



"""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				):






					'''simple docstring'''





					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Union[str, Any]:
											torch.manual_seed(0						)
											snake_case_ :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    lowerCAmelCase_				(       self:							List[str]						)   ->	str:
											snake_case_ :Union[str, Any]						=			self.dummy_uncond_unet
											snake_case_ :Tuple						=			PNDMScheduler()

											snake_case_ :str						=			PNDMPipeline(unet=snake_case						,     scheduler=snake_case						)
											pndm.to(snake_case						)
											pndm.set_progress_bar_config(disable=snake_case						)

											snake_case_ :int						=			torch.manual_seed(0						)
											snake_case_ :List[Any]						=			pndm(generator=snake_case						,     num_inference_steps=20						,     output_type="""numpy"""						).images

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

											snake_case_ :Optional[int]						=			image[0, -3:, -3:, -1]
											snake_case_ :str						=			image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_ :List[str]						=			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				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Union[str, Any]:
											snake_case_ :int						=			"""google/ddpm-cifar10-32"""

											snake_case_ :List[str]						=			UNetaDModel.from_pretrained(snake_case						)
											snake_case_ :Any						=			PNDMScheduler()

											snake_case_ :Tuple						=			PNDMPipeline(unet=snake_case						,     scheduler=snake_case						)
											pndm.to(snake_case						)
											pndm.set_progress_bar_config(disable=snake_case						)
											snake_case_ :str						=			torch.manual_seed(0						)
											snake_case_ :Any						=			pndm(generator=snake_case						,     output_type="""numpy"""						).images

											snake_case_ :Optional[int]						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_ :Optional[int]						=			np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]						)

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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a      =				{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["ReformerTokenizer"]

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ReformerAttention",
						    "ReformerForMaskedLM",
						    "ReformerForQuestionAnswering",
						    "ReformerForSequenceClassification",
						    "ReformerLayer",
						    "ReformerModel",
						    "ReformerModelWithLMHead",
						    "ReformerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer import ReformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_reformer_fast import ReformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_reformer import (
												    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ReformerAttention,
												    ReformerForMaskedLM,
												    ReformerForQuestionAnswering,
												    ReformerForSequenceClassification,
												    ReformerLayer,
												    ReformerModel,
												    ReformerModelWithLMHead,
												    ReformerPreTrainedModel,
												)

else:
						import sys

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