
"""simple docstring"""


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)

enable_full_determinism()



class   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					str   =			UNetaDModel
       UpperCamelCase_		:					Optional[Any]   =			'''sample'''






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		4
              UpperCamelCase					 =		3
              UpperCamelCase					 =		(32, 32)

              UpperCamelCase					 =		floats_tensor((batch_size, num_channels) + sizes			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor([10]			).to(UpperCAmelCase_			)

              return {"sample": noise, "timestep": time_step}






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  int:







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






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  List[str]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		{
                  "block_out_channels": (32, 64),
                  "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
                  "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
                  "attention_head_dim": 3,
                  "out_channels": 3,
                  "in_channels": 3,
                  "layers_per_block": 2,
                  "sample_size": 32,
              }
              UpperCamelCase					 =		self.dummy_input
              return init_dict, inputs_dict





class   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Optional[int]   =			UNetaDModel
       UpperCamelCase_		:					Any   =			'''sample'''






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		4
              UpperCamelCase					 =		4
              UpperCamelCase					 =		(32, 32)

              UpperCamelCase					 =		floats_tensor((batch_size, num_channels) + sizes			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor([10]			).to(UpperCAmelCase_			)

              return {"sample": noise, "timestep": time_step}






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Dict:







              """simple docstring"""
              return (4, 32, 32)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              return (4, 32, 32)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		{
                  "sample_size": 32,
                  "in_channels": 4,
                  "out_channels": 4,
                  "layers_per_block": 2,
                  "block_out_channels": (32, 64),
                  "attention_head_dim": 32,
                  "down_block_types": ("DownBlock2D", "DownBlock2D"),
                  "up_block_types": ("UpBlock2D", "UpBlock2D"),
              }
              UpperCamelCase					 =		self.dummy_input
              return init_dict, inputs_dict






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"       ,      output_loading_info=UpperCAmelCase_			)

              self.assertIsNotNone(UpperCAmelCase_			)
              self.assertEqual(len(loading_info["missing_keys"]			)       ,      0			)

              model.to(UpperCAmelCase_			)
              UpperCamelCase					 =		model(**self.dummy_input			).sample

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






       @unittest.skipIf(torch_device != "cuda"       ,      "This test is supposed to run on GPU"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  List[str]:







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"       ,      output_loading_info=UpperCAmelCase_			)
              model.to(UpperCAmelCase_			)
              UpperCamelCase					 =		model(**self.dummy_input			).sample

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






       @unittest.skipIf(torch_device != "cuda"       ,      "This test is supposed to run on GPU"			)
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
              UpperCamelCase ,       UpperCamelCase					 =		UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"       ,      output_loading_info=UpperCAmelCase_			)
              model_accelerate.to(UpperCAmelCase_			)
              model_accelerate.eval()

              UpperCamelCase					 =		torch.randn(
                  1       ,      model_accelerate.config.in_channels       ,      model_accelerate.config.sample_size       ,      model_accelerate.config.sample_size       ,      generator=torch.manual_seed(0			)       ,      )
              UpperCamelCase					 =		noise.to(UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor([10] * noise.shape[0]			).to(UpperCAmelCase_			)

              UpperCamelCase					 =		model_accelerate(UpperCAmelCase_       ,      UpperCAmelCase_			)["sample"]

              # two models don't need to stay in the device at the same time
              del model_accelerate
              torch.cuda.empty_cache()
              gc.collect()

              UpperCamelCase ,       UpperCamelCase					 =		UNetaDModel.from_pretrained(
                  "fusing/unet-ldm-dummy-update"       ,      output_loading_info=UpperCAmelCase_       ,      low_cpu_mem_usage=UpperCAmelCase_			)
              model_normal_load.to(UpperCAmelCase_			)
              model_normal_load.eval()
              UpperCamelCase					 =		model_normal_load(UpperCAmelCase_       ,      UpperCAmelCase_			)["sample"]

              assert torch_all_close(UpperCAmelCase_       ,      UpperCAmelCase_       ,      rtol=1e-3			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"			)
              model.eval()
              model.to(UpperCAmelCase_			)

              UpperCamelCase					 =		torch.randn(
                  1       ,      model.config.in_channels       ,      model.config.sample_size       ,      model.config.sample_size       ,      generator=torch.manual_seed(0			)       ,      )
              UpperCamelCase					 =		noise.to(UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor([10] * noise.shape[0]			).to(UpperCAmelCase_			)

              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			).sample

              UpperCamelCase					 =		output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              UpperCamelCase					 =		torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]			)
              # fmt: on

              self.assertTrue(torch_all_close(UpperCAmelCase_       ,      UpperCAmelCase_       ,      rtol=1e-3			)			)





class   __a  (      _lowerCAmelCase    ,     _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Union[str, Any]   =			UNetaDModel
       UpperCamelCase_		:					Tuple   =			'''sample'''






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							Optional[int]=(32, 32)			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		4
              UpperCamelCase					 =		3

              UpperCamelCase					 =		floats_tensor((batch_size, num_channels) + sizes			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor(batch_size * [10]			).to(dtype=torch.intaa       ,      device=UpperCAmelCase_			)

              return {"sample": noise, "timestep": time_step}






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  str:







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






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  str:







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






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







              """simple docstring"""
              UpperCamelCase					 =		{
                  "block_out_channels": [32, 64, 64, 64],
                  "in_channels": 3,
                  "layers_per_block": 1,
                  "out_channels": 3,
                  "time_embedding_type": "fourier",
                  "norm_eps": 1e-6,
                  "mid_block_scale_factor": math.sqrt(2.0			),
                  "norm_num_groups": None,
                  "down_block_types": [
                      "SkipDownBlock2D",
                      "AttnSkipDownBlock2D",
                      "SkipDownBlock2D",
                      "SkipDownBlock2D",
                  ],
                  "up_block_types": [
                      "SkipUpBlock2D",
                      "SkipUpBlock2D",
                      "AttnSkipUpBlock2D",
                      "SkipUpBlock2D",
                  ],
              }
              UpperCamelCase					 =		self.dummy_input
              return init_dict, inputs_dict






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  str:







              """simple docstring"""
              UpperCamelCase ,       UpperCamelCase					 =		UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"       ,      output_loading_info=UpperCAmelCase_			)
              self.assertIsNotNone(UpperCAmelCase_			)
              self.assertEqual(len(loading_info["missing_keys"]			)       ,      0			)

              model.to(UpperCAmelCase_			)
              UpperCamelCase					 =		self.dummy_input
              UpperCamelCase					 =		floats_tensor((4, 3) + (256, 256)			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		noise
              UpperCamelCase					 =		model(**UpperCAmelCase_			)

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






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"			)
              model.to(UpperCAmelCase_			)

              UpperCamelCase					 =		4
              UpperCamelCase					 =		3
              UpperCamelCase					 =		(256, 256)

              UpperCamelCase					 =		torch.ones((batch_size, num_channels) + sizes			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor(batch_size * [1e-4]			).to(UpperCAmelCase_			)

              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			).sample

              UpperCamelCase					 =		output[0, -3:, -3:, -1].flatten().cpu()
              # fmt: off
              UpperCamelCase					 =		torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]			)
              # fmt: on

              self.assertTrue(torch_all_close(UpperCAmelCase_       ,      UpperCAmelCase_       ,      rtol=1e-2			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"			)
              model.to(UpperCAmelCase_			)

              UpperCamelCase					 =		4
              UpperCamelCase					 =		3
              UpperCamelCase					 =		(32, 32)

              UpperCamelCase					 =		torch.ones((batch_size, num_channels) + sizes			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor(batch_size * [1e-4]			).to(UpperCAmelCase_			)

              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			).sample

              UpperCamelCase					 =		output[0, -3:, -3:, -1].flatten().cpu()
              # fmt: off
              UpperCamelCase					 =		torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]			)
              # fmt: on

              self.assertTrue(torch_all_close(UpperCAmelCase_       ,      UpperCAmelCase_       ,      rtol=1e-2			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[Any]:







              """simple docstring"""
              # not required for this model
              pass







"""simple docstring"""


import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)



class   __a  (      unittest.TestCase	):






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







              """simple docstring"""
              UpperCamelCase					 =		["a", "b", "c"]

              # Defaults to last layer if both are None
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [2]			)

              # Out indices set to match out features
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(["a", "c"]       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [0, 2]			)

              # Out features set to match out indices
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      [0, 2]       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [0, 2]			)

              # Out features selected from negative indices
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      [-3, -1]       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [-3, -1]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # Stage names must be set
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 1)       ,      UpperCAmelCase_			)

              # Out features must be a list
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(("a", "b")       ,      (0, 1)       ,      ["a", "b"]			)

              # Out features must be a subset of stage names
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 1)       ,      ["a"]			)

              # Out indices must be a list or tuple
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(UpperCAmelCase_       ,      0       ,      ["a", "b"]			)

              # Out indices must be a subset of stage names
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(UpperCAmelCase_       ,      (0, 1)       ,      ["a"]			)

              # Out features and out indices must be the same length
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0,)       ,      ["a", "b", "c"]			)

              # Out features should match out indices
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 2)       ,      ["a", "b", "c"]			)

              # Out features and out indices should be in order
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["b", "a"]       ,      (0, 1)       ,      ["a", "b"]			)

              # Check passes with valid inputs
              verify_out_features_out_indices(["a", "b", "d"]       ,      (0, 1, -1)       ,      ["a", "b", "c", "d"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		BackboneMixin()

              UpperCamelCase					 =		["a", "b", "c"]
              UpperCamelCase					 =		["a", "c"]
              UpperCamelCase					 =		[0, 2]

              # Check that the output features and indices are set correctly
              self.assertEqual(backbone.out_features       ,      ["a", "c"]			)
              self.assertEqual(backbone.out_indices       ,      [0, 2]			)

              # Check out features and indices are updated correctly
              UpperCamelCase					 =		["a", "b"]
              self.assertEqual(backbone.out_features       ,      ["a", "b"]			)
              self.assertEqual(backbone.out_indices       ,      [0, 1]			)

              UpperCamelCase					 =		[-3, -1]
              self.assertEqual(backbone.out_features       ,      ["a", "c"]			)
              self.assertEqual(backbone.out_indices       ,      [-3, -1]			)





