




import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_=None						):

						'''simple docstring'''


						_UpperCAmelCase													=					None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype						) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												_UpperCAmelCase													=					weight.view(torch.intaa						)
												_UpperCAmelCase													=					"bfloat16"
						_UpperCAmelCase													=					weight.cpu().numpy()
						_UpperCAmelCase													=					os.path.join(snake_case_     ,   f"""{weight_name}.dat"""						)
						if index is not None:
												if dtype is None:
																		_UpperCAmelCase													=					str(array.dtype						)
												_UpperCAmelCase													=					{"dtype": dtype, "shape": list(array.shape						)}
						if array.ndim == 0:
												_UpperCAmelCase													=					array[None]
						_UpperCAmelCase													=					np.memmap(snake_case_     ,   dtype=array.dtype     ,   mode="w+"     ,   shape=array.shape						)
						_UpperCAmelCase													=					array[:]
						file_array.flush()
						return index


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					tuple(weight_info["shape"]						)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												_UpperCAmelCase													=					(1,)

						_UpperCAmelCase													=					weight_info["dtype"]
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												_UpperCAmelCase													=					"int16"

						_UpperCAmelCase													=					np.memmap(snake_case_     ,   dtype=snake_case_     ,   shape=snake_case_     ,   mode="r"						)

						if len(weight_info["shape"]						) == 0:
												_UpperCAmelCase													=					weight[0]
						_UpperCAmelCase													=					torch.tensor(snake_case_						)
						if weight_info["dtype"] == "bfloat16":
												_UpperCAmelCase													=					weight.view(torch.bfloataa						)

						return weight


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						if index is None or len(snake_case_						) == 0:
												# Nothing to save
												return

						_UpperCAmelCase													=					os.path.join(snake_case_     ,   "index.json"						)
						if os.path.isfile(snake_case_						):
												with open(snake_case_     ,   "r"     ,   encoding="utf-8"						) as f:
																		_UpperCAmelCase													=					json.load(snake_case_						)
						else:
												_UpperCAmelCase													=					{}
						current_index.update(snake_case_						)

						with open(snake_case_     ,   "w"     ,   encoding="utf-8"						) as f:
												json.dump(snake_case_     ,   snake_case_     ,   indent=2						)


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						os.makedirs(snake_case_     ,   exist_ok=snake_case_						)
						_UpperCAmelCase													=					{}
						for name, parameter in state_dict.items():
												_UpperCAmelCase													=					offload_weight(snake_case_     ,   snake_case_     ,   snake_case_     ,   index=snake_case_						)

						# Update index
						save_offload_index(snake_case_     ,   snake_case_						)






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			def __init__(	self   :      Union[str, Any]   ,   snake_case__   :      Mapping   ,   snake_case__   :      str				):




									"""simple docstring"""


									_UpperCAmelCase													=					dataset
									_UpperCAmelCase													=					prefix







			def __getitem__(	self   :      int   ,   snake_case__   :      Any				):




									"""simple docstring"""


									return self.dataset[F"""{self.prefix}{key}"""]







			def __iter__(	self   :      Union[str, Any]				):




									"""simple docstring"""


									return iter([key for key in self.dataset if key.startswith(self.prefix				)]				)







			def __len__(	self   :      List[str]				):




									"""simple docstring"""


									return len(self.dataset				)






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			def __init__(	self   :      Tuple   ,   snake_case__   :      Dict[str, torch.Tensor] = None   ,   snake_case__   :      Optional[Union[str, os.PathLike]] = None   ,   snake_case__   :      Mapping = None   ,   snake_case__   :      Union[str, Any]=None   ,   ):




									"""simple docstring"""


									if state_dict is None and save_folder is None:
															raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."				)

									_UpperCAmelCase													=					{} if state_dict is None else state_dict
									_UpperCAmelCase													=					save_folder
									if index is None and save_folder is not None:
															with open(os.path.join(snake_case__   ,   "index.json"				)				) as f:
																					_UpperCAmelCase													=					json.load(snake_case__				)
									_UpperCAmelCase													=					{} if index is None else index
									_UpperCAmelCase													=					list(self.state_dict.keys()				)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]				)
									_UpperCAmelCase													=					device







			def __getitem__(	self   :      List[Any]   ,   snake_case__   :      str				):




									"""simple docstring"""


									if key in self.state_dict:
															return self.state_dict[key]
									_UpperCAmelCase													=					self.index[key]
									if weight_info.get("safetensors_file"				) is not None:
															if not is_safetensors_available():
																					raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`."				)

															from safetensors import safe_open

															_UpperCAmelCase													=					"cpu" if self.device is None else self.device
															with safe_open(weight_info["safetensors_file"]   ,   framework="pt"   ,   device=snake_case__				) as f:
																					_UpperCAmelCase													=					f.get_tensor(weight_info.get("weight_name"   ,   snake_case__				)				)

															if "dtype" in weight_info:
																					return tensor.to(getattr(snake_case__   ,   weight_info["dtype"]				)				)
															else:
																					return tensor

									_UpperCAmelCase													=					os.path.join(self.save_folder   ,   F"""{key}.dat"""				)
									return load_offloaded_weight(snake_case__   ,   snake_case__				)







			def __iter__(	self   :      Optional[int]				):




									"""simple docstring"""


									return iter(self.all_keys				)







			def __len__(	self   :      int				):




									"""simple docstring"""


									return len(self.all_keys				)




def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					{}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + "."						)
												    }						)
						return result










from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			@abstractmethod
			def __lt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __gt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __eq__(	self   :      T   ,   snake_case__   :      object				):




									"""simple docstring"""


									pass




lowercase_     :    Tuple      =			TypeVar('T', bound=Comparable)






class    __lowerCAmelCase			(	Generic[T]			):







			def __init__(	self   :      Optional[int]				):




									"""simple docstring"""


									_UpperCAmelCase													=					[]
									_UpperCAmelCase													=					0







			def __repr__(	self   :      Dict				):




									"""simple docstring"""


									return str(self.h				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									if child_idx > 0:
															return (child_idx - 1) // 2
									return None







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 1
									if left_child_index < self.heap_size:
															return left_child_index
									return None







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 2
									if right_child_index < self.heap_size:
															return right_child_index
									return None







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      int				):




									"""simple docstring"""


									if index < self.heap_size:
															_UpperCAmelCase													=					index
															_UpperCAmelCase													=					self.left_child_idx(snake_case__				)
															_UpperCAmelCase													=					self.right_child_idx(snake_case__				)
															# check which child is larger than its parent
															if left_child is not None and self.h[left_child] > self.h[violation]:
																					_UpperCAmelCase													=					left_child
															if right_child is not None and self.h[right_child] > self.h[violation]:
																					_UpperCAmelCase													=					right_child
															# if violation indeed exists
															if violation != index:
																					# swap to fix the violation
																					_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[index], self.h[violation]
																					# fix the subsequent violation recursively if any
																					self.max_heapify(snake_case__				)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Iterable[T]				):




									"""simple docstring"""


									_UpperCAmelCase													=					list(snake_case__				)
									_UpperCAmelCase													=					len(self.h				)
									if self.heap_size > 1:
															# max_heapify from right to left but exclude leaves (last level)
															for i in range(self.heap_size // 2 - 1   ,   -1   ,   -1				):
																					self.max_heapify(snake_case__				)







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									if self.heap_size >= 2:
															_UpperCAmelCase													=					self.h[0]
															_UpperCAmelCase													=					self.h.pop(-1				)
															self.heap_size -= 1
															self.max_heapify(0				)
															return me
									elif self.heap_size == 1:
															self.heap_size -= 1
															return self.h.pop(-1				)
									else:
															raise Exception("Empty heap"				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      T				):




									"""simple docstring"""


									self.h.append(snake_case__				)
									_UpperCAmelCase													=					(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
															self.max_heapify(snake_case__				)
															_UpperCAmelCase													=					(idx - 1) // 2







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.heap_size
									for j in range(size - 1   ,   0   ,   -1				):
															_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[j], self.h[0]
															self.heap_size -= 1
															self.max_heapify(0				)
									_UpperCAmelCase													=					size


if __name__ == "__main__":
				import doctest

				# run doc test
				doctest.testmod()

				# demo
				for unsorted in [
				    [0],
				    [2],
				    [3, 5],
				    [5, 3],
				    [5, 5],
				    [0, 0, 0, 0],
				    [1, 1, 1, 1],
				    [2, 2, 3, 5],
				    [0, 2, 2, 3, 5],
				    [2, 5, 3, 0, 2, 3, 0, 3],
				    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
				    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
				    [-45, -2, -5],
				]:
								print(f"""unsorted array: {unsorted}""")

								lowercase_     :    Heap[int]      =			Heap()
								heap.build_max_heap(unsorted)
								print(f"""after build heap: {heap}""")

								print(f"""max value: {heap.extract_max()}""")
								print(f"""after max value removed: {heap}""")

								heap.insert(1_00)
								print(f"""after new value 100 inserted: {heap}""")

								heap.heap_sort()
								print(f"""heap-sorted array: {heap}\n""")




