


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 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   @property
   def _snake_case    (		self ):






     """simple docstring"""





     torch.manual_seed(0 )
     lowerCamelCase :					List[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 ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.dummy_uncond_unet
     lowerCamelCase :					str    =				ScoreSdeVeScheduler()

     lowerCamelCase :					Any    =				ScoreSdeVePipeline(unet=__A						,				scheduler=__A )
     sde_ve.to(__A )
     sde_ve.set_progress_bar_config(disable=__A )

     lowerCamelCase :					Union[str, Any]    =				torch.manual_seed(0 )
     lowerCamelCase :					Dict    =				sde_ve(num_inference_steps=2						,				output_type="numpy"						,				generator=__A ).images

     lowerCamelCase :					Union[str, Any]    =				torch.manual_seed(0 )
     lowerCamelCase :					List[Any]    =				sde_ve(num_inference_steps=2						,				output_type="numpy"						,				generator=__A						,				return_dict=__A )[
         0
     ]

     lowerCamelCase :					Optional[int]    =				image[0, -3:, -3:, -1]
     lowerCamelCase :					Optional[Any]    =				image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     lowerCamelCase :					List[str]    =				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 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				"google/ncsnpp-church-256"
     lowerCamelCase :					Dict    =				UNetaDModel.from_pretrained(__A )

     lowerCamelCase :					Dict    =				ScoreSdeVeScheduler.from_pretrained(__A )

     lowerCamelCase :					str    =				ScoreSdeVePipeline(unet=__A						,				scheduler=__A )
     sde_ve.to(__A )
     sde_ve.set_progress_bar_config(disable=__A )

     lowerCamelCase :					str    =				torch.manual_seed(0 )
     lowerCamelCase :					List[Any]    =				sde_ve(num_inference_steps=10						,				output_type="numpy"						,				generator=__A ).images

     lowerCamelCase :					int    =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)

     lowerCamelCase :					Optional[int]    =				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_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

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



