



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( A			:       Union[str, Any]					,	A			:       int					,	A			:       Optional[int]					,	A			:       Any=None			)						->			int:
					"""simple docstring"""





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






def        a( A			:       Any					,	A			:       List[Any]			)						->			int:
					"""simple docstring"""





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

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

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

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

					return weight






def        a( A			:       Any					,	A			:       Dict			)						->			List[Any]:
					"""simple docstring"""





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

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

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






def        a( A			:       Union[str, os.PathLike]					,	A			:       Dict[str, torch.Tensor]			)						->			Optional[Any]:
					"""simple docstring"""





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

					# Update index
					save_offload_index(A					,	A			)



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  dataset
						a   	=  prefix




	def __getitem__(self						,			lowerCamelCase_						):
						"""simple docstring"""

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




	def __iter__(self						):
						"""simple docstring"""

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




	def __len__(self						):
						"""simple docstring"""

						return len(self.dataset						)




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_ = None						,			lowerCamelCase_=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."						)

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




	def __getitem__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if key in self.state_dict:
											return self.state_dict[key]
						a   	=  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

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

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

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




	def __iter__(self						):
						"""simple docstring"""

						return iter(self.all_keys						)




	def __len__(self						):
						"""simple docstring"""

						return len(self.all_keys						)






def        a( A			:       Dict[str, torch.Tensor]					,	A			:       List[str]			)						->			Tuple:
					"""simple docstring"""





					a   	=  {}
					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





def        a( A			:       int					,	A			:       int					,	A			:       int			)						->			float:
					"""simple docstring"""





					if common_ratio == 1:
										# Formula for sum if common ratio is 1
										return num_of_terms * first_term

					# Formula for finding sum of n terms of a GeometricProgression
					return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)
