



"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							DiTPipeline
					_a         =							CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
					_a         =							PipelineTesterMixin.required_optional_params - {
					    'latents',
					    'num_images_per_prompt',
					    'callback',
					    'callback_steps',
					}
					_a         =							CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
					_a         =							False



					def 	snake_case ( self							:						List[str]  )->      List[str]:
								torch.manual_seed(0  )
								lowerCamelCase__	:	int				=TransformeraDModel(
								    sample_size=16,						num_layers=2,						patch_size=4,						attention_head_dim=8,						num_attention_heads=2,						in_channels=4,						out_channels=8,						attention_bias=lowerCamelCase,						activation_fn='''gelu-approximate''',						num_embeds_ada_norm=1000,						norm_type='''ada_norm_zero''',						norm_elementwise_affine=lowerCamelCase,						)
								lowerCamelCase__	:	Tuple				=AutoencoderKL()
								lowerCamelCase__	:	Optional[int]				=DDIMScheduler()
								lowerCamelCase__	:	Dict				={'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
								return components



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Optional[int]=0  )->      Dict:
								if str(lowerCamelCase  ).startswith('''mps'''  ):
											lowerCamelCase__	:	str				=torch.manual_seed(lowerCamelCase  )
								else:
											lowerCamelCase__	:	Dict				=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				={
								    '''class_labels''': [1],
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs



					def 	snake_case ( self							:						Dict  )->      int:
								lowerCamelCase__	:	Union[str, Any]				='''cpu'''

								lowerCamelCase__	:	Optional[Any]				=self.get_dummy_components()
								lowerCamelCase__	:	Any				=self.pipeline_class(**lowerCamelCase  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=self.get_dummy_inputs(lowerCamelCase  )
								lowerCamelCase__	:	Any				=pipe(**lowerCamelCase  ).images
								lowerCamelCase__	:	List[str]				=image[0, -3:, -3:, -1]

								self.assertEqual(image.shape,						(1, 16, 16, 3)  )
								lowerCamelCase__	:	Union[str, Any]				=np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]  )
								lowerCamelCase__	:	Optional[int]				=np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(lowerCamelCase,						1E-3  )



					def 	snake_case ( self							:						str  )->      List[str]:
								self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase,						expected_max_diff=1E-3  )




					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available(),						reason='''XFormers attention is only available with CUDA and `xformers` installed''',						)
					def 	snake_case ( self							:						Dict  )->      Any:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )





@require_torch_gpu
@slow
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						List[Any]  )->      Union[str, Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					def 	snake_case ( self							:						Dict  )->      str:
								lowerCamelCase__	:	Any				=torch.manual_seed(0  )

								lowerCamelCase__	:	Any				=DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''  )
								pipe.to('''cuda'''  )

								lowerCamelCase__	:	Optional[Any]				=['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
								lowerCamelCase__	:	int				=pipe.get_label_ids(lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=pipe(lowerCamelCase,						generator=lowerCamelCase,						num_inference_steps=40,						output_type='''np'''  ).images

								for word, image in zip(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	Optional[int]				=load_numpy(
											    F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''  )
											assert np.abs((expected_image - image).max()  ) < 1E-2




					def 	snake_case ( self							:						Any  )->      int:
								lowerCamelCase__	:	Union[str, Any]				=DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''  )
								lowerCamelCase__	:	Any				=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
								pipe.to('''cuda'''  )

								lowerCamelCase__	:	Any				=['''vase''', '''umbrella''']
								lowerCamelCase__	:	Dict				=pipe.get_label_ids(lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=torch.manual_seed(0  )
								lowerCamelCase__	:	Tuple				=pipe(lowerCamelCase,						generator=lowerCamelCase,						num_inference_steps=25,						output_type='''np'''  ).images

								for word, image in zip(lowerCamelCase,						lowerCamelCase  ):
											lowerCamelCase__	:	List[Any]				=load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    F'''/dit/{word}_512.npy'''  )

											assert np.abs((expected_image - image).max()  ) < 1E-1









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""

			# 1. Validate that path exists between current and next vertices
			if graph[path[curr_ind - 1]][next_ver] == 0:
						return False

			# 2. Validate that next vertex is not already in path
			return not any(vertex == next_ver for vertex in path			)






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					list[int]							,	__lowerCamelCase       :					int			):
			"""simple docstring"""

			# Base Case
			if curr_ind == len(__lowerCamelCase			):
						# return whether path exists between current and starting vertices
						return graph[path[curr_ind - 1]][path[0]] == 1

			# Recursive Step
			for next_ver in range(0							,	len(__lowerCamelCase			)			):
						if valid_connection(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			):
									# Insert current vertex  into path as next transition
									lowerCamelCase__	:	Tuple				=next_ver
									# Validate created path
									if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	curr_ind + 1			):
												return True
									# Backtrack
									lowerCamelCase__	:	int				=-1
			return False






def   snake_case__     (	__lowerCamelCase       :					list[list[int]]							,	__lowerCamelCase       :					int = 0			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=[-1] * (len(__lowerCamelCase			) + 1)
			# initialize start and end of path with starting index
			lowerCamelCase__	:	Union[str, Any]				=start_index
			# evaluate and if we find answer return path either return empty array
			return path if util_hamilton_cycle(__lowerCamelCase							,	__lowerCamelCase							,	1			) else []




