






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				:List[str]			,							a				:Union[str, Any]			,							a				:Dict=None				)							->    Any:
	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				:Optional[Any]			,							a				:List[Any]				)							->    Optional[Any]:
	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				:int				)							->    int:
	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]				)							->    List[str]:
	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_      (			lowercase     ):






						'''simple docstring'''




						def __init__(   self   :		List[Any]				,      __UpperCAmelCase   :		Mapping				,      __UpperCAmelCase   :		str						)					->Optional[Any]:




							"""simple docstring"""


							a							=   dataset
							a							=   prefix







						def __getitem__(   self   :		List[str]				,      __UpperCAmelCase   :		Optional[int]						)					->int:




							"""simple docstring"""


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







						def __iter__(   self   :		List[str]						)					->Any:




							"""simple docstring"""


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







						def __len__(   self   :		Optional[Any]						)					->List[Any]:




							"""simple docstring"""


							return len(self.dataset						)






class 	lowercase_      (			lowercase     ):






						'''simple docstring'''




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




							"""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(__UpperCAmelCase				,      '''index.json'''						)						) as f:
									a							=   json.load(__UpperCAmelCase						)
							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   :		Tuple				,      __UpperCAmelCase   :		str						)					->Union[str, Any]:




							"""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=__UpperCAmelCase						) as f:
									a							=   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

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







						def __iter__(   self   :		Optional[int]						)					->Any:




							"""simple docstring"""


							return iter(self.all_keys						)







						def __len__(   self   :		List[str]						)					->Optional[int]:




							"""simple docstring"""


							return len(self.all_keys						)
def 		_a					(   a				:Dict[str, torch.Tensor]			,							a				:List[str]				)							->    Dict:
	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				:float				)							->    float:
 return -num if num < 0 else num
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_min() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs_val(a				) < abs_val(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs(a				) > abs(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max_sort() arg is an empty sequence'''				)
 return sorted(a			,							key=a				)[-1]
def 		_a					(   )							->    int:
 assert abs_val(0				) == 0
 assert abs_val(34				) == 34
 assert abs_val(-100_000_000_000				) == 100_000_000_000

 a							=   [-3, -1, 2, -11]
 assert abs_max(a				) == -11
 assert abs_max_sort(a				) == -11
 assert abs_min(a				) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34


