
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'''




	_A			=  VQModel
	_A			=  'sample'







	@property
	def      _lowerCamelCase       (      self	:Tuple  ,     a	:List[str]=(3_2, 3_2)		)	->				int:
								__UpperCamelCase			:     Dict         =      4
								__UpperCamelCase			:     int         =      3

								__UpperCamelCase			:     str         =      floats_tensor((batch_size, num_channels) + sizes		).to(a		)

								return {"sample": image}







	@property
	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								return (3, 3_2, 3_2)







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				str:
								return (3, 3_2, 3_2)







	def      _lowerCamelCase       (      self	:int		)	->				Dict:
								__UpperCamelCase			:     Tuple         =      {
								    "block_out_channels": [3_2, 6_4],
								    "in_channels": 3,
								    "out_channels": 3,
								    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
								    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "latent_channels": 3,
								}
								__UpperCamelCase			:     int         =      self.dummy_input
								return init_dict, inputs_dict







	def      _lowerCamelCase       (      self	:Any		)	->				Tuple:
								pass







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[Any]:
								pass







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Any:
								__UpperCamelCase			:     Union[str, Any]         =      VQModel.from_pretrained("fusing/vqgan-dummy"  ,     output_loading_info=a		)
								self.assertIsNotNone(a		)
								self.assertEqual(len(loading_info["missing_keys"]		)  ,     0		)

								model.to(a		)
								__UpperCamelCase			:     Optional[int]         =      model(**self.dummy_input		)

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







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Tuple:
								__UpperCamelCase			:     Any         =      VQModel.from_pretrained("fusing/vqgan-dummy"		)
								model.to(a		).eval()

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

								__UpperCamelCase			:     Tuple         =      torch.randn(1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size		)
								__UpperCamelCase			:     str         =      image.to(a		)
								with torch.no_grad():
															__UpperCamelCase			:     List[Any]         =      model(a		).sample

								__UpperCamelCase			:     Optional[int]         =      output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								__UpperCamelCase			:     Optional[int]         =      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(a  ,     a  ,     atol=1E-3		)		)

from typing import TYPE_CHECKING

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


lowercase						:      str									= {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      str									= ['VisionEncoderDecoderModel']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      List[Any]									= ['TFVisionEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      int									= ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)