
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	ShapEPipeline
	snake_case_          =	["prompt"]
	snake_case_          =	["prompt"]
	snake_case_          =	[
	    "num_images_per_prompt",
	    "num_inference_steps",
	    "generator",
	    "latents",
	    "guidance_scale",
	    "frame_size",
	    "output_type",
	    "return_dict",
	]
	snake_case_          =	False






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return 32






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return 32






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					return self.time_input_dim * 4






	@property
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return 8






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
					return tokenizer






	@property
	def 							UpperCamelCase_    (			self   :					int					):
					torch.manual_seed(0					)
					__A       =					CLIPTextConfig(
					    bos_token_id=0				,eos_token_id=2				,hidden_size=self.text_embedder_hidden_size				,projection_dim=self.text_embedder_hidden_size				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
					return CLIPTextModelWithProjection(A					)






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					torch.manual_seed(0					)

					__A       =					{
					    "num_attention_heads": 2,
					    "attention_head_dim": 16,
					    "embedding_dim": self.time_input_dim,
					    "num_embeddings": 32,
					    "embedding_proj_dim": self.text_embedder_hidden_size,
					    "time_embed_dim": self.time_embed_dim,
					    "num_layers": 1,
					    "clip_embed_dim": self.time_input_dim * 2,
					    "additional_embeddings": 0,
					    "time_embed_act_fn": "gelu",
					    "norm_in_type": "layer",
					    "encoder_hid_proj_type": None,
					    "added_emb_type": None,
					}

					__A       =					PriorTransformer(**A					)
					return model






	@property
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					torch.manual_seed(0					)

					__A       =					{
					    "param_shapes": (
					        (self.renderer_dim, 93),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					    ),
					    "d_latent": self.time_input_dim,
					    "d_hidden": self.renderer_dim,
					    "n_output": 12,
					    "background": (
					        0.1,
					        0.1,
					        0.1,
					    ),
					}
					__A       =					ShapERenderer(**A					)
					return model






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					self.dummy_prior
					__A       =					self.dummy_text_encoder
					__A       =					self.dummy_tokenizer
					__A       =					self.dummy_renderer

					__A       =					HeunDiscreteScheduler(
					    beta_schedule="exp"				,num_train_timesteps=10_24				,prediction_type="sample"				,use_karras_sigmas=A				,clip_sample=A				,clip_sample_range=1.0				,)
					__A       =					{
					    "prior": prior,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					Optional[Any]				,A   :					Any=0					):
					if str(A					).startswith("mps"					):
									__A       =					torch.manual_seed(A					)
					else:
									__A       =					torch.Generator(device=A					).manual_seed(A					)
					__A       =					{
					    "prompt": "horse",
					    "generator": generator,
					    "num_inference_steps": 1,
					    "frame_size": 32,
					    "output_type": "np",
					}
					return inputs






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					"cpu"

					__A       =					self.get_dummy_components()

					__A       =					self.pipeline_class(**A					)
					__A       =					pipe.to(A					)

					pipe.set_progress_bar_config(disable=A					)

					__A       =					pipe(**self.get_dummy_inputs(A					)					)
					__A       =					output.images[0]
					__A       =					image[0, -3:, -3:, -1]

					assert image.shape == (20, 32, 32, 3)

					__A       =					np.array(
					    [
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					    ]					)

					assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
					self._test_inference_batch_consistent(batch_sizes=[1, 2]					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					torch_device == "cpu"
					__A       =					True

					self._test_inference_batch_single_identical(
					    batch_size=2				,test_max_difference=A				,relax_max_difference=A				,)







	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					__A       =					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					1
					__A       =					2

					__A       =					self.get_dummy_inputs(A					)

					for key in inputs.keys():
									if key in self.batch_params:
													__A       =					batch_size * [inputs[key]]

					__A       =					pipe(**A				,num_images_per_prompt=A					)[0]

					assert images.shape[0] == batch_size * num_images_per_prompt





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




	'''simple docstring'''





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







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/shap_e/test_shap_e_np_out.npy"					)
					__A       =					ShapEPipeline.from_pretrained("openai/shap-e"					)
					__A       =					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					torch.Generator(device=A					).manual_seed(0					)

					__A       =					pipe(
					    "a shark"				,generator=A				,guidance_scale=15.0				,num_inference_steps=64				,frame_size=64				,output_type="np"				,).images[0]

					assert images.shape == (20, 64, 64, 3)

					assert_mean_pixel_difference(A				,A					)



from __future__ import annotations

__A			:					str						=			list[tuple[int, int]]

__A			:					Optional[int]						=			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__A			:					List[str]						=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float     , __lowerCamelCase					:			Node | None     , ):
       SCREAMING_SNAKE_CASE										=   pos_x
       SCREAMING_SNAKE_CASE										=   pos_y
       SCREAMING_SNAKE_CASE										=   (pos_y, pos_x)
       SCREAMING_SNAKE_CASE										=   goal_x
       SCREAMING_SNAKE_CASE										=   goal_y
       SCREAMING_SNAKE_CASE										=   g_cost
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   self.calculate_heuristic()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   abs(self.pos_x - self.goal_x			)
       SCREAMING_SNAKE_CASE										=   abs(self.pos_y - self.goal_y			)
       return dx + dy





   def __lt__(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return self.f_cost < other.f_cost





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			tuple[int, int]     , __lowerCamelCase					:			tuple[int, int]			):
       SCREAMING_SNAKE_CASE										=   Node(start[1]     , start[0]     , goal[1]     , goal[0]     , 0     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   Node(goal[1]     , goal[0]     , goal[1]     , goal[0]     , 99999     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   [self.start]
       SCREAMING_SNAKE_CASE										=   []

       SCREAMING_SNAKE_CASE										=   False





   def      _snake_case		(       self					:			Optional[Any]			):
       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(0			)

           if current_node.pos == self.target.pos:
               SCREAMING_SNAKE_CASE										=   True
               return self.retrace_path(__lowerCamelCase			)

           self.closed_nodes.append(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.get_successors(__lowerCamelCase			)

           for child_node in successors:
               if child_node in self.closed_nodes:
                   continue

               if child_node not in self.open_nodes:
                   self.open_nodes.append(__lowerCamelCase			)
               else:
                   # retrieve the best current path
                   SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase			)			)

                   if child_node.g_cost < better_node.g_cost:
                       self.open_nodes.append(__lowerCamelCase			)
                   else:
                       self.open_nodes.append(__lowerCamelCase			)

       if not self.reached:
           return [self.start.pos]
       return None





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Node			):
       SCREAMING_SNAKE_CASE										=   []
       for action in delta:
           SCREAMING_SNAKE_CASE										=   parent.pos_x + action[1]
           SCREAMING_SNAKE_CASE										=   parent.pos_y + action[0]

           if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(__lowerCamelCase			) - 1):
               continue

           if grid[pos_y][pos_x] != 0:
               continue

           successors.append(
               Node(
                   __lowerCamelCase     , __lowerCamelCase     , self.target.pos_y     , self.target.pos_x     , parent.g_cost + 1     , __lowerCamelCase     , )			)
       return successors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Node | None			):
       SCREAMING_SNAKE_CASE										=   node
       SCREAMING_SNAKE_CASE										=   []
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)			)
           SCREAMING_SNAKE_CASE										=   current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
    __A			:					Optional[Any]						=			(0, 0)
    __A			:					Optional[int]						=			(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('------')

    __A			:					List[str]						=			GreedyBestFirst(init, goal)
    __A			:					Tuple						=			greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __A			:					Optional[Any]						=			2

        for elem in grid:
            print(elem)