
'''simple docstring'''




import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
      import bitsandbytes as bnb
      import torch
      import torch.nn as nn

      from ..pytorch_utils import ConvaD

if is_accelerate_available():
      from accelerate import init_empty_weights
      from accelerate.utils import find_tied_parameters

lowercase			:				Optional[int]    		=			logging.get_logger(__name__)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None						):




    '''simple docstring'''



    if "." in tensor_name:
        A       :   List[str]					=						tensor_name.split('''.'''						)
        for split in splits[:-1]:
            A       :   Tuple					=						getattr(snake_case__		,				snake_case__						)
            if new_module is None:
                raise ValueError(F'{module} has no attribute {split}.'						)
            A       :   Optional[int]					=						new_module
        A       :   List[str]					=						splits[-1]

    if tensor_name not in module._parameters and tensor_name not in module._buffers:
        raise ValueError(F'{module} does not have a parameter or a buffer named {tensor_name}.'						)
    A       :   Tuple					=						tensor_name in module._buffers
    A       :   int					=						getattr(snake_case__		,				snake_case__						)

    if old_value.device == torch.device('''meta'''						) and device not in ["meta", torch.device('''meta'''						)] and value is None:
        raise ValueError(F'{tensor_name} is on the meta device, we need a `value` to put in on {device}.'						)

    A       :   List[str]					=						False
    A       :   Tuple					=						False
    if is_buffer or not is_bitsandbytes_available():
        A       :   Union[str, Any]					=						False
        A       :   Optional[Any]					=						False
    else:
        A       :   Any					=						hasattr(bnb.nn		,				'''Params4bit'''						) and isinstance(module._parameters[tensor_name]		,				bnb.nn.Paramsabit						)
        A       :   Dict					=						isinstance(module._parameters[tensor_name]		,				bnb.nn.IntaParams						)

    if is_abit or is_abit:
        A       :   List[Any]					=						module._parameters[tensor_name]
        if param.device.type != "cuda":
            if value is None:
                A       :   Dict					=						old_value.to(snake_case__						)
            elif isinstance(snake_case__		,				torch.Tensor						):
                A       :   Optional[Any]					=						value.to('''cpu'''						)
                if value.dtype == torch.inta:
                    A       :   Tuple					=						version.parse(importlib.metadata.version('''bitsandbytes'''						)						) > version.parse(
                        '''0.37.2'''						)
                    if not is_abit_serializable:
                        raise ValueError(
                            '''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
                            '''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'''						)
            else:
                A       :   List[Any]					=						torch.tensor(snake_case__		,				device='''cpu'''						)

            # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
            # Since weights are saved in the correct "orientation", we skip transposing when loading.
            if issubclass(module.source_cls		,				snake_case__						) and fpaa_statistics is None:
                A       :   Optional[int]					=						new_value.T

            A       :   Optional[Any]					=						old_value.__dict__
            if is_abit:
                A       :   Optional[int]					=						bnb.nn.IntaParams(snake_case__		,				requires_grad=snake_case__		,				**snake_case__						).to(snake_case__						)
            elif is_abit:
                A       :   int					=						bnb.nn.Paramsabit(snake_case__		,				requires_grad=snake_case__		,				**snake_case__						).to(snake_case__						)

            A       :   Tuple					=						new_value
            if fpaa_statistics is not None:
                setattr(module.weight		,				'''SCB'''		,				fpaa_statistics.to(snake_case__						)						)

    else:
        if value is None:
            A       :   List[str]					=						old_value.to(snake_case__						)
        elif isinstance(snake_case__		,				torch.Tensor						):
            A       :   Optional[Any]					=						value.to(snake_case__						)
        else:
            A       :   Tuple					=						torch.tensor(snake_case__		,				device=snake_case__						)

        if is_buffer:
            A       :   str					=						new_value
        else:
            A       :   Optional[Any]					=						nn.Parameter(snake_case__		,				requires_grad=old_value.requires_grad						)
            A       :   Any					=						new_value







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None		,				snake_case__=False						):




    '''simple docstring'''



    for name, module in model.named_children():
        if current_key_name is None:
            A       :   List[Any]					=						[]
        current_key_name.append(snake_case__						)

        if (isinstance(snake_case__		,				nn.Linear						) or isinstance(snake_case__		,				snake_case__						)) and name not in modules_to_not_convert:
            # Check if the current key is not in the `modules_to_not_convert`
            if not any(key in '''.'''.join(snake_case__						) for key in modules_to_not_convert						):
                with init_empty_weights():
                    if isinstance(snake_case__		,				snake_case__						):
                        A,  A       :   Tuple					=						module.weight.shape
                    else:
                        A       :   int					=						module.in_features
                        A       :   Tuple					=						module.out_features

                    if quantization_config.quantization_method() == "llm_int8":
                        A       :   Any					=						bnb.nn.LinearabitLt(
                            snake_case__		,				snake_case__		,				module.bias is not None		,				has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight		,				threshold=quantization_config.llm_inta_threshold		,				)
                        A       :   Any					=						True
                    else:
                        if (
                            quantization_config.llm_inta_skip_modules is not None
                            and name in quantization_config.llm_inta_skip_modules
                        ):
                            pass
                        else:
                            A       :   int					=						bnb.nn.Linearabit(
                                snake_case__		,				snake_case__		,				module.bias is not None		,				quantization_config.bnb_abit_compute_dtype		,				compress_statistics=quantization_config.bnb_abit_use_double_quant		,				quant_type=quantization_config.bnb_abit_quant_type		,				)
                            A       :   str					=						True
                    # Store the module class in case we need to transpose the weight later
                    A       :   Tuple					=						type(snake_case__						)
                    # Force requires grad to False to avoid unexpected errors
                    model._modules[name].requires_grad_(snake_case__						)
        if len(list(module.children()						)						) > 0:
            A,  A       :   Any					=						_replace_with_bnb_linear(
                snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				has_been_replaced=snake_case__		,				)
        # Remove the last key for recursion
        current_key_name.pop(-1						)
    return model, has_been_replaced







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=None						):




    '''simple docstring'''



    A       :   str					=						['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
    A,  A       :   List[str]					=						_replace_with_bnb_linear(
        snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)

    if not has_been_replaced:
        logger.warning(
            '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
            ''' Please double check your model architecture, or submit an issue on github if you think this is'''
            ''' a bug.'''						)

    return model







def 		lowerCAmelCase_				(   *snake_case__		,				**snake_case__						):




    '''simple docstring'''



    warnings.warn(
        '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''		,				snake_case__		,				)
    return replace_with_bnb_linear(*snake_case__		,				**snake_case__						)







def 		lowerCAmelCase_				(   *snake_case__		,				**snake_case__						):




    '''simple docstring'''



    warnings.warn(
        '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'''		,				snake_case__		,				)
    return set_module_quantized_tensor_to_device(*snake_case__		,				**snake_case__						)







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Any					=						deepcopy(snake_case__						)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
    tied_model.tie_weights()

    A       :   Optional[Any]					=						find_tied_parameters(snake_case__						)
    # For compatibility with Accelerate < 0.18
    if isinstance(snake_case__		,				snake_case__						):
        A       :   Tuple					=						sum(list(tied_params.values()						)		,				[]						) + list(tied_params.keys()						)
    else:
        A       :   int					=						sum(snake_case__		,				[]						)
    A       :   Dict					=						len(snake_case__						) > 0

    # Check if it is a base model
    A       :   Dict					=						not hasattr(snake_case__		,				model.base_model_prefix						)

    # Ignore this for base models (BertModel, GPT2Model, etc.)
    if (not has_tied_params) and is_base_model:
        return []

    # otherwise they have an attached head
    A       :   List[Any]					=						list(model.named_children()						)
    A       :   List[str]					=						[list_modules[-1][0]]

    # add last module together with tied weights
    A       :   Dict					=						set(snake_case__						) - set(snake_case__						)
    A       :   Optional[Any]					=						list(set(snake_case__						)						) + list(snake_case__						)

    # remove ".weight" from the keys
    A       :   Any					=						['''.weight''', '''.bias''']
    A       :   Tuple					=						[]
    for name in list_untouched:
        for name_to_remove in names_to_remove:
            if name_to_remove in name:
                A       :   Dict					=						name.replace(snake_case__		,				''''''						)
        filtered_module_names.append(snake_case__						)

    return filtered_module_names











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)





