




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


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if len(snake_case_						) == 0:
												raise ValueError("find_max() arg is an empty sequence"						)
						_UpperCAmelCase													=					nums[0]
						for x in nums:
												if x > max_num:
																		_UpperCAmelCase													=					x
						return max_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)




