


"""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


a__       :							Dict        =  logging.get_logger(__name__)

enable_full_determinism()




class 				UpperCamelCase_			(				UpperCamelCase    ,						UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						int					      =    UNetaDModel
	snake_case__       :						List[str]					      =    "sample"




	@property
	def 						UpperCAmelCase_   (				self    :       str     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       4
						__SCREAMING_SNAKE_CASE		     =       3
						__SCREAMING_SNAKE_CASE		     =       (3_2, 3_2)

						__SCREAMING_SNAKE_CASE		     =       floats_tensor((batch_size, num_channels) + sizes     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor([1_0]     ).to(UpperCAmelCase__     )

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




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




	@property
	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Tuple:
						return (3, 3_2, 3_2)




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       {
						    "block_out_channels": (3_2, 6_4),
						    "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": 3_2,
						}
						__SCREAMING_SNAKE_CASE		     =       self.dummy_input
						return init_dict, inputs_dict




class 				UpperCamelCase_			(				UpperCamelCase    ,						UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Union[str, Any]					      =    UNetaDModel
	snake_case__       :						List[str]					      =    "sample"




	@property
	def 						UpperCAmelCase_   (				self    :       str     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       4
						__SCREAMING_SNAKE_CASE		     =       4
						__SCREAMING_SNAKE_CASE		     =       (3_2, 3_2)

						__SCREAMING_SNAKE_CASE		     =       floats_tensor((batch_size, num_channels) + sizes     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor([1_0]     ).to(UpperCAmelCase__     )

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




	@property
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Optional[int]:
						return (4, 3_2, 3_2)




	@property
	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Any:
						return (4, 3_2, 3_2)




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       {
						    "sample_size": 3_2,
						    "in_channels": 4,
						    "out_channels": 4,
						    "layers_per_block": 2,
						    "block_out_channels": (3_2, 6_4),
						    "attention_head_dim": 3_2,
						    "down_block_types": ("DownBlock2D", "DownBlock2D"),
						    "up_block_types": ("UpBlock2D", "UpBlock2D"),
						}
						__SCREAMING_SNAKE_CASE		     =       self.dummy_input
						return init_dict, inputs_dict




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       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__     )
						__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"    ,    output_loading_info=UpperCAmelCase__     )
						model.to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Optional[Any]:
						# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"    ,    output_loading_info=UpperCAmelCase__     )
						model_accelerate.to(UpperCAmelCase__     )
						model_accelerate.eval()

						__SCREAMING_SNAKE_CASE		     =       torch.randn(
						    1    ,    model_accelerate.config.in_channels    ,    model_accelerate.config.sample_size    ,    model_accelerate.config.sample_size    ,    generator=torch.manual_seed(0     )    ,    )
						__SCREAMING_SNAKE_CASE		     =       noise.to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor([1_0] * noise.shape[0]     ).to(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       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()

						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       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()
						__SCREAMING_SNAKE_CASE		     =       model_normal_load(UpperCAmelCase__    ,    UpperCAmelCase__     )["sample"]

						assert torch_all_close(UpperCAmelCase__    ,    UpperCAmelCase__    ,    rtol=1E-3     )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"     )
						model.eval()
						model.to(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       torch.randn(
						    1    ,    model.config.in_channels    ,    model.config.sample_size    ,    model.config.sample_size    ,    generator=torch.manual_seed(0     )    ,    )
						__SCREAMING_SNAKE_CASE		     =       noise.to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor([1_0] * noise.shape[0]     ).to(UpperCAmelCase__     )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     ).sample

						__SCREAMING_SNAKE_CASE		     =       output[0, -1, -3:, -3:].flatten().cpu()
						# fmt: off
						__SCREAMING_SNAKE_CASE		     =       torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]     )
						# fmt: on

						self.assertTrue(torch_all_close(UpperCAmelCase__    ,    UpperCAmelCase__    ,    rtol=1E-3     )     )




class 				UpperCamelCase_			(				UpperCamelCase    ,						UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Optional[int]					      =    UNetaDModel
	snake_case__       :						Any					      =    "sample"




	@property
	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]=(3_2, 3_2)     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       4
						__SCREAMING_SNAKE_CASE		     =       3

						__SCREAMING_SNAKE_CASE		     =       floats_tensor((batch_size, num_channels) + sizes     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor(batch_size * [1_0]     ).to(dtype=torch.intaa    ,    device=UpperCAmelCase__     )

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




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




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




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       {
						    "block_out_channels": [3_2, 6_4, 6_4, 6_4],
						    "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",
						    ],
						}
						__SCREAMING_SNAKE_CASE		     =       self.dummy_input
						return init_dict, inputs_dict




	@slow
	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       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__     )
						__SCREAMING_SNAKE_CASE		     =       self.dummy_input
						__SCREAMING_SNAKE_CASE		     =       floats_tensor((4, 3) + (2_5_6, 2_5_6)     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       noise
						__SCREAMING_SNAKE_CASE		     =       model(**UpperCAmelCase__     )

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




	@slow
	def 						UpperCAmelCase_   (				self    :       int     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"     )
						model.to(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       4
						__SCREAMING_SNAKE_CASE		     =       3
						__SCREAMING_SNAKE_CASE		     =       (2_5_6, 2_5_6)

						__SCREAMING_SNAKE_CASE		     =       torch.ones((batch_size, num_channels) + sizes     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor(batch_size * [1E-4]     ).to(UpperCAmelCase__     )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     ).sample

						__SCREAMING_SNAKE_CASE		     =       output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						__SCREAMING_SNAKE_CASE		     =       torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]     )
						# fmt: on

						self.assertTrue(torch_all_close(UpperCAmelCase__    ,    UpperCAmelCase__    ,    rtol=1E-2     )     )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"     )
						model.to(UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       4
						__SCREAMING_SNAKE_CASE		     =       3
						__SCREAMING_SNAKE_CASE		     =       (3_2, 3_2)

						__SCREAMING_SNAKE_CASE		     =       torch.ones((batch_size, num_channels) + sizes     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.tensor(batch_size * [1E-4]     ).to(UpperCAmelCase__     )

						with torch.no_grad():
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     ).sample

						__SCREAMING_SNAKE_CASE		     =       output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						__SCREAMING_SNAKE_CASE		     =       torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]     )
						# fmt: on

						self.assertTrue(torch_all_close(UpperCAmelCase__    ,    UpperCAmelCase__    ,    rtol=1E-2     )     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						# not required for this model
						pass




"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return (data["data"], data["target"])




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       XGBClassifier()
					classifier.fit(lowerCAmelCase_						,		lowerCAmelCase_     )
					return classifier




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       load_iris()
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       data_handling(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       train_test_split(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		test_size=0.25     )

					__SCREAMING_SNAKE_CASE		     =       iris["target_names"]

					# Create an XGBoost Classifier from the training data
					__SCREAMING_SNAKE_CASE		     =       xgboost(lowerCAmelCase_						,		lowerCAmelCase_     )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		display_labels=lowerCAmelCase_						,		cmap="Blues"						,		normalize="true"						,		)
					plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
					plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()
