



from __future__ import annotations

snake_case__					:     Tuple    		=   [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	):
	__lowercase		       =			[
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(_SCREAMING_SNAKE_CASE   )   )
	]  # the reference grid
	__lowercase		       =			1
	__lowercase		       =			[
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(_SCREAMING_SNAKE_CASE   )   )
	]  # the action grid

	__lowercase		       =			init[0]
	__lowercase		       =			init[1]
	__lowercase		       =			0
	__lowercase		       =			g + heuristic[x][y]  # cost from starting cell to destination cell
	__lowercase		       =			[[f, g, x, y]]

	__lowercase		       =			False  # flag that is set when search is complete
	__lowercase		       =			False  # flag set if we can't find expand

	while not found and not resign:
		if len(_SCREAMING_SNAKE_CASE   ) == 0:
			raise ValueError("Algorithm is unable to find solution"   )
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			__lowercase		       =			cell.pop()
			__lowercase		       =			next_cell[2]
			__lowercase		       =			next_cell[3]
			__lowercase		       =			next_cell[1]

			if x == goal[0] and y == goal[1]:
				__lowercase		       =			True
			else:
				for i in range(len(_SCREAMING_SNAKE_CASE   )   ):  # to try out different valid actions
					__lowercase		       =			x + DIRECTIONS[i][0]
					__lowercase		       =			y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE   ) and ya >= 0 and ya < len(grid[0]   ):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							__lowercase		       =			g + cost
							__lowercase		       =			ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]   )
							__lowercase		       =			1
							__lowercase		       =			i
	__lowercase		       =			[]
	__lowercase		       =			goal[0]
	__lowercase		       =			goal[1]
	invpath.append([x, y]   )  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		__lowercase		       =			x - DIRECTIONS[action[x][y]][0]
		__lowercase		       =			y - DIRECTIONS[action[x][y]][1]
		__lowercase		       =			xa
		__lowercase		       =			ya
		invpath.append([x, y]   )

	__lowercase		       =			[]
	for i in range(len(_SCREAMING_SNAKE_CASE   )   ):
		path.append(invpath[len(_SCREAMING_SNAKE_CASE   ) - 1 - i]   )
	return path, action


if __name__ == "__main__":
	snake_case__					:     List[str]    		=   [
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 1, 0],
	    [0, 0, 0, 0, 1, 0],
	]

	snake_case__					:     Optional[int]    		=   [0, 0]
	# all coordinates are given in format [y,x]
	snake_case__					:     Union[str, Any]    		=   [len(grid) - 1, len(grid[0]) - 1]
	snake_case__					:     Dict    		=   1

	# the cost map which pushes the path closer to the goal
	snake_case__					:     Tuple    		=   [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
	for i in range(len(grid)):
		for j in range(len(grid[0])):
			snake_case__					:     Optional[int]    		=   abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				snake_case__					:     List[Any]    		=   99

	snake_case__		,       snake_case__					:     Union[str, Any]    		=   search(grid, init, goal, cost, heuristic)

	print("""ACTION MAP""")
	for i in range(len(action)):
		print(action[i])

	for i in range(len(path)):
		print(path[i])








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						@register_to_config
						def __init__(	self	:							Optional[Any]							,      *,
						    lowerCamelCase	:							int = 4							,      lowerCamelCase	:							int = 768							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



							super().__init__()

							__lowercase		       =			nn.Parameter(torch.zeros(lowerCamelCase	)	)

							# parameters for additional clip time embeddings
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)

							# parameters for encoder hidden states
							__lowercase		       =			clip_extra_context_tokens
							__lowercase		       =			nn.Linear(
							    lowerCamelCase							,      self.clip_extra_context_tokens * cross_attention_dim	)
							__lowercase		       =			nn.Linear(lowerCamelCase							,      lowerCamelCase	)
							__lowercase		       =			nn.LayerNorm(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      *, lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								__lowercase		       =			image_embeddings.shape[0]
								__lowercase		       =			self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
								__lowercase		       =			classifier_free_guidance_embeddings.expand(
								    lowerCamelCase							,      -1	)
								__lowercase		       =			torch.cat([classifier_free_guidance_embeddings, image_embeddings]							,      dim=0	)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase		       =			prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase		       =			self.embedding_proj(lowerCamelCase	)
							__lowercase		       =			self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase	)
							__lowercase		       =			time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase		       =			self.clip_extra_context_tokens_proj(lowerCamelCase	)
							__lowercase		       =			clip_extra_context_tokens.reshape(lowerCamelCase							,      -1							,      self.clip_extra_context_tokens	)
							__lowercase		       =			clip_extra_context_tokens.permute(0							,      2							,      1	)

							__lowercase		       =			self.encoder_hidden_states_proj(lowerCamelCase	)
							__lowercase		       =			self.text_encoder_hidden_states_norm(lowerCamelCase	)
							__lowercase		       =			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]							,      dim=1	)

							return text_encoder_hidden_states, additive_clip_time_embeddings



