
from .number_theory.prime_numbers import next_prime






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		)->				None:





													'''simple docstring'''






													__UpperCamelCase       					=     size_table
													__UpperCamelCase       					=     [None] * self.size_table
													__UpperCamelCase       					=     0.7_5 if lim_charge is None else lim_charge
													__UpperCamelCase       					=     1 if charge_factor is None else charge_factor
													__UpperCamelCase       					=     []
													__UpperCamelCase       					=     {}



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													return self._keys



							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													return sum(1 for slot in self.values if slot is not None     ) / (
													    self.size_table * self.charge_factor
													)



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													return key % self.size_table



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													print(F"step {step_ord}"     )
													print(list(range(len(self.values     )     )     )     )
													print(self.values     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     values
													for value in values:
																			self.insert_data(SCREAMING_SNAKE_CASE_     )
																			self._step_by_step(SCREAMING_SNAKE_CASE_     )
																			i += 1



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     data
													__UpperCamelCase       					=     data



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     self.hash_function(key + 1     )

													while self.values[new_key] is not None and self.values[new_key] != key:
																			if self.values.count(SCREAMING_SNAKE_CASE_     ) > 0:
																									__UpperCamelCase       					=     self.hash_function(new_key + 1     )
																			else:
																									__UpperCamelCase       					=     None
																									break

													return new_key



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     [value for value in self.values if value is not None]
													__UpperCamelCase       					=     next_prime(self.size_table       ,		factor=2     )
													self._keys.clear()
													__UpperCamelCase       					=     [None] * self.size_table  # hell's pointers D: don't DRY ;/
													for value in survivor_values:
																			self.insert_data(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.hash_function(SCREAMING_SNAKE_CASE_     )

													if self.values[key] is None:
																			self._set_value(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )

													elif self.values[key] == data:
																			pass

													else:
																			__UpperCamelCase       					=     self._collision_resolution(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			if collision_resolution is not None:
																									self._set_value(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			else:
																									self.rehashing()
																									self.insert_data(SCREAMING_SNAKE_CASE_     )






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_	(							snake_case							:				Union[str, Any]	, snake_case							:				str	, snake_case							:				Dict	, snake_case							:				Optional[Any]=None	) ->      Optional[Any]:



						'''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 		A_	(							snake_case							:				Tuple	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''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 		A_	(							snake_case							:				Dict	, snake_case							:				int	) ->      str:



						'''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 		A_	(							snake_case							:				Union[str, os.PathLike]	, snake_case							:				Dict[str, torch.Tensor]	) ->      Dict:



						'''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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     prefix



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, 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     )->				str:





													'''simple docstring'''






													return len(self.dataset     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_=None       ,		)->				Tuple:





													'''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(SCREAMING_SNAKE_CASE_       ,		'''index.json'''     )     ) as f:
																									__UpperCamelCase       					=     json.load(SCREAMING_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       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''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=SCREAMING_SNAKE_CASE_     ) as f:
																									__UpperCamelCase       					=     f.get_tensor(weight_info.get('''weight_name'''       ,		SCREAMING_SNAKE_CASE_     )     )

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

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



							def __iter__(					self     )->				Dict:





													'''simple docstring'''






													return iter(self.all_keys     )



							def __len__(					self     )->				Dict:





													'''simple docstring'''






													return len(self.all_keys     )





def 		A_	(							snake_case							:				Dict[str, torch.Tensor]	, snake_case							:				List[str]	) ->      Tuple:



						'''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




