




'''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 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase		,							__lowercase=None  ) ->   Optional[int]:
     A:		List[str]        =		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:		int        =		weight.view(torch.intaa  )
          A:		List[Any]        =		'''bfloat16'''
     A:		Dict        =		weight.cpu().numpy()
     A:		Any        =		os.path.join(__lowercase		,							F"""{weight_name}.dat"""  )
     if index is not None:
          if dtype is None:
               A:		Optional[Any]        =		str(array.dtype  )
          A:		Optional[Any]        =		{'''dtype''': dtype, '''shape''': list(array.shape  )}
     if array.ndim == 0:
          A:		str        =		array[None]
     A:		List[str]        =		np.memmap(__lowercase		,							dtype=array.dtype		,							mode='''w+'''		,							shape=array.shape  )
     A:		List[str]        =		array[:]
     file_array.flush()
     return index
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   List[str]:
     A:		Optional[Any]        =		tuple(weight_info['''shape''']  )
     if shape == ():
          # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
          A:		List[str]        =		(1,)

     A:		Union[str, Any]        =		weight_info['''dtype''']
     if dtype == "bfloat16":
          # NumPy does not support bfloat16 so this was saved as a int16
          A:		Optional[Any]        =		'''int16'''

     A:		Optional[int]        =		np.memmap(__lowercase		,							dtype=__lowercase		,							shape=__lowercase		,							mode='''r'''  )

     if len(weight_info['''shape''']  ) == 0:
          A:		Dict        =		weight[0]
     A:		Any        =		torch.tensor(__lowercase  )
     if weight_info["dtype"] == "bfloat16":
          A:		List[Any]        =		weight.view(torch.bfloataa  )

     return weight
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   List[Any]:
     if index is None or len(__lowercase  ) == 0:
          # Nothing to save
          return

     A:		Union[str, Any]        =		os.path.join(__lowercase		,							'''index.json'''  )
     if os.path.isfile(__lowercase  ):
          with open(__lowercase		,							'''r'''		,							encoding='''utf-8'''  ) as f:
               A:		List[str]        =		json.load(__lowercase  )
     else:
          A:		Any        =		{}
     current_index.update(__lowercase  )

     with open(__lowercase		,							'''w'''		,							encoding='''utf-8'''  ) as f:
          json.dump(__lowercase		,							__lowercase		,							indent=2  )
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   int:
     os.makedirs(__lowercase		,							exist_ok=__lowercase  )
     A:		str        =		{}
     for name, parameter in state_dict.items():
          A:		List[str]        =		offload_weight(__lowercase		,							__lowercase		,							__lowercase		,							index=__lowercase  )

     # Update index
     save_offload_index(__lowercase		,							__lowercase  )






class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def __init__(   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Mapping				,     SCREAMING_SNAKE_CASE_							:       str  ) ->		str:







       '''simple docstring'''



       A:		str        =		dataset
       A:		Any        =		prefix



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







       '''simple docstring'''



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



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







       '''simple docstring'''



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




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







       '''simple docstring'''



       return len(self.dataset  )






class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

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

       A:		Tuple        =		{} if state_dict is None else state_dict
       A:		int        =		save_folder
       if index is None and save_folder is not None:
            with open(os.path.join(SCREAMING_SNAKE_CASE_				,     '''index.json'''  )  ) as f:
                 A:		Optional[int]        =		json.load(SCREAMING_SNAKE_CASE_  )
       A:		Dict        =		{} if index is None else index
       A:		Dict        =		list(self.state_dict.keys()  )
       self.all_keys.extend([key for key in self.index if key not in self.all_keys]  )
       A:		str        =		device



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







       '''simple docstring'''



       if key in self.state_dict:
            return self.state_dict[key]
       A:		int        =		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:		Any        =		'''cpu''' if self.device is None else self.device
            with safe_open(weight_info['''safetensors_file''']				,     framework='''pt'''				,     device=SCREAMING_SNAKE_CASE_  ) as f:
                 A:		Union[str, Any]        =		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

       A:		List[Any]        =		os.path.join(self.save_folder				,     f"""{key}.dat"""  )
       return load_offloaded_weight(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )



  def __iter__(   self							:       str  ) ->		str:







       '''simple docstring'''



       return iter(self.all_keys  )




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







       '''simple docstring'''



       return len(self.all_keys  )






def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   Union[str, Any]:
     A:		Optional[Any]        =		{}
     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'''


from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def 				_snake_case    (   self							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		int:







       '''simple docstring'''



       return True




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







       '''simple docstring'''



       A:		Optional[Any]        =		CpmTokenizer.from_pretrained('''TsinghuaAI/CPM-Generate'''  )
       A:		Optional[int]        =		'''Hugging Face大法好，谁用谁知道。'''
       A:		str        =		'''Hugging Face大法好,谁用谁知道。<unk>'''
       A:		Dict        =		'''▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'''.split()

       A:		int        =		tokenizer.tokenize(SCREAMING_SNAKE_CASE_  )
       self.assertListEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		tokens + [tokenizer.unk_token]

       A:		str        =		[1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_  )				,     SCREAMING_SNAKE_CASE_  )

       A:		Any        =		tokenizer.decode(SCREAMING_SNAKE_CASE_  )
       self.assertEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )






