


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     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case=None						)					->      List[str]:







 """simple docstring"""


 _lowercase											=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											=weight.view(torch.intaa						)
  _lowercase											='''bfloat16'''
 _lowercase											=weight.cpu().numpy()
 _lowercase											=os.path.join(__snake_case					,						F"{weight_name}.dat"						)
 if index is not None:
  if dtype is None:
   _lowercase											=str(array.dtype						)
  _lowercase											={'''dtype''': dtype, '''shape''': list(array.shape						)}
 if array.ndim == 0:
  _lowercase											=array[None]
 _lowercase											=np.memmap(__snake_case					,						dtype=array.dtype					,						mode='''w+'''					,						shape=array.shape						)
 _lowercase											=array[:]
 file_array.flush()
 return index
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      Union[str, Any]:







 """simple docstring"""


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

 _lowercase											=weight_info['''dtype''']
 if dtype == "bfloat16":
  # NumPy does not support bfloat16 so this was saved as a int16
  _lowercase											='''int16'''

 _lowercase											=np.memmap(__snake_case					,						dtype=__snake_case					,						shape=__snake_case					,						mode='''r'''						)

 if len(weight_info['''shape''']						) == 0:
  _lowercase											=weight[0]
 _lowercase											=torch.tensor(__snake_case						)
 if weight_info["dtype"] == "bfloat16":
  _lowercase											=weight.view(torch.bfloataa						)

 return weight
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      str:







 """simple docstring"""


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

 _lowercase											=os.path.join(__snake_case					,						'''index.json'''						)
 if os.path.isfile(__snake_case						):
  with open(__snake_case					,						'''r'''					,						encoding='''utf-8'''						) as f:
   _lowercase											=json.load(__snake_case						)
 else:
  _lowercase											={}
 current_index.update(__snake_case						)

 with open(__snake_case					,						'''w'''					,						encoding='''utf-8'''						) as f:
  json.dump(__snake_case					,						__snake_case					,						indent=2						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      List[str]:







 """simple docstring"""


 os.makedirs(__snake_case					,						exist_ok=__snake_case						)
 _lowercase											={}
 for name, parameter in state_dict.items():
  _lowercase											=offload_weight(__snake_case					,						__snake_case					,						__snake_case					,						index=__snake_case						)

 # Update index
 save_offload_index(__snake_case					,						__snake_case						)







class 				lowerCamelCase__       (	lowerCAmelCase):





     def __init__(self		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Dict:
      _lowercase											=dataset
      _lowercase											=prefix





     def __getitem__(self		,					UpperCAmelCase	)					->       Dict:
      return self.dataset[f"{self.prefix}{key}"]





     def __iter__(self	)					->       int:
      return iter([key for key in self.dataset if key.startswith(self.prefix	)]	)





     def __len__(self	)					->       Any:
      return len(self.dataset	)







class 				lowerCamelCase__       (	lowerCAmelCase):





     def __init__(self		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase=None		,					)					->       List[Any]:
      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											={} if state_dict is None else state_dict
      _lowercase											=save_folder
      if index is None and save_folder is not None:
       with open(os.path.join(UpperCAmelCase		,					'''index.json'''	)	) as f:
        _lowercase											=json.load(UpperCAmelCase	)
      _lowercase											={} if index is None else index
      _lowercase											=list(self.state_dict.keys()	)
      self.all_keys.extend([key for key in self.index if key not in self.all_keys]	)
      _lowercase											=device





     def __getitem__(self		,					UpperCAmelCase	)					->       List[str]:
      # State dict gets priority
      if key in self.state_dict:
       return self.state_dict[key]
      _lowercase											=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											='''cpu''' if self.device is None else self.device
       with safe_open(weight_info['''safetensors_file''']		,					framework='''pt'''		,					device=UpperCAmelCase	) as f:
        _lowercase											=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

      _lowercase											=os.path.join(self.save_folder		,					f"{key}.dat"	)
      return load_offloaded_weight(UpperCAmelCase		,					UpperCAmelCase	)





     def __iter__(self	)					->       Optional[int]:
      return iter(self.all_keys	)





     def __len__(self	)					->       Any:
      return len(self.all_keys	)



def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      Any:







 """simple docstring"""


 _lowercase											={}
 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





import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 _lowercase											=BertConfig.from_pretrained(
     '''bert-large-cased'''					,						vocab_size=__snake_case					,						max_position_embeddings=512					,						is_decoder=__snake_case					,						add_cross_attention=__snake_case					,						)
 _lowercase											=bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 _lowercase											=BertGenerationConfig(**__snake_case						)
 if is_encoder:
  _lowercase											=BertGenerationEncoder(__snake_case						)
 else:
  _lowercase											=BertGenerationDecoder(__snake_case						)
 print(F"Building PyTorch model from configuration: {config}"						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     __snake_case					,						__snake_case					,						model_class='''bert'''					,						is_encoder_named_decoder=__snake_case					,						is_encoder=__snake_case					,						)

 # Save pytorch-model
 print(F"Save PyTorch model and config to {pytorch_dump_path}"						)
 model.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__            =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--is_encoder_named_decoder''',
      action='''store_true''',
      help='''If decoder has to be renamed to encoder in PyTorch model.''',
  )
  parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
  parser.add_argument('''--vocab_size''', default=5_0358, type=int, help='''Vocab size of model''')
  UpperCAmelCase__            =							parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )

