


'''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  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=KandinskyVaaPriorPipeline
       UpperCAmelCase										=["prompt"]
       UpperCAmelCase										=["prompt", "negative_prompt"]
       UpperCAmelCase										=[
           "num_images_per_prompt",
           "generator",
           "num_inference_steps",
           "latents",
           "negative_prompt",
           "guidance_scale",
           "output_type",
           "return_dict",
       ]
       UpperCAmelCase										=False






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




          '''simple docstring'''
          return 3_2






       @property
       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          return 3_2






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




          '''simple docstring'''
          return self.time_input_dim






       @property
       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          return self.time_input_dim * 4






       @property
       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          return 1_0_0






       @property
       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
          return tokenizer






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




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase       :       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=3_7  ,							layer_norm_eps=1E-0_5  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1_0_0_0  ,							)
          return CLIPTextModelWithProjection(snake_case)






       @property
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          torch.manual_seed(0)

          _UpperCAmelCase       :       Tuple							   ={
              'num_attention_heads': 2,
              'attention_head_dim': 1_2,
              'embedding_dim': self.text_embedder_hidden_size,
              'num_layers': 1,
          }

          _UpperCAmelCase       :       Optional[int]							   =PriorTransformer(**snake_case)
          # 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
          _UpperCAmelCase       :       int							   =nn.Parameter(torch.ones(model.clip_std.shape))
          return model






       @property
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          torch.manual_seed(0)
          _UpperCAmelCase       :       Union[str, Any]							   =CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size  ,							image_size=2_2_4  ,							projection_dim=self.text_embedder_hidden_size  ,							intermediate_size=3_7  ,							num_attention_heads=4  ,							num_channels=3  ,							num_hidden_layers=5  ,							patch_size=1_4  ,							)

          _UpperCAmelCase       :       Optional[Any]							   =CLIPVisionModelWithProjection(snake_case)
          return model






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




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =CLIPImageProcessor(
              crop_size=2_2_4  ,							do_center_crop=snake_case  ,							do_normalize=snake_case  ,							do_resize=snake_case  ,							image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,							image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,							resample=3  ,							size=2_2_4  ,							)

          return image_processor






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =self.dummy_prior
          _UpperCAmelCase       :       List[Any]							   =self.dummy_image_encoder
          _UpperCAmelCase       :       Any							   =self.dummy_text_encoder
          _UpperCAmelCase       :       Optional[int]							   =self.dummy_tokenizer
          _UpperCAmelCase       :       List[Any]							   =self.dummy_image_processor

          _UpperCAmelCase       :       List[Any]							   =UnCLIPScheduler(
              variance_type='fixed_small_log'  ,							prediction_type='sample'  ,							num_train_timesteps=1_0_0_0  ,							clip_sample=snake_case  ,							clip_sample_range=10.0  ,							)

          _UpperCAmelCase       :       str							   ={
              'prior': prior,
              'image_encoder': image_encoder,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'scheduler': scheduler,
              'image_processor': image_processor,
          }

          return components






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=0)						->  str:




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






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   ='cpu'

          _UpperCAmelCase       :       Dict							   =self.get_dummy_components()

          _UpperCAmelCase       :       List[str]							   =self.pipeline_class(**snake_case)
          _UpperCAmelCase       :       Tuple							   =pipe.to(snake_case)

          pipe.set_progress_bar_config(disable=snake_case)

          _UpperCAmelCase       :       Optional[Any]							   =pipe(**self.get_dummy_inputs(snake_case))
          _UpperCAmelCase       :       int							   =output.image_embeds

          _UpperCAmelCase       :       Optional[int]							   =pipe(
              **self.get_dummy_inputs(snake_case)  ,							return_dict=snake_case  ,							)[0]

          _UpperCAmelCase       :       int							   =image[0, -1_0:]
          _UpperCAmelCase       :       Dict							   =image_from_tuple[0, -1_0:]

          assert image.shape == (1, 3_2)

          _UpperCAmelCase       :       List[Any]							   =np.array(
              [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56])

          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)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =torch_device == 'cpu'
          _UpperCAmelCase       :       Optional[Any]							   =True
          _UpperCAmelCase       :       Dict							   =False

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






       @skip_mps
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =torch_device == 'cpu'
          _UpperCAmelCase       :       Dict							   =False

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



'''simple docstring'''






import numpy as np






class  __magic_name__							:






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =(0, 0)
          _UpperCAmelCase       :       Any							   =None
          _UpperCAmelCase       :       List[str]							   =0
          _UpperCAmelCase       :       Dict							   =0
          _UpperCAmelCase       :       Dict							   =0






       def __eq__(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.position == cell.position






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          print(self.position)






class  __magic_name__							:






       def __init__(      self  ,							snake_case=(5, 5))						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =np.zeros(snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =world_size[0]
          _UpperCAmelCase       :       Any							   =world_size[1]






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          print(self.w)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Any:




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






def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =[]
   _UpperCAmelCase       :       Union[str, Any]							   =[]
   _open.append(__lowerCamelCase							)

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

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


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

