



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						(						snake_case_       ,snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     VQModel
   SCREAMING_SNAKE_CASE__							=     '''sample'''





   @property
   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase=(32, 32)			):
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  3

         _lowerCAmelCase 			=  floats_tensor((batch_size, num_channels) + sizes			).to(_lowerCAmelCase			)

         return {"sample": image}





   @property
   def 	__lowerCAmelCase							(	self			):
         return (3, 32, 32)





   @property
   def 	__lowerCAmelCase							(	self			):
         return (3, 32, 32)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  {
             '''block_out_channels''': [32, 64],
             '''in_channels''': 3,
             '''out_channels''': 3,
             '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
             '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
             '''latent_channels''': 3,
         }
         _lowerCAmelCase 			=  self.dummy_input
         return init_dict, inputs_dict





   def 	__lowerCAmelCase							(	self			):
         pass





   def 	__lowerCAmelCase							(	self			):
         pass





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase			,   _lowerCAmelCase 			=  VQModel.from_pretrained('''fusing/vqgan-dummy'''       ,     output_loading_info=_lowerCAmelCase			)
         self.assertIsNotNone(_lowerCAmelCase			)
         self.assertEqual(len(loading_info['''missing_keys''']			)       ,     0			)

         model.to(_lowerCAmelCase			)
         _lowerCAmelCase 			=  model(**self.dummy_input			)

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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			)

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

         _lowerCAmelCase 			=  output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         _lowerCAmelCase 			=  torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]			)
         # fmt: on
         self.assertTrue(torch.allclose(_lowerCAmelCase       ,     _lowerCAmelCase       ,     atol=1E-3			)			)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

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