
'''simple docstring'''







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       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: str=None	):
						_SCREAMING_SNAKE_CASE					=			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.
												_SCREAMING_SNAKE_CASE					=			weight.view(torch.intaa	)
												_SCREAMING_SNAKE_CASE					=			"bfloat16"
						_SCREAMING_SNAKE_CASE					=			weight.cpu().numpy()
						_SCREAMING_SNAKE_CASE					=			os.path.join(_SCREAMING_SNAKE_CASE						,      F'{weight_name}.dat'	)
						if index is not None:
												if dtype is None:
																		_SCREAMING_SNAKE_CASE					=			str(array.dtype	)
												_SCREAMING_SNAKE_CASE					=			{"dtype": dtype, "shape": list(array.shape	)}
						if array.ndim == 0:
												_SCREAMING_SNAKE_CASE					=			array[None]
						_SCREAMING_SNAKE_CASE					=			np.memmap(_SCREAMING_SNAKE_CASE						,      dtype=array.dtype						,      mode="w+"						,      shape=array.shape	)
						_SCREAMING_SNAKE_CASE					=			array[:]
						file_array.flush()
						return index



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

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

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

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

						return weight



def       _a		(						_SCREAMING_SNAKE_CASE		: str						,      _SCREAMING_SNAKE_CASE		: Tuple	):
						if index is None or len(_SCREAMING_SNAKE_CASE	) == 0:
												# Nothing to save
												return

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

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



def       _a		(						_SCREAMING_SNAKE_CASE		: Union[str, os.PathLike]						,      _SCREAMING_SNAKE_CASE		: Dict[str, torch.Tensor]	):
						os.makedirs(_SCREAMING_SNAKE_CASE						,      exist_ok=_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			{}
						for name, parameter in state_dict.items():
												_SCREAMING_SNAKE_CASE					=			offload_weight(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      index=_SCREAMING_SNAKE_CASE	)

						# Update index
						save_offload_index(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			dataset
								_SCREAMING_SNAKE_CASE					=			prefix




		def __getitem__(					self       ,    UpperCamelCase   ):
								return self.dataset[F'{self.prefix}{key}']




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




		def __len__(					self   ):
								return len(self.dataset   )


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase = None       ,    UpperCamelCase = None       ,    UpperCamelCase = None       ,    UpperCamelCase=None       ,    ):
								if state_dict is None and save_folder is None:
														raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."   )

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




		def __getitem__(					self       ,    UpperCamelCase   ):
								# State dict gets priority
								if key in self.state_dict:
														return self.state_dict[key]
								_SCREAMING_SNAKE_CASE					=			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

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

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

								_SCREAMING_SNAKE_CASE					=			os.path.join(self.save_folder       ,    F'{key}.dat'   )
								return load_offloaded_weight(UpperCamelCase       ,    UpperCamelCase   )




		def __iter__(					self   ):
								return iter(self.all_keys   )




		def __len__(					self   ):
								return len(self.all_keys   )



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict[str, torch.Tensor]						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						_SCREAMING_SNAKE_CASE					=			{}
						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

'''simple docstring'''







class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase=None   ):
								_SCREAMING_SNAKE_CASE					=			data
								_SCREAMING_SNAKE_CASE					=			None




		def __repr__(					self   ):
								_SCREAMING_SNAKE_CASE					=			""
								_SCREAMING_SNAKE_CASE					=			self
								while temp:
														string_rep += F'<{temp.data}> ---> '
														_SCREAMING_SNAKE_CASE					=			temp.next
								string_rep += "<END>"
								return string_rep



def       _a		(						_SCREAMING_SNAKE_CASE		: Any	):

						# if elements_list is empty
						if not elements_list:
												raise Exception("The Elements List is empty"	)

						# Set first element as Head
						_SCREAMING_SNAKE_CASE					=			Node(elements_list[0]	)
						_SCREAMING_SNAKE_CASE					=			head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_SCREAMING_SNAKE_CASE					=			Node(_SCREAMING_SNAKE_CASE	)
												_SCREAMING_SNAKE_CASE					=			current.next
						return head


_snake_case					: List[Any]     			=					[1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
_snake_case					: List[Any]     			=					make_linked_list(list_data)
print("""Linked List:""")




print(linked_list)