

'''simple docstring'''






import heapq
import sys

import numpy as np

lowerCAmelCase__      		=			tuple[int, int]






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Dict    ):
   __lowercase													=				[]
   __lowercase													=				set()






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   if not self.empty():
    return self.elements[0][0]
   else:
    return float('''inf'''    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return len(self.elements    ) == 0






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






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Tuple    ):
   if item in self.set:
    self.set.remove(lowercase__    )
    __lowercase													=				[]
    ((__lowercase)						,			(__lowercase))													=				heapq.heappop(self.elements    )
    while x != item:
     temp.append((pro, x)    )
     ((__lowercase)						,			(__lowercase))													=				heapq.heappop(self.elements    )
    for prito, yyy in temp:
     heapq.heappush(self.elements      ,(prito, yyy)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   return self.elements[0][1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   ((__lowercase)						,			(__lowercase))													=				heapq.heappop(self.elements    )
   self.set.remove(lowercase__    )
   return (priority, item)


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				np.array(A__   )
 __lowercase													=				np.array(A__   )
 return np.linalg.norm(a - b   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 return consistent_heuristic(A__  ,       A__   ) // t


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				g_function[start] + Wa * heuristics[i](A__  ,       A__   )
 return ans


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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

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

 for i in range(A__   ):
  for j in range(A__   ):
   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:-'''   )
 __lowercase													=				back_pointer[goal]
 while x != start:
  print(A__  ,       end=''' '''   )
  __lowercase													=				back_pointer[x]
 print(A__   )
 sys.exit()


def 	_A  (					A__   ):






 """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  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       ):






 """simple docstring"""



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

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

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


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				[]
 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


lowerCAmelCase__      		=			{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowerCAmelCase__      		=			[
    (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),
]
lowerCAmelCase__      		=			make_common_ground()


lowerCAmelCase__      		=			blocks_blk
# hyper parameters
lowerCAmelCase__      		=			1
lowerCAmelCase__      		=			1
lowerCAmelCase__      		=			20
lowerCAmelCase__      		=			3  # one consistent and two other inconsistent

# start and end destination
lowerCAmelCase__      		=			(0, 0)
lowerCAmelCase__      		=			(n - 1, n - 1)

lowerCAmelCase__      		=			1


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

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

 __lowercase													=				[]
 __lowercase													=				[]
 while open_list[0].minkey() < float('''inf'''   ):
  for i in range(1  ,       A__   ):
   # 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(A__  ,       A__  ,       A__   )
    else:
     __lowercase						,			__lowercase													=				open_list[i].top_show()
     visited.add(A__   )
     expand_state(
         A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       )
     close_list_inad.append(A__   )
   else:
    if g_function[goal] <= open_list[0].minkey():
     if g_function[goal] < float('''inf'''   ):
      do_something(A__  ,       A__  ,       A__   )
    else:
     __lowercase													=				open_list[0].top_show()
     visited.add(A__   )
     expand_state(
         A__  ,       0  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       )
     close_list_anchor.append(A__   )
 print('''No path found to goal'''   )
 print()
 for i in range(n - 1  ,       -1  ,       -1   ):
  for j in range(A__   ):
   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)



'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__      		=			False
lowerCAmelCase__      		=			True
lowerCAmelCase__      		=			False


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()

 parser.add_argument(
     '''--repo_path''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			{
     '''image_size''': '''sample_size''',
     '''num_res_blocks''': '''layers_per_block''',
     '''block_channels''': '''block_out_channels''',
     '''down_blocks''': '''down_block_types''',
     '''up_blocks''': '''up_block_types''',
     '''downscale_freq_shift''': '''freq_shift''',
     '''resnet_num_groups''': '''norm_num_groups''',
     '''resnet_act_fn''': '''act_fn''',
     '''resnet_eps''': '''norm_eps''',
     '''num_head_channels''': '''attention_head_dim''',
 }

 lowerCAmelCase__      		=			{
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 lowerCAmelCase__      		=			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

 with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
  lowerCAmelCase__      		=			reader.read()
  lowerCAmelCase__      		=			json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, '''config.json'''):
  lowerCAmelCase__      		=			UNetaDModel(**config)
 else:
  lowerCAmelCase__      		=			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  lowerCAmelCase__      		=			class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 lowerCAmelCase__      		=			dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    lowerCAmelCase__      		=			config[key]
    del config[key]

  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

 if do_only_weights:
  lowerCAmelCase__      		=			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

  lowerCAmelCase__      		=			{}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
    continue
   lowerCAmelCase__      		=			False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('''.''')[0] == key:
     lowerCAmelCase__      		=			param_value
     lowerCAmelCase__      		=			True
   if not has_changed:
    lowerCAmelCase__      		=			param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))

