVeScheduler()

      lowerCAmelCase_      :				Tuple      =       KarrasVePipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pipe(num_inference_steps=20   ,						generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      assert image.shape == (1, 2_56, 2_56, 3)
      lowerCAmelCase_      :				Union[str, Any]      =       np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]		)

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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A :  List[Any]    							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A :  int    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



