


"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class _lowerCAmelCase       (   __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							unittest.TestCase			):
   __UpperCAmelCase				: Optional[Any]													=						AltDiffusionPipeline
   __UpperCAmelCase				: Tuple													=						TEXT_TO_IMAGE_PARAMS
   __UpperCAmelCase				: Optional[int]													=						TEXT_TO_IMAGE_BATCH_PARAMS
   __UpperCAmelCase				: Dict													=						TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase				: Any													=						TEXT_TO_IMAGE_IMAGE_PARAMS







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   str         =					UNetaDConditionModel(
           block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=4		,			out_channels=4		,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,			cross_attention_dim=32		,			)
       snake_case					:   Tuple         =					DDIMScheduler(
           beta_start=0.00085		,			beta_end=0.012		,			beta_schedule="scaled_linear"		,			clip_sample=lowerCamelCase__		,			set_alpha_to_one=lowerCamelCase__		,			)
       torch.manual_seed(0		)
       snake_case					:   List[str]         =					AutoencoderKL(
           block_out_channels=[32, 64]		,			in_channels=3		,			out_channels=3		,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,			latent_channels=4		,			)

       # TODO: address the non-deterministic text encoder (fails for save-load tests)
       # torch.manual_seed(0)
       # text_encoder_config = RobertaSeriesConfig(
       #     hidden_size=32,
       #     project_dim=32,
       #     intermediate_size=37,
       #     layer_norm_eps=1e-05,
       #     num_attention_heads=4,
       #     num_hidden_layers=5,
       #     vocab_size=5002,
       # )
       # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

       torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					CLIPTextConfig(
           bos_token_id=0		,			eos_token_id=2		,			hidden_size=32		,			projection_dim=32		,			intermediate_size=37		,			layer_norm_eps=1e-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=5002		,			)
       snake_case					:   List[Any]         =					CLIPTextModel(lowerCamelCase__		)

       snake_case					:   List[Any]         =					XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"		)
       snake_case					:   Tuple         =					77

       snake_case					:   Dict         =					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=0		)			->	Union[str, Any]:

       '''simple docstring'''




       if str(lowerCamelCase__		).startswith("mps"		):
           snake_case					:   Optional[int]         =					torch.manual_seed(lowerCamelCase__		)
       else:
           snake_case					:   Dict         =					torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)
       snake_case					:   Dict         =					{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Any         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       snake_case					:   Optional[int]         =					self.get_dummy_components()
       torch.manual_seed(0		)
       snake_case					:   str         =					RobertaSeriesConfig(
           hidden_size=32		,			project_dim=32		,			intermediate_size=37		,			layer_norm_eps=1e-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=5002		,			)
       # TODO: remove after fixing the non-deterministic text encoder
       snake_case					:   int         =					RobertaSeriesModelWithTransformation(lowerCamelCase__		)
       snake_case					:   Any         =					text_encoder

       snake_case					:   Dict         =					AltDiffusionPipeline(**lowerCamelCase__		)
       snake_case					:   Any         =					alt_pipe.to(lowerCamelCase__		)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

       snake_case					:   Dict         =					self.get_dummy_inputs(lowerCamelCase__		)
       snake_case					:   Optional[int]         =					'''A photo of an astronaut'''
       snake_case					:   int         =					alt_pipe(**lowerCamelCase__		)
       snake_case					:   Tuple         =					output.images
       snake_case					:   str         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case					:   List[Any]         =					np.array(
           [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]		)

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







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   List[str]         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       snake_case					:   Optional[Any]         =					self.get_dummy_components()
       snake_case					:   Optional[Any]         =					PNDMScheduler(skip_prk_steps=lowerCamelCase__		)
       torch.manual_seed(0		)
       snake_case					:   Optional[int]         =					RobertaSeriesConfig(
           hidden_size=32		,			project_dim=32		,			intermediate_size=37		,			layer_norm_eps=1e-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=5002		,			)
       # TODO: remove after fixing the non-deterministic text encoder
       snake_case					:   int         =					RobertaSeriesModelWithTransformation(lowerCamelCase__		)
       snake_case					:   str         =					text_encoder
       snake_case					:   str         =					AltDiffusionPipeline(**lowerCamelCase__		)
       snake_case					:   Optional[Any]         =					alt_pipe.to(lowerCamelCase__		)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

       snake_case					:   List[Any]         =					self.get_dummy_inputs(lowerCamelCase__		)
       snake_case					:   Tuple         =					alt_pipe(**lowerCamelCase__		)
       snake_case					:   int         =					output.images
       snake_case					:   Tuple         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case					:   List[Any]         =					np.array(
           [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]		)

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







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







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   int         =					AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"		,			safety_checker=lowerCamelCase__		)
       snake_case					:   Union[str, Any]         =					alt_pipe.to(lowerCamelCase__		)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

       snake_case					:   Any         =					'''A painting of a squirrel eating a burger'''
       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Optional[Any]         =					alt_pipe([prompt]		,			generator=lowerCamelCase__		,			guidance_scale=6.0		,			num_inference_steps=20		,			output_type="np"		)

       snake_case					:   Union[str, Any]         =					output.images

       snake_case					:   Tuple         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case					:   int         =					np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]		)

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







   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Any         =					DDIMScheduler.from_pretrained("BAAI/AltDiffusion"		,			subfolder="scheduler"		)

       snake_case					:   Optional[int]         =					AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"		,			scheduler=lowerCamelCase__		,			safety_checker=lowerCamelCase__		)
       snake_case					:   Dict         =					alt_pipe.to(lowerCamelCase__		)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

       snake_case					:   Optional[int]         =					'''A painting of a squirrel eating a burger'''
       snake_case					:   Dict         =					torch.manual_seed(0		)

       snake_case					:   List[str]         =					alt_pipe([prompt]		,			generator=lowerCamelCase__		,			num_inference_steps=2		,			output_type="numpy"		)
       snake_case					:   Optional[int]         =					output.images

       snake_case					:   Any         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       snake_case					:   str         =					np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]		)

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









"""simple docstring"""



from __future__ import annotations

__snake_case         =					list[tuple[int, int]]

__snake_case         =					[
    [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],
]

__snake_case         =					([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			)			->	str:

       '''simple docstring'''




       snake_case					:   int         =					pos_x
       snake_case					:   List[str]         =					pos_y
       snake_case					:   List[Any]         =					(pos_y, pos_x)
       snake_case					:   Optional[int]         =					goal_x
       snake_case					:   Dict         =					goal_y
       snake_case					:   Any         =					g_cost
       snake_case					:   List[Any]         =					parent
       snake_case					:   Union[str, Any]         =					self.calculate_heuristic()







   def 				lowerCamelCase	(			self		)			->	float:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					abs(self.pos_x - self.goal_x		)
       snake_case					:   Dict         =					abs(self.pos_y - self.goal_y		)
       return dx + dy







   def __lt__(			self		,			UpperCamelCase__		)			->	bool:

       '''simple docstring'''




       return self.f_cost < other.f_cost







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   int         =					Node(start[1]		,			start[0]		,			goal[1]		,			goal[0]		,			0		,			UpperCamelCase__		)
       snake_case					:   List[Any]         =					Node(goal[1]		,			goal[0]		,			goal[1]		,			goal[0]		,			9_9999		,			UpperCamelCase__		)

       snake_case					:   Tuple         =					[self.start]
       snake_case					:   list[Node]         =					[]

       snake_case					:   Dict         =					False







   def 				lowerCamelCase	(			self		)			->	Path | None:

       '''simple docstring'''




       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           snake_case					:   str         =					self.open_nodes.pop(0		)

           if current_node.pos == self.target.pos:
               snake_case					:   Tuple         =					True
               return self.retrace_path(UpperCamelCase__		)

           self.closed_nodes.append(UpperCamelCase__		)
           snake_case					:   Optional[Any]         =					self.get_successors(UpperCamelCase__		)

           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(UpperCamelCase__		)
               else:
                   # retrieve the best current path
                   snake_case					:   Dict         =					self.open_nodes.pop(self.open_nodes.index(UpperCamelCase__		)		)

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

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	list[Node]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]
       for action in delta:
           snake_case					:   Union[str, Any]         =					parent.pos_x + action[1]
           snake_case					:   str         =					parent.pos_y + action[0]

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

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

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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Path:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					node
       snake_case					:   int         =					[]
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)		)
           snake_case					:   Any         =					current_node.parent
       path.reverse()
       return path


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

  print("""------""")

  __snake_case         =					GreedyBestFirst(init, goal)
  __snake_case         =					greedy_bf.search()
  if path:
    for pos_x, pos_y in path:
      __snake_case         =					2

    for elem in grid:
      print(elem)





