

'''simple docstring'''







import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class 					A__      (      _snake_case						,						_snake_case						,						unittest.TestCase			):
    lowercase						=		VQModel
    lowercase						=		"sample"



    @property
    def 					snake_case_			(					self		,				UpperCamelCase__=(32, 32)       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      4
           A_						=      3

           A_						=      floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCamelCase__       )

           return {"sample": image}



    @property
    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           return (3, 32, 32)



    @property
    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           return (3, 32, 32)



    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      {
               """block_out_channels""": [32, 64],
               """in_channels""": 3,
               """out_channels""": 3,
               """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
               """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
               """latent_channels""": 3,
           }
           A_						=      self.dummy_input
           return init_dict, inputs_dict



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           pass



    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           pass



    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           A_					,			A_						=      VQModel.from_pretrained("""fusing/vqgan-dummy"""		,				output_loading_info=UpperCamelCase__       )
           self.assertIsNotNone(UpperCamelCase__       )
           self.assertEqual(len(loading_info["""missing_keys"""]       )		,				0       )

           model.to(UpperCamelCase__       )
           A_						=      model(**self.dummy_input       )

           assert image is not None, "Make sure output is not None"







    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
           model.to(UpperCamelCase__       ).eval()

           torch.manual_seed(0       )
           if torch.cuda.is_available():
                  torch.cuda.manual_seed_all(0       )

           A_						=      torch.randn(1		,				model.config.in_channels		,				model.config.sample_size		,				model.config.sample_size       )
           A_						=      image.to(UpperCamelCase__       )
           with torch.no_grad():
                  A_						=      model(UpperCamelCase__       ).sample

           A_						=      output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           A_						=      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]       )
           # fmt: on
           self.assertTrue(torch.allclose(UpperCamelCase__		,				UpperCamelCase__		,				atol=1e-3       )       )



'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):



    def __init__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      None:





           '''simple docstring'''
           warnings.warn(
               """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
               """ use BeitImageProcessor instead."""		,				UpperCamelCase__		,				)
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
