


"""simple docstring"""



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: int													=						KandinskyVaaPriorPipeline
   __UpperCAmelCase				: List[str]													=						['''prompt''']
   __UpperCAmelCase				: List[str]													=						['''prompt''', '''negative_prompt''']
   __UpperCAmelCase				: Union[str, Any]													=						[
       '''num_images_per_prompt''',
       '''generator''',
       '''num_inference_steps''',
       '''latents''',
       '''negative_prompt''',
       '''guidance_scale''',
       '''output_type''',
       '''return_dict''',
   ]
   __UpperCAmelCase				: int													=						False







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

       '''simple docstring'''




       return 32







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

       '''simple docstring'''




       return 32







   @property
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       return self.time_input_dim







   @property
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       return self.time_input_dim * 4







   @property
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       return 100







   @property
   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Dict         =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
       return tokenizer







   @property
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   List[Any]         =					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=1000		,			)
       return CLIPTextModelWithProjection(UpperCamelCase__		)







   @property
   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       torch.manual_seed(0		)

       snake_case					:   Any         =					{
           "num_attention_heads": 2,
           "attention_head_dim": 12,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }

       snake_case					:   Any         =					PriorTransformer(**UpperCamelCase__		)
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       snake_case					:   Optional[Any]         =					nn.Parameter(torch.ones(model.clip_std.shape		)		)
       return model







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Optional[int]         =					CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size		,			image_size=224		,			projection_dim=self.text_embedder_hidden_size		,			intermediate_size=37		,			num_attention_heads=4		,			num_channels=3		,			num_hidden_layers=5		,			patch_size=14		,			)

       snake_case					:   Dict         =					CLIPVisionModelWithProjection(UpperCamelCase__		)
       return model







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

       '''simple docstring'''




       snake_case					:   Any         =					CLIPImageProcessor(
           crop_size=224		,			do_center_crop=UpperCamelCase__		,			do_normalize=UpperCamelCase__		,			do_resize=UpperCamelCase__		,			image_mean=[0.48145466, 0.4578275, 0.40821073]		,			image_std=[0.26862954, 0.26130258, 0.27577711]		,			resample=3		,			size=224		,			)

       return image_processor







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					self.dummy_prior
       snake_case					:   Optional[Any]         =					self.dummy_image_encoder
       snake_case					:   List[str]         =					self.dummy_text_encoder
       snake_case					:   List[str]         =					self.dummy_tokenizer
       snake_case					:   List[Any]         =					self.dummy_image_processor

       snake_case					:   Optional[Any]         =					UnCLIPScheduler(
           variance_type="fixed_small_log"		,			prediction_type="sample"		,			num_train_timesteps=1000		,			clip_sample=UpperCamelCase__		,			clip_sample_range=10.0		,			)

       snake_case					:   Union[str, Any]         =					{
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }

       return components







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

       '''simple docstring'''




       if str(UpperCamelCase__		).startswith("mps"		):
           snake_case					:   Optional[Any]         =					torch.manual_seed(UpperCamelCase__		)
       else:
           snake_case					:   Any         =					torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
       snake_case					:   List[str]         =					{
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs







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

       '''simple docstring'''




       snake_case					:   List[str]         =					"cpu"

       snake_case					:   List[str]         =					self.get_dummy_components()

       snake_case					:   Optional[int]         =					self.pipeline_class(**UpperCamelCase__		)
       snake_case					:   str         =					pipe.to(UpperCamelCase__		)

       pipe.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   Any         =					pipe(**self.get_dummy_inputs(UpperCamelCase__		)		)
       snake_case					:   str         =					output.image_embeds

       snake_case					:   List[str]         =					pipe(
           **self.get_dummy_inputs(UpperCamelCase__		)		,			return_dict=UpperCamelCase__		,			)[0]

       snake_case					:   Any         =					image[0, -10:]
       snake_case					:   Optional[int]         =					image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       snake_case					:   List[Any]         =					np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]		)

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







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

       '''simple docstring'''




       snake_case					:   List[str]         =					torch_device == "cpu"
       snake_case					:   Union[str, Any]         =					True
       snake_case					:   Tuple         =					False

       self._test_inference_batch_single_identical(
           test_max_difference=UpperCamelCase__		,			relax_max_difference=UpperCamelCase__		,			test_mean_pixel_difference=UpperCamelCase__		,			)







   @skip_mps
   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Any         =					torch_device == "cpu"
       snake_case					:   Optional[Any]         =					False

       self._test_attention_slicing_forward_pass(
           test_max_difference=UpperCamelCase__		,			test_mean_pixel_difference=UpperCamelCase__		,			)









"""simple docstring"""



import numpy as np







class _lowerCAmelCase       :







   def __init__(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					(0, 0)
       snake_case					:   str         =					None
       snake_case					:   int         =					0
       snake_case					:   Optional[Any]         =					0
       snake_case					:   Tuple         =					0







   def __eq__(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return self.position == cell.position







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       print(self.position		)







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__=(5, 5)		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					np.zeros(UpperCamelCase__		)
       snake_case					:   str         =					world_size[0]
       snake_case					:   str         =					world_size[1]







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       print(self.w		)







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

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[
           (-1, -1),
           (-1, 0),
           (-1, 1),
           (0, -1),
           (0, 1),
           (1, -1),
           (1, 0),
           (1, 1),
       ]
       snake_case					:   Tuple         =					cell.position[0]
       snake_case					:   str         =					cell.position[1]
       snake_case					:   List[Any]         =					[]
       for n in neughbour_cord:
           snake_case					:   Any         =					current_x + n[0]
           snake_case					:   List[str]         =					current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
               snake_case					:   Tuple         =					Cell()
               snake_case					:   int         =					(x, y)
               snake_case					:   List[Any]         =					cell
               neighbours.append(UpperCamelCase__		)
       return neighbours







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]		,							lowercase						:						Union[str, Any]				)			->		Any:


    """simple docstring"""





    snake_case					:   Tuple         =					[]
    snake_case					:   List[Any]         =					[]
    _open.append(lowercase				)

    while _open:
        snake_case					:   Union[str, Any]         =					np.argmin([n.f for n in _open]				)
        snake_case					:   Tuple         =					_open[min_f]
        _closed.append(_open.pop(lowercase				)				)
        if current == goal:
            break
        for n in world.get_neigbours(lowercase				):
            for c in _closed:
                if c == n:
                    continue
            snake_case					:   int         =					current.g + 1
            snake_case      ,snake_case					:   Any         =					n.position
            snake_case      ,snake_case					:   Any         =					goal.position
            snake_case					:   int         =					(ya - ya) ** 2 + (xa - xa) ** 2
            snake_case					:   List[Any]         =					n.h + n.g

            for c in _open:
                if c == n and c.f < n.f:
                    continue
            _open.append(lowercase				)
    snake_case					:   str         =					[]
    while current.parent is not None:
        path.append(current.position				)
        snake_case					:   List[str]         =					current.parent
    path.append(current.position				)
    return path[::-1]


if __name__ == "__main__":
  __snake_case         =					Gridworld()
  # Start position and goal
  __snake_case         =					Cell()
  __snake_case         =					(0, 0)
  __snake_case         =					Cell()
  __snake_case         =					(4, 4)
  print(F'''path from {start.position} to {goal.position}''')
  __snake_case         =					astar(world, start, goal)
  #   Just for visual reasons.
  for i in s:
    __snake_case         =					1
  print(world.w)





