






import gc
import inspect
import unittest

import torch
from parameterized import parameterized

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

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class 							SCREAMING_SNAKE_CASE__		(       _a				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	PriorTransformer
					a_			=	"hidden_states"



					@property
					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   Optional[int]     =						4
						snake_case__				:   List[Any]     =						8
						snake_case__				:   Optional[int]     =						7

						snake_case__				:   List[Any]     =						floats_tensor((batch_size, embedding_dim) ).to(_A )

						snake_case__				:   Optional[int]     =						floats_tensor((batch_size, embedding_dim) ).to(_A )
						snake_case__				:   Optional[Any]     =						floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(_A )

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}



					def  _lowercase  (      self  :						List[str]							,	__A  :						List[Any]=0 ):
						torch.manual_seed(_A )
						snake_case__				:   str     =						4
						snake_case__				:   Union[str, Any]     =						8
						snake_case__				:   List[str]     =						7

						snake_case__				:   Optional[int]     =						torch.randn((batch_size, embedding_dim) ).to(_A )

						snake_case__				:   List[str]     =						torch.randn((batch_size, embedding_dim) ).to(_A )
						snake_case__				:   Dict     =						torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_A )

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}



					@property
					def  _lowercase  (      self  :						List[Any] ):
						return (4, 8)



					@property
					def  _lowercase  (      self  :						Any ):
						return (4, 8)



					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__				:   Optional[int]     =						{
						    "num_attention_heads": 2,
						    "attention_head_dim": 4,
						    "num_layers": 2,
						    "embedding_dim": 8,
						    "num_embeddings": 7,
						    "additional_embeddings": 4,
						}
						snake_case__				:   Optional[int]     =						self.dummy_input
						return init_dict, inputs_dict



					def  _lowercase  (      self  :						Any ):
						snake_case__,		snake_case__				:   int     =						PriorTransformer.from_pretrained(
						    "hf-internal-testing/prior-dummy"							,	output_loading_info=_A )
						self.assertIsNotNone(_A )
						self.assertEqual(len(loading_info["missing_keys"] )							,	0 )

						model.to(_A )
						snake_case__				:   Any     =						model(**self.dummy_input )[0]

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



					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__,		snake_case__				:   List[str]     =						self.prepare_init_args_and_inputs_for_common()

						snake_case__				:   Any     =						self.model_class(**_A )
						snake_case__				:   Optional[int]     =						inspect.signature(model.forward )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						snake_case__				:   Dict     =						[*signature.parameters.keys()]

						snake_case__				:   str     =						["hidden_states", "timestep"]
						self.assertListEqual(arg_names[:2]							,	_A )






					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   List[Any]     =						PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
						snake_case__				:   str     =						model.to(_A )

						if hasattr(_A							,	"set_default_attn_processor" ):
							model.set_default_attn_processor()

						snake_case__				:   Dict     =						self.get_dummy_seed_input()

						with torch.no_grad():
							snake_case__				:   List[str]     =						model(**_A )[0]

						snake_case__				:   Optional[Any]     =						output[0, :5].flatten().cpu()
						print(_A )

						# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
						# the expected output slices are not the same for CPU and GPU.
						snake_case__				:   str     =						torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9] )
						self.assertTrue(torch_all_close(_A							,	_A							,	rtol=1e-2 ) )



@slow
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Any							,	__A  :						Optional[int]=1							,	__A  :						List[str]=7_6_8							,	__A  :						str=7_7							,	__A  :						Dict=0 ):
						torch.manual_seed(_A )
						snake_case__				:   Any     =						batch_size
						snake_case__				:   Union[str, Any]     =						embedding_dim
						snake_case__				:   Dict     =						num_embeddings

						snake_case__				:   Any     =						torch.randn((batch_size, embedding_dim) ).to(_A )

						snake_case__				:   Dict     =						torch.randn((batch_size, embedding_dim) ).to(_A )
						snake_case__				:   Any     =						torch.randn((batch_size, num_embeddings, embedding_dim) ).to(_A )

						return {
						    "hidden_states": hidden_states,
						    "timestep": 2,
						    "proj_embedding": proj_embedding,
						    "encoder_hidden_states": encoder_hidden_states,
						}



					def  _lowercase  (      self  :						int ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					@parameterized.expand(
					    [
					        # fmt: off
					        [1_3, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
					        [3_7, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
					        # fmt: on
					    ] )
					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						List[str] ):
						snake_case__				:   int     =						PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"							,	subfolder="prior" )
						model.to(_A )
						snake_case__				:   Optional[int]     =						self.get_dummy_seed_input(seed=_A )

						with torch.no_grad():
							snake_case__				:   Tuple     =						model(**_A )[0]

						assert list(sample.shape ) == [1, 7_6_8]

						snake_case__				:   List[Any]     =						sample[0, :8].flatten().cpu()
						print(_A )
						snake_case__				:   List[Any]     =						torch.tensor(_A )

						assert torch_all_close(_A							,	_A							,	atol=1e-3 )











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   SCREAMING_SNAKE_CASE       (				snake_case_      :							dict     ):
	return (data["data"], data["target"])







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							np.ndarray  ,						snake_case_      :							np.ndarray     ):
	snake_case__				:   Optional[int]     =						XGBClassifier()
	classifier.fit(snake_case_  ,						snake_case_     )
	return classifier







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						load_iris()
	snake_case__,		snake_case__				:   str     =						data_handling(snake_case_     )
	snake_case__,		snake_case__,		snake_case__,		snake_case__				:   int     =						train_test_split(
	    snake_case_  ,						snake_case_  ,						test_size=0.25     )

	snake_case__				:   Dict     =						iris["target_names"]

	# Create an XGBoost Classifier from the training data
	snake_case__				:   Dict     =						xgboost(snake_case_  ,						snake_case_     )

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


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()



