


'''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 						_snake_case    (   unittest.TestCase		):




					@property
					def  snake_case__  (					self):
								torch.manual_seed(0)
								UpperCAmelCase__     :    Union[str, Any]         =   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  snake_case__  (					self):
								UpperCAmelCase__     :    Any         =   self.dummy_uncond_unet
								UpperCAmelCase__     :    List[Any]         =   PNDMScheduler()

								UpperCAmelCase__     :    Optional[Any]         =   PNDMPipeline(unet=lowercase__							,							scheduler=lowercase__)
								pndm.to(lowercase__)
								pndm.set_progress_bar_config(disable=lowercase__)

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

								UpperCAmelCase__     :    List[Any]         =   torch.manual_seed(0)
								UpperCAmelCase__     :    Any         =   pndm(generator=lowercase__							,							num_inference_steps=20							,							output_type="""numpy"""							,							return_dict=lowercase__)[0]

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

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase__     :    List[Any]         =   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 						_snake_case    (   unittest.TestCase		):




					def  snake_case__  (					self):
								UpperCAmelCase__     :    Optional[Any]         =   """google/ddpm-cifar10-32"""

								UpperCAmelCase__     :    Union[str, Any]         =   UNetaDModel.from_pretrained(lowercase__)
								UpperCAmelCase__     :    Optional[int]         =   PNDMScheduler()

								UpperCAmelCase__     :    Optional[Any]         =   PNDMPipeline(unet=lowercase__							,							scheduler=lowercase__)
								pndm.to(lowercase__)
								pndm.set_progress_bar_config(disable=lowercase__)
								UpperCAmelCase__     :    List[str]         =   torch.manual_seed(0)
								UpperCAmelCase__     :    Any         =   pndm(generator=lowercase__							,							output_type="""numpy""").images

								UpperCAmelCase__     :    Optional[Any]         =   image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase__     :    str         =   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 ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase__											=	{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__											=	[
     '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPTNeoXJapaneseForCausalLM''',
     '''GPTNeoXJapaneseLayer''',
     '''GPTNeoXJapaneseModel''',
     '''GPTNeoXJapanesePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase__											=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

