



"""simple docstring"""





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class    lowercase__     :







       '''simple docstring'''


       def   lowercase__       (							self    :						List[Any]					)				->		Optional[Any]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"					)

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"					)

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							UNetaDConditionModel(
                 sample_size=32	,       layers_per_block=1	,       block_out_channels=[32, 64]	,       down_block_types=[
                     "ResnetDownsampleBlock2D",
                     "SimpleCrossAttnDownBlock2D",
                 ]	,       mid_block_type="UNetMidBlock2DSimpleCrossAttn"	,       up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]	,       in_channels=3	,       out_channels=6	,       cross_attention_dim=32	,       encoder_hid_dim=32	,       attention_head_dim=8	,       addition_embed_type="text"	,       addition_embed_type_num_heads=2	,       cross_attention_norm="group_norm"	,       resnet_time_scale_shift="scale_shift"	,       act_fn="gelu"	,       )
             unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							DDPMScheduler(
                 num_train_timesteps=1000	,       beta_schedule="squaredcos_cap_v2"	,       beta_start=0.0001	,       beta_end=0.02	,       thresholding=_UpperCAmelCase	,       dynamic_thresholding_ratio=0.95	,       sample_max_value=1.0	,       prediction_type="epsilon"	,       variance_type="learned_range"	,       )

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							IFWatermarker()

             return {
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "watermarker": watermarker,
                 "safety_checker": None,
                 "feature_extractor": None,
             }



       def   lowercase__       (							self    :						Optional[Any]					)				->		List[str]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"					)

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"					)

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							UNetaDConditionModel(
                 sample_size=32	,       layers_per_block=[1, 2]	,       block_out_channels=[32, 64]	,       down_block_types=[
                     "ResnetDownsampleBlock2D",
                     "SimpleCrossAttnDownBlock2D",
                 ]	,       mid_block_type="UNetMidBlock2DSimpleCrossAttn"	,       up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]	,       in_channels=6	,       out_channels=6	,       cross_attention_dim=32	,       encoder_hid_dim=32	,       attention_head_dim=8	,       addition_embed_type="text"	,       addition_embed_type_num_heads=2	,       cross_attention_norm="group_norm"	,       resnet_time_scale_shift="scale_shift"	,       act_fn="gelu"	,       class_embed_type="timestep"	,       mid_block_scale_factor=1.414	,       time_embedding_act_fn="gelu"	,       time_embedding_dim=32	,       )
             unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							DDPMScheduler(
                 num_train_timesteps=1000	,       beta_schedule="squaredcos_cap_v2"	,       beta_start=0.0001	,       beta_end=0.02	,       thresholding=_UpperCAmelCase	,       dynamic_thresholding_ratio=0.95	,       sample_max_value=1.0	,       prediction_type="epsilon"	,       variance_type="learned_range"	,       )

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							DDPMScheduler(
                 num_train_timesteps=1000	,       beta_schedule="squaredcos_cap_v2"	,       beta_start=0.0001	,       beta_end=0.02	,       )

             torch.manual_seed(0					)
             UpperCAmelCase_    				=							IFWatermarker()

             return {
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "image_noising_scheduler": image_noising_scheduler,
                 "watermarker": watermarker,
                 "safety_checker": None,
                 "feature_extractor": None,
             }



       def   lowercase__       (							self    :						List[str]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.get_dummy_components()
             UpperCAmelCase_    				=							self.pipeline_class(**_UpperCAmelCase					)
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)

             UpperCAmelCase_    				=							inputs["prompt"]
             UpperCAmelCase_    				=							inputs["generator"]
             UpperCAmelCase_    				=							inputs["num_inference_steps"]
             UpperCAmelCase_    				=							inputs["output_type"]

             if "image" in inputs:
                   UpperCAmelCase_    				=							inputs["image"]
             else:
                   UpperCAmelCase_    				=							None

             if "mask_image" in inputs:
                   UpperCAmelCase_    				=							inputs["mask_image"]
             else:
                   UpperCAmelCase_    				=							None

             if "original_image" in inputs:
                   UpperCAmelCase_    				=							inputs["original_image"]
             else:
                   UpperCAmelCase_    				=							None

             UpperCAmelCase_				,					UpperCAmelCase_    				=							pipe.encode_prompt(_UpperCAmelCase					)

             # inputs with prompt converted to embeddings
             UpperCAmelCase_    				=							{
                 "prompt_embeds": prompt_embeds,
                 "negative_prompt_embeds": negative_prompt_embeds,
                 "generator": generator,
                 "num_inference_steps": num_inference_steps,
                 "output_type": output_type,
             }

             if image is not None:
                   UpperCAmelCase_    				=							image

             if mask_image is not None:
                   UpperCAmelCase_    				=							mask_image

             if original_image is not None:
                   UpperCAmelCase_    				=							original_image

             # set all optional components to None
             for optional_component in pipe._optional_components:
                   setattr(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)

             UpperCAmelCase_    				=							pipe(**_UpperCAmelCase					)[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							self.pipeline_class.from_pretrained(_UpperCAmelCase					)
                   pipe_loaded.to(_UpperCAmelCase					)
                   pipe_loaded.set_progress_bar_config(disable=_UpperCAmelCase					)

             pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

             for optional_component in pipe._optional_components:
                   self.assertTrue(
                       getattr(_UpperCAmelCase	,       _UpperCAmelCase					) is None	,       F"""`{optional_component}` did not stay set to None after loading."""	,       )

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)

             UpperCAmelCase_    				=							inputs["generator"]
             UpperCAmelCase_    				=							inputs["num_inference_steps"]
             UpperCAmelCase_    				=							inputs["output_type"]

             # inputs with prompt converted to embeddings
             UpperCAmelCase_    				=							{
                 "prompt_embeds": prompt_embeds,
                 "negative_prompt_embeds": negative_prompt_embeds,
                 "generator": generator,
                 "num_inference_steps": num_inference_steps,
                 "output_type": output_type,
             }

             if image is not None:
                   UpperCAmelCase_    				=							image

             if mask_image is not None:
                   UpperCAmelCase_    				=							mask_image

             if original_image is not None:
                   UpperCAmelCase_    				=							original_image

             UpperCAmelCase_    				=							pipe_loaded(**_UpperCAmelCase					)[0]

             UpperCAmelCase_    				=							np.abs(to_np(_UpperCAmelCase					) - to_np(_UpperCAmelCase					)					).max()
             self.assertLess(_UpperCAmelCase	,       1e-4					)







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


             '''simple docstring'''




             UpperCAmelCase_    				=							self.get_dummy_components()
             UpperCAmelCase_    				=							self.pipeline_class(**_UpperCAmelCase					)
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)
             UpperCAmelCase_    				=							pipe(**_UpperCAmelCase					)[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							self.pipeline_class.from_pretrained(_UpperCAmelCase					)
                   pipe_loaded.to(_UpperCAmelCase					)
                   pipe_loaded.set_progress_bar_config(disable=_UpperCAmelCase					)

             pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

             UpperCAmelCase_    				=							self.get_dummy_inputs(_UpperCAmelCase					)
             UpperCAmelCase_    				=							pipe_loaded(**_UpperCAmelCase					)[0]

             UpperCAmelCase_    				=							np.abs(to_np(_UpperCAmelCase					) - to_np(_UpperCAmelCase					)					).max()
             self.assertLess(_UpperCAmelCase	,       1e-4					)









"""simple docstring"""





from heapq import heappop, heappush

import numpy as np
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							grid.shape
      UpperCAmelCase_    				=							[-1, 1, 0, 0]
      UpperCAmelCase_    				=							[0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      UpperCAmelCase_				,					UpperCAmelCase_    				=							[(0, source)], set()
      UpperCAmelCase_    				=							np.full((rows, cols)			,    np.inf   )
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							np.empty((rows, cols)			,    dtype=lowerCAmelCase__   )
      UpperCAmelCase_    				=							None

      while queue:
            ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heappop(lowerCAmelCase__   )
            if (x, y) in visited:
                  continue
            visited.add((x, y)   )

            if (x, y) == destination:
                  UpperCAmelCase_    				=							[]
                  while (x, y) != source:
                        path.append((x, y)   )
                        UpperCAmelCase_				,					UpperCAmelCase_    				=							predecessors[x, y]
                  path.append(lowerCAmelCase__   )  # add the source manually
                  path.reverse()
                  return matrix[destination], path

            for i in range(len(lowerCAmelCase__   )   ):
                  UpperCAmelCase_				,					UpperCAmelCase_    				=							x + dx[i], y + dy[i]
                  if 0 <= nx < rows and 0 <= ny < cols:
                        UpperCAmelCase_    				=							grid[nx][ny]
                        if next_node == 1 and matrix[nx, ny] > dist + 1:
                              heappush(lowerCAmelCase__			,    (dist + 1, (nx, ny))   )
                              UpperCAmelCase_    				=							dist + 1
                              UpperCAmelCase_    				=							(x, y)

      return np.inf, []


if __name__ == "__main__":
  import doctest

  doctest.testmod()




