





'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_		     = {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		     = [
          "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "BioGptForCausalLM",
          "BioGptForTokenClassification",
          "BioGptForSequenceClassification",
          "BioGptModel",
          "BioGptPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

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









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 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			str				=    VQModel
     __snake_case					:			Optional[Any]				=    "sample"






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Optional[Any]=(32, 32) )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		4
           SCREAMING_SNAKE_CASE	=		3

           SCREAMING_SNAKE_CASE	=		floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase__ )

           return {"sample": image}






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






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




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	str:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,output_loading_info=lowerCamelCase__ )
           self.assertIsNotNone(lowerCamelCase__ )
           self.assertEqual(len(loading_info["""missing_keys"""] )		,0 )

           model.to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(**self.dummy_input )

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy""" )
           model.to(lowerCamelCase__ ).eval()

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

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

           SCREAMING_SNAKE_CASE	=		output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           SCREAMING_SNAKE_CASE	=		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(lowerCamelCase__		,lowerCamelCase__		,atol=1e-3 ) )


