"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase       (   unittest.TestCase			):







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Tuple         =					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 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.dummy_uncond_unet
       snake_case					:   str         =					PNDMScheduler()

       snake_case					:   List[Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Any         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		,			return_dict=UpperCamelCase__		)[0]

       snake_case					:   Optional[Any]         =					image[0, -3:, -3:, -1]
       snake_case					:   List[str]         =					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   int         =					np.array([1.0, 1.0, 0.0, 1.0, 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 _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					"google/ddpm-cifar10-32"

       snake_case					:   Optional[int]         =					UNetaDModel.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PNDMScheduler()

       snake_case					:   Union[str, Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)
       snake_case					:   int         =					torch.manual_seed(0		)
       snake_case					:   str         =					pndm(generator=UpperCamelCase__		,			output_type="numpy"		).images

       snake_case					:   Union[str, Any]         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   Optional[Any]         =					np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]		)

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









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case         =					{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					["""DistilBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DistilBertForMaskedLM""",
      """DistilBertForMultipleChoice""",
      """DistilBertForQuestionAnswering""",
      """DistilBertForSequenceClassification""",
      """DistilBertForTokenClassification""",
      """DistilBertModel""",
      """DistilBertPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDistilBertForMaskedLM""",
      """TFDistilBertForMultipleChoice""",
      """TFDistilBertForQuestionAnswering""",
      """TFDistilBertForSequenceClassification""",
      """TFDistilBertForTokenClassification""",
      """TFDistilBertMainLayer""",
      """TFDistilBertModel""",
      """TFDistilBertPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """FlaxDistilBertForMaskedLM""",
      """FlaxDistilBertForMultipleChoice""",
      """FlaxDistilBertForQuestionAnswering""",
      """FlaxDistilBertForSequenceClassification""",
      """FlaxDistilBertForTokenClassification""",
      """FlaxDistilBertModel""",
      """FlaxDistilBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

  __snake_case         =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





