
'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class  A				:
     __magic_name__						=		42

     # setable values
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		None





     @classmethod
     def 				__lowerCAmelCase							(							cls						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         return cls(common=_UpperCamelCase						,			init_noise_sigma=_UpperCamelCase						,			timesteps=_UpperCamelCase	)





@dataclass
class  A				(							_lowerCamelCase						):
     __magic_name__						=		42





class  A				(							_lowerCamelCase    ,			_lowerCamelCase						):
     __magic_name__						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

     __magic_name__						=		42





     @property
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return True





     @register_to_config
     def __init__(							self						,			SCREAMING_SNAKE_CASE = 1000						,			SCREAMING_SNAKE_CASE = 0.0_001						,			SCREAMING_SNAKE_CASE = 0.02						,			SCREAMING_SNAKE_CASE = "linear"						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = "fixed_small"						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = "epsilon"						,			SCREAMING_SNAKE_CASE = jnp.floataa						,			) ->				List[Any]:






         """simple docstring"""






         A       :   Tuple					=						dtype





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE = None	) ->				DDPMSchedulerState:






         """simple docstring"""






         if common is None:
             A       :   int					=						CommonSchedulerState.create(self	)

         # standard deviation of the initial noise distribution
         A       :   Any					=						jnp.array(1.0						,			dtype=self.dtype	)

         A       :   List[Any]					=						jnp.arange(0						,			self.config.num_train_timesteps	).round()[::-1]

         return DDPMSchedulerState.create(
             common=_UpperCamelCase						,			init_noise_sigma=_UpperCamelCase						,			timesteps=_UpperCamelCase						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				jnp.ndarray:






         """simple docstring"""






         return sample





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = ()	) ->				DDPMSchedulerState:






         """simple docstring"""






         A       :   int					=						self.config.num_train_timesteps // num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # rounding to avoid issues when num_inference_step is power of 3
         A       :   int					=						(jnp.arange(0						,			_UpperCamelCase	) * step_ratio).round()[::-1]

         return state.replace(
             num_inference_steps=_UpperCamelCase						,			timesteps=_UpperCamelCase						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None	) ->				Optional[Any]:






         """simple docstring"""






         A       :   List[str]					=						state.common.alphas_cumprod[t]
         A       :   Optional[Any]					=						jnp.where(t > 0						,			state.common.alphas_cumprod[t - 1]						,			jnp.array(1.0						,			dtype=self.dtype	)	)

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         A       :   Any					=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

         if variance_type is None:
             A       :   Any					=						self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small":
             A       :   List[str]					=						jnp.clip(_UpperCamelCase						,			a_min=1e-20	)
         # for rl-diffuser https://arxiv.org/abs/2205.09991
         elif variance_type == "fixed_small_log":
             A       :   Tuple					=						jnp.log(jnp.clip(_UpperCamelCase						,			a_min=1e-20	)	)
         elif variance_type == "fixed_large":
             A       :   str					=						state.common.betas[t]
         elif variance_type == "fixed_large_log":
             # Glide max_log
             A       :   Dict					=						jnp.log(state.common.betas[t]	)
         elif variance_type == "learned":
             return predicted_variance
         elif variance_type == "learned_range":
             A       :   Any					=						variance
             A       :   Optional[Any]					=						state.common.betas[t]
             A       :   Dict					=						(predicted_variance + 1) / 2
             A       :   Any					=						frac * max_log + (1 - frac) * min_log

         return variance





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = True						,			) ->				Union[FlaxDDPMSchedulerOutput, Tuple]:






         """simple docstring"""






         A       :   str					=						timestep

         if key is None:
             A       :   Optional[Any]					=						jax.random.PRNGKey(0	)

         if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
             A,  A       :   Tuple					=						jnp.split(_UpperCamelCase						,			sample.shape[1]						,			axis=1	)
         else:
             A       :   List[str]					=						None

         # 1. compute alphas, betas
         A       :   Dict					=						state.common.alphas_cumprod[t]
         A       :   Optional[Any]					=						jnp.where(t > 0						,			state.common.alphas_cumprod[t - 1]						,			jnp.array(1.0						,			dtype=self.dtype	)	)
         A       :   List[str]					=						1 - alpha_prod_t
         A       :   Tuple					=						1 - alpha_prod_t_prev

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
             A       :   Union[str, Any]					=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
             A       :   List[str]					=						model_output
         elif self.config.prediction_type == "v_prediction":
             A       :   Any					=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         else:
             raise ValueError(
                 F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
                 ''' for the FlaxDDPMScheduler.'''	)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
             A       :   Dict					=						jnp.clip(_UpperCamelCase						,			-1						,			1	)

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         A       :   Tuple					=						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
         A       :   Optional[Any]					=						state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         A       :   str					=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         def random_variance():
             A       :   str					=						jax.random.split(_UpperCamelCase						,			num=1	)
             A       :   Any					=						jax.random.normal(_UpperCamelCase						,			shape=model_output.shape						,			dtype=self.dtype	)
             return (self._get_variance(_UpperCamelCase						,			_UpperCamelCase						,			predicted_variance=_UpperCamelCase	) ** 0.5) * noise

         A       :   Any					=						jnp.where(t > 0						,			random_variance()						,			jnp.zeros(model_output.shape						,			dtype=self.dtype	)	)

         A       :   Dict					=						pred_prev_sample + variance

         if not return_dict:
             return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=_UpperCamelCase						,			state=_UpperCamelCase	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				jnp.ndarray:






         """simple docstring"""






         return add_noise_common(state.common						,			_UpperCamelCase						,			_UpperCamelCase						,			_UpperCamelCase	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				jnp.ndarray:






         """simple docstring"""






         return get_velocity_common(state.common						,			_UpperCamelCase						,			_UpperCamelCase						,			_UpperCamelCase	)





     def __len__(							self	) ->				Any:






         """simple docstring"""






         return self.config.num_train_timesteps











import heapq
import sys

import numpy as np

snake_case__					:							Tuple													=     tuple[int, int]





class A_					:




   def __init__(self	:Union[str, Any] )->					Union[str, Any]:
         __A          =				[]
         __A          =				set()




   def _lowerCAmelCase      (self	:List[str] )->					str:
         if not self.empty():
               return self.elements[0][0]
         else:
               return float('''inf''' )




   def _lowerCAmelCase      (self	:Any )->					Any:
         return len(self.elements ) == 0




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Optional[int] )->					Optional[int]:
         if item not in self.set:
               heapq.heappush(self.elements    ,   (priority, item) )
               self.set.add(_UpperCamelCase )
         else:
               # update
               # print("update", item)
               __A          =				[]
               ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               while x != item:
                     temp.append((pri, x) )
                     ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               temp.append((priority, item) )
               for pro, xxx in temp:
                     heapq.heappush(self.elements    ,   (pro, xxx) )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:int )->					int:
         if item in self.set:
               self.set.remove(_UpperCamelCase )
               __A          =				[]
               ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               while x != item:
                     temp.append((pro, x) )
                     ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
               for prito, yyy in temp:
                     heapq.heappush(self.elements    ,   (prito, yyy) )




   def _lowerCAmelCase      (self	:Optional[Any] )->					int:
         return self.elements[0][1]






   def _lowerCAmelCase      (self	:List[str] )->					List[Any]:
         ((__A)   ,						(__A))          =				heapq.heappop(self.elements )
         self.set.remove(_UpperCamelCase )
         return (priority, item)





def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						Any:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      __A          =				np.array(lowerCamelCase )
      return np.linalg.norm(a - b )



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						List[str]:







      '''simple docstring'''






      return consistent_heuristic(lowerCamelCase					,				lowerCamelCase ) // t



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos )	->						Any:







      '''simple docstring'''






      return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   int					,				lowerCamelCase:   TPos					,				lowerCamelCase:   dict[TPos, float] )	->						Tuple:







      '''simple docstring'''






      __A          =				g_function[start] + Wa * heuristics[i](lowerCamelCase					,				lowerCamelCase )
      return ans



def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				np.chararray((n, n) )
      for i in range(lowerCamelCase ):
            for j in range(lowerCamelCase ):
                  __A          =				'''*'''

      for i in range(lowerCamelCase ):
            for j in range(lowerCamelCase ):
                  if (j, (n - 1) - i) in blocks:
                        __A          =				'''#'''

      __A          =				'''-'''
      __A          =				back_pointer[goal]
      while x != start:
            ((__A)   ,						(__A))          =				x
            # print(x)
            __A          =				'''-'''
            __A          =				back_pointer[x]
      __A          =				'''-'''

      for i in range(lowerCamelCase ):
            for j in range(lowerCamelCase ):
                  if (i, j) == (0, n - 1):
                        print(grid[i][j]					,				end=''' ''' )
                        print('''<-- End position'''					,				end=''' ''' )
                  else:
                        print(grid[i][j]					,				end=''' ''' )
            print()
      print('''^''' )
      print('''Start position''' )
      print()
      print('''# is an obstacle''' )
      print('''- is the path taken by algorithm''' )
      print('''PATH TAKEN BY THE ALGORITHM IS:-''' )
      __A          =				back_pointer[goal]
      while x != start:
            print(lowerCamelCase					,				end=''' ''' )
            __A          =				back_pointer[x]
      print(lowerCamelCase )
      sys.exit()



def _a				(	lowerCamelCase:   TPos )	->						Optional[Any]:







      '''simple docstring'''






      if p[0] < 0 or p[0] > n - 1:
            return False
      if p[1] < 0 or p[1] > n - 1:
            return False
      return True



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Tuple					,				lowerCamelCase:   Tuple					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   str					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Dict					,				)	->						Dict:







      '''simple docstring'''






      for itera in range(lowerCamelCase ):
            open_list[itera].remove_element(lowerCamelCase )
      # print("s", s)
      # print("j", j)
      ((__A)   ,						(__A))          =				s
      __A          =				(x - 1, y)
      __A          =				(x + 1, y)
      __A          =				(x, y + 1)
      __A          =				(x, y - 1)

      for neighbours in [left, right, up, down]:
            if neighbours not in blocks:
                  if valid(lowerCamelCase ) and neighbours not in visited:
                        # print("neighbour", neighbours)
                        visited.add(lowerCamelCase )
                        __A          =				-1
                        __A          =				float('''inf''' )

                  if valid(lowerCamelCase ) and g_function[neighbours] > g_function[s] + 1:
                        __A          =				g_function[s] + 1
                        __A          =				s
                        if neighbours not in close_list_anchor:
                              open_list[0].put(lowerCamelCase					,				key(lowerCamelCase					,				0					,				lowerCamelCase					,				lowerCamelCase ) )
                              if neighbours not in close_list_inad:
                                    for var in range(1					,				lowerCamelCase ):
                                          if key(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ) <= Wa * key(
                                              lowerCamelCase					,				0					,				lowerCamelCase					,				lowerCamelCase ):
                                                open_list[j].put(
                                                    lowerCamelCase					,				key(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ) )



def _a				(	)	->						str:







      '''simple docstring'''






      __A          =				[]
      for x in range(1					,				5 ):
            for y in range(1					,				6 ):
                  some_list.append((x, y) )

      for x in range(15					,				20 ):
            some_list.append((x, 17) )

      for x in range(10					,				19 ):
            for y in range(1					,				15 ):
                  some_list.append((x, y) )

    # L block
      for x in range(1					,				4 ):
            for y in range(12					,				19 ):
                  some_list.append((x, y) )
      for x in range(3					,				13 ):
            for y in range(16					,				19 ):
                  some_list.append((x, y) )
      return some_list


snake_case__					:							Union[str, Any]													=     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

snake_case__					:							List[str]													=     [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
snake_case__					:							List[str]													=     make_common_ground()


snake_case__					:							int													=     blocks_blk
# hyper parameters
snake_case__					:							Optional[int]													=     1
snake_case__					:							List[Any]													=     1
snake_case__					:							int													=     20
snake_case__					:							Optional[int]													=     3  # one consistent and two other inconsistent

# start and end destination
snake_case__					:							Tuple													=     (0, 0)
snake_case__					:							Any													=     (n - 1, n - 1)

snake_case__					:							List[Any]													=     1



def _a				(	lowerCamelCase:   TPos					,				lowerCamelCase:   TPos					,				lowerCamelCase:   int )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				{start: 0, goal: float('''inf''' )}
      __A          =				{start: -1, goal: -1}
      __A          =				[]
      __A          =				set()

      for i in range(lowerCamelCase ):
            open_list.append(PriorityQueue() )
            open_list[i].put(lowerCamelCase					,				key(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ) )

      __A          =				[]
      __A          =				[]
      while open_list[0].minkey() < float('''inf''' ):
            for i in range(1					,				lowerCamelCase ):
                  # print(open_list[0].minkey(), open_list[i].minkey())
                  if open_list[i].minkey() <= Wa * open_list[0].minkey():
                        global t
                        t += 1
                        if g_function[goal] <= open_list[i].minkey():
                              if g_function[goal] < float('''inf''' ):
                                    do_something(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        else:
                              __A   ,						__A          =				open_list[i].top_show()
                              visited.add(lowerCamelCase )
                              expand_state(
                                  lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				)
                              close_list_inad.append(lowerCamelCase )
                  else:
                        if g_function[goal] <= open_list[0].minkey():
                              if g_function[goal] < float('''inf''' ):
                                    do_something(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        else:
                              __A          =				open_list[0].top_show()
                              visited.add(lowerCamelCase )
                              expand_state(
                                  lowerCamelCase					,				0					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				)
                              close_list_anchor.append(lowerCamelCase )
      print('''No path found to goal''' )
      print()
      for i in range(n - 1					,				-1					,				-1 ):
            for j in range(lowerCamelCase ):
                  if (j, i) in blocks:
                        print('''#'''					,				end=''' ''' )
                  elif (j, i) in back_pointer:
                        if (j, i) == (n - 1, n - 1):
                              print('''*'''					,				end=''' ''' )
                        else:
                              print('''-'''					,				end=''' ''' )
                  else:
                        print('''*'''					,				end=''' ''' )
                  if (j, i) == (n - 1, n - 1):
                        print('''<-- End position'''					,				end=''' ''' )
            print()
      print('''^''' )
      print('''Start position''' )
      print()
      print('''# is an obstacle''' )
      print('''- is the path taken by algorithm''' )


if __name__ == "__main__":
       multi_a_star(start, goal, n_heuristic)





