




'''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 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase=None      )				->Union[str, Any]:






       '''simple docstring'''




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




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Optional[Any]:






       '''simple docstring'''




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

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

       lowercase  :		Union[str, Any]						=      np.memmap(lowerCamelCase   ,				dtype=lowerCamelCase   ,				shape=lowerCamelCase   ,				mode='''r'''      )

       if len(weight_info['''shape''']      ) == 0:
              lowercase  :		Any						=      weight[0]
       lowercase  :		Optional[int]						=      torch.tensor(lowerCamelCase      )
       if weight_info["dtype"] == "bfloat16":
              lowercase  :		List[Any]						=      weight.view(torch.bfloataa      )

       return weight




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




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

       lowercase  :		Optional[Any]						=      os.path.join(lowerCamelCase   ,				'''index.json'''      )
       if os.path.isfile(lowerCamelCase      ):
              with open(lowerCamelCase   ,				'''r'''   ,				encoding='''utf-8'''      ) as f:
                     lowercase  :		Optional[int]						=      json.load(lowerCamelCase      )
       else:
              lowercase  :		int						=      {}
       current_index.update(lowerCamelCase      )

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




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Optional[int]:






       '''simple docstring'''




       os.makedirs(lowerCamelCase   ,				exist_ok=lowerCamelCase      )
       lowercase  :		Tuple						=      {}
       for name, parameter in state_dict.items():
              lowercase  :		Optional[int]						=      offload_weight(lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				index=lowerCamelCase      )

       # Update index
       save_offload_index(lowerCamelCase   ,				lowerCamelCase      )

class   SCREAMING_SNAKE_CASE_		(       _SCREAMING_SNAKE_CASE				):



 def __init__(  self   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      dataset
        lowercase  :		str						=      prefix



 def __getitem__(  self   ,		A ):






        '''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   SCREAMING_SNAKE_CASE_		(       _SCREAMING_SNAKE_CASE				):



 def __init__(  self   ,		A = None   ,		A = None   ,		A = None   ,		A=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.''' )

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



 def __getitem__(  self   ,		A ):






        '''simple docstring'''




        if key in self.state_dict:
               return self.state_dict[key]
        lowercase  :		List[str]						=      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

               lowercase  :		Union[str, Any]						=      '''cpu''' if self.device is None else self.device
               with safe_open(weight_info['''safetensors_file''']   ,		framework='''pt'''   ,		device=__A ) as f:
                      lowercase  :		Union[str, Any]						=      f.get_tensor(weight_info.get('''weight_name'''   ,		__A ) )

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

        lowercase  :		int						=      os.path.join(self.save_folder   ,		f"""{key}.dat""" )
        return load_offloaded_weight(__A   ,		__A )



 def __iter__(  self ):






        '''simple docstring'''




        return iter(self.all_keys )





 def __len__(  self ):






        '''simple docstring'''




        return len(self.all_keys )






def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->str:






       '''simple docstring'''




       lowercase  :		str						=      {}
       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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase	:      int				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




