




import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__a			:	str							     =		logging.get_logger(__name__)




class     A					(				lowerCamelCase_       ):







							def __init__(    self   :	Tuple   ,     *__UpperCAmelCase   :	Optional[Any]   ,     **__UpperCAmelCase   :	Dict				)		->					None:

										"""simple docstring"""



										warnings.warn(
										    'The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use LevitImageProcessor instead.'   ,     __UpperCAmelCase   ,     )
										super().__init__(*__UpperCAmelCase   ,     **__UpperCAmelCase				)






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     ,		__snake_case     ,		__snake_case     ,		__snake_case=None					)	->			str:

			'''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     ,		__snake_case					)	->			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     ,		__snake_case					)	->			Optional[int]:

			'''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     ,		__snake_case					)	->			Tuple:

			'''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     A					(				lowerCamelCase_       ):







							def __init__(    self   :	int   ,     __UpperCAmelCase   :	Mapping   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= dataset
										UpperCamelCase_     				= prefix







							def __getitem__(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict				)		->					Tuple:

										"""simple docstring"""



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







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

										"""simple docstring"""



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







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

										"""simple docstring"""



										return len(self.dataset				)







class     A					(				lowerCamelCase_       ):







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

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

										UpperCamelCase_     				= os.path.join(self.save_folder   ,     f'''{key}.dat'''				)
										return load_offloaded_weight(__UpperCAmelCase   ,     __UpperCAmelCase				)







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

										"""simple docstring"""



										return iter(self.all_keys				)







							def __len__(    self   :	Any				)		->					Any:

										"""simple docstring"""



										return len(self.all_keys				)







def  a_		(							__snake_case     ,		__snake_case					)	->			Any:

			'''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
