




"""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 lowerCAmelCase__	(     lowercase,	lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			VQModel
			lowerCamelCase__      =			"""sample"""
			@property
			def 					A_    (  self  ,      lowercase=(32, 32)  ):
							_lowerCamelCase     :			str    =    4
							_lowerCamelCase     :			Tuple    =    3

							_lowerCamelCase     :			Any    =    floats_tensor((batch_size, num_channels) + sizes  ).to(lowercase  )

							return {"sample": image}
			@property
			def 					A_    (  self  ):
							return (3, 32, 32)
			@property
			def 					A_    (  self  ):
							return (3, 32, 32)
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    {
							    '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     :			List[str]    =    self.dummy_input
							return init_dict, inputs_dict
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							_lowerCamelCase, _lowerCamelCase     :			List[str]    =    VQModel.from_pretrained('fusing/vqgan-dummy'  ,      output_loading_info=lowercase  )
							self.assertIsNotNone(lowercase  )
							self.assertEqual(len(loading_info['missing_keys']  )  ,      0  )

							model.to(lowercase  )
							_lowerCamelCase     :			Dict    =    model(**self.dummy_input  )

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






			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    VQModel.from_pretrained('fusing/vqgan-dummy'  )
							model.to(lowercase  ).eval()

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

							_lowerCamelCase     :			Any    =    torch.randn(1  ,      model.config.in_channels  ,      model.config.sample_size  ,      model.config.sample_size  )
							_lowerCamelCase     :			List[str]    =    image.to(lowercase  )
							with torch.no_grad():
											_lowerCamelCase     :			int    =    model(lowercase  ).sample

							_lowerCamelCase     :			List[str]    =    output[0, -1, -3:, -3:].flatten().cpu()
							# fmt: off
							_lowerCamelCase     :			Tuple    =    torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]  )
							# fmt: on
							self.assertTrue(torch.allclose(lowercase  ,      lowercase  ,      atol=1E-3  )  )





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

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