






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       __UpperCAmelCase		(__lowercase       ,unittest.TestCase    ):
			__snake_case				:      str 						=							DiTPipeline
			__snake_case				:      List[Any] 						=							CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
			__snake_case				:      str 						=							PipelineTesterMixin.required_optional_params - {
			    """latents""",
			    """num_images_per_prompt""",
			    """callback""",
			    """callback_steps""",
			}
			__snake_case				:      List[str] 						=							CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
			__snake_case				:      int 						=							False







			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									torch.manual_seed(0     )
									_SCREAMING_SNAKE_CASE	      =  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=SCREAMING_SNAKE_CASE__      ,	activation_fn="""gelu-approximate"""      ,	num_embeds_ada_norm=1_000      ,	norm_type="""ada_norm_zero"""      ,	norm_elementwise_affine=SCREAMING_SNAKE_CASE__      ,	)
									_SCREAMING_SNAKE_CASE	      =  AutoencoderKL()
									_SCREAMING_SNAKE_CASE	      =  DDIMScheduler()
									_SCREAMING_SNAKE_CASE	      =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
									return components







			def 							UpperCamelCase      (	self:  Any      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  Any=0     ):







									'''simple docstring'''




									if str(SCREAMING_SNAKE_CASE__     ).startswith("""mps"""     ):
															_SCREAMING_SNAKE_CASE	      =  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
									else:
															_SCREAMING_SNAKE_CASE	      =  torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
									_SCREAMING_SNAKE_CASE	      =  {
									    """class_labels""": [1],
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  """cpu"""

									_SCREAMING_SNAKE_CASE	      =  self.get_dummy_components()
									_SCREAMING_SNAKE_CASE	      =  self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
									pipe.to(SCREAMING_SNAKE_CASE__     )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

									_SCREAMING_SNAKE_CASE	      =  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
									_SCREAMING_SNAKE_CASE	      =  pipe(**SCREAMING_SNAKE_CASE__     ).images
									_SCREAMING_SNAKE_CASE	      =  image[0, -3:, -3:, -1]

									self.assertEqual(image.shape      ,	(1, 16, 16, 3)     )
									_SCREAMING_SNAKE_CASE	      =  np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]     )
									_SCREAMING_SNAKE_CASE	      =  np.abs(image_slice.flatten() - expected_slice     ).max()
									self.assertLessEqual(SCREAMING_SNAKE_CASE__      ,	1E-3     )







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									self._test_inference_batch_single_identical(relax_max_difference=SCREAMING_SNAKE_CASE__      ,	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 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



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







			def 							UpperCamelCase      (	self:  Union[str, Any]     ):







									'''simple docstring'''




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  torch.manual_seed(0     )

									_SCREAMING_SNAKE_CASE	      =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""     )
									pipe.to("""cuda"""     )

									_SCREAMING_SNAKE_CASE	      =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
									_SCREAMING_SNAKE_CASE	      =  pipe.get_label_ids(SCREAMING_SNAKE_CASE__     )

									_SCREAMING_SNAKE_CASE	      =  pipe(SCREAMING_SNAKE_CASE__      ,	generator=SCREAMING_SNAKE_CASE__      ,	num_inference_steps=40      ,	output_type="""np"""     ).images

									for word, image in zip(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
															_SCREAMING_SNAKE_CASE	      =  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 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""     )
									_SCREAMING_SNAKE_CASE	      =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
									pipe.to("""cuda"""     )

									_SCREAMING_SNAKE_CASE	      =  ["""vase""", """umbrella"""]
									_SCREAMING_SNAKE_CASE	      =  pipe.get_label_ids(SCREAMING_SNAKE_CASE__     )

									_SCREAMING_SNAKE_CASE	      =  torch.manual_seed(0     )
									_SCREAMING_SNAKE_CASE	      =  pipe(SCREAMING_SNAKE_CASE__      ,	generator=SCREAMING_SNAKE_CASE__      ,	num_inference_steps=25      ,	output_type="""np"""     ).images

									for word, image in zip(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
															_SCREAMING_SNAKE_CASE	      =  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



from queue import PriorityQueue
from typing import Any

import numpy as np




def  __magic_name__ (   __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						PriorityQueue		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						float | int		,  )			->   float | int:
				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								__lowerCamelCase    					=	cst_fwd.get(__lowerCAmelCase		,  np.inf							)
								__lowerCamelCase    					=	cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt)							)
												__lowerCamelCase    					=	new_cost_f
												__lowerCamelCase    					=	v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																__lowerCamelCase    					=	cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict							)			->   int:
				__lowerCamelCase    					=	-1

				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	{source: 0}
				__lowerCamelCase    					=	{destination: 0}
				__lowerCamelCase    					=	{source: None}
				__lowerCamelCase    					=	{destination: None}
				__lowerCamelCase    					=	PriorityQueue()
				__lowerCamelCase    					=	PriorityQueue()

				__lowerCamelCase    					=	np.inf

				queue_forward.put((0, source)							)
				queue_backward.put((0, destination)							)

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								__lowerCamelCase					,    __lowerCamelCase    					=	queue_forward.get()
								visited_forward.add(__lowerCAmelCase							)

								__lowerCamelCase					,    __lowerCamelCase    					=	queue_backward.get()
								visited_backward.add(__lowerCAmelCase							)

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								__lowerCamelCase    					=	shortest_distance
				return shortest_path_distance


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
SCREAMING_SNAKE_CASE__   :			Optional[int]												=    {
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()



