






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  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	List[str]      ):
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[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 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.dummy_uncond_unet
       SCREAMING_SNAKE_CASE       :      str							=    ScoreSdeVeScheduler()

       SCREAMING_SNAKE_CASE       :      str							=    ScoreSdeVePipeline(unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_      )
       sde_ve.to(UpperCAmelCase_      )
       sde_ve.set_progress_bar_config(disable=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    sde_ve(num_inference_steps=2					, output_type="numpy"					, generator=UpperCAmelCase_      ).images

       SCREAMING_SNAKE_CASE       :      int							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    sde_ve(num_inference_steps=2					, output_type="numpy"					, generator=UpperCAmelCase_					, return_dict=UpperCAmelCase_      )[
           0
       ]

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE       :      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  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    "google/ncsnpp-church-256"
       SCREAMING_SNAKE_CASE       :      Any							=    UNetaDModel.from_pretrained(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    ScoreSdeVeScheduler.from_pretrained(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    ScoreSdeVePipeline(unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_      )
       sde_ve.to(UpperCAmelCase_      )
       sde_ve.set_progress_bar_config(disable=UpperCAmelCase_      )

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

       SCREAMING_SNAKE_CASE       :      Any							=    image[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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














# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case    				=		{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """CpmAntForCausalLM""",
       """CpmAntModel""",
       """CpmAntPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

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






