


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, 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  lowerCamelCase__(      self   :int      )					->    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



					def  lowerCamelCase__(      self   :Optional[Any]      )					->    str:
												a__             =     self.dummy_uncond_unet
												a__             =     ScoreSdeVeScheduler()

												a__             =     ScoreSdeVePipeline(unet=__snake_case			,scheduler=__snake_case      )
												sde_ve.to(__snake_case      )
												sde_ve.set_progress_bar_config(disable=__snake_case      )

												a__             =     torch.manual_seed(0      )
												a__             =     sde_ve(num_inference_steps=2			,output_type='numpy'			,generator=__snake_case      ).images

												a__             =     torch.manual_seed(0      )
												a__             =     sde_ve(num_inference_steps=2			,output_type='numpy'			,generator=__snake_case			,return_dict=__snake_case      )[
												    0
												]

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

												assert image.shape == (1, 32, 32, 3)
												a__             =     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      ):
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    str:
												a__             =     'google/ncsnpp-church-256'
												a__             =     UNetaDModel.from_pretrained(__snake_case      )

												a__             =     ScoreSdeVeScheduler.from_pretrained(__snake_case      )

												a__             =     ScoreSdeVePipeline(unet=__snake_case			,scheduler=__snake_case      )
												sde_ve.to(__snake_case      )
												sde_ve.set_progress_bar_config(disable=__snake_case      )

												a__             =     torch.manual_seed(0      )
												a__             =     sde_ve(num_inference_steps=10			,output_type='numpy'			,generator=__snake_case      ).images

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

												assert image.shape == (1, 2_56, 2_56, 3)

												a__             =     np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

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








from typing import TYPE_CHECKING

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


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




