import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    ConvBertConfig.from_json_file(__A						)
    _lowerCamelCase  :		Optional[int]			    =    ConvBertModel(__A						)

    _lowerCamelCase  :		Union[str, Any]			    =    load_tf_weights_in_convbert(__A ,			__A ,			__A						)
    model.save_pretrained(__A						)

    _lowerCamelCase  :		Optional[Any]			    =    TFConvBertModel.from_pretrained(__A ,			from_pt=__A						)
    tf_model.save_pretrained(__A						)


if __name__ == "__main__":
    lowerCAmelCase      :				Dict     			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--convbert_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained ConvBERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    lowerCAmelCase      :				Union[str, Any]     			=parser.parse_args()
    convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)





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 ,			__A ,			__A ,			__A=None						):







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







def     A__	(       __A ,			__A						):







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

    _lowerCamelCase  :		str			    =    weight_info["""dtype"""]
    if dtype == "bfloat16":
        # NumPy does not support bfloat16 so this was saved as a int16
        _lowerCamelCase  :		Optional[int]			    =    """int16"""

    _lowerCamelCase  :		str			    =    np.memmap(__A ,			dtype=__A ,			shape=__A ,			mode="""r"""						)

    if len(weight_info["""shape"""]						) == 0:
        _lowerCamelCase  :		List[str]			    =    weight[0]
    _lowerCamelCase  :		List[str]			    =    torch.tensor(__A						)
    if weight_info["dtype"] == "bfloat16":
        _lowerCamelCase  :		List[Any]			    =    weight.view(torch.bfloataa						)

    return weight







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    if index is None or len(__A						) == 0:
        # Nothing to save
        return

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

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







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    os.makedirs(__A ,			exist_ok=__A						)
    _lowerCamelCase  :		Dict			    =    {}
    for name, parameter in state_dict.items():
        _lowerCamelCase  :		Union[str, Any]			    =    offload_weight(__A ,			__A ,			__A ,			index=__A						)

    # Update index
    save_offload_index(__A ,			__A						)




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    dataset
          _lowerCamelCase  :		Tuple			    =    prefix



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


          """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  :					Any)					->int:


          """simple docstring"""


          return len(self.dataset)





class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






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

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



      def __getitem__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					str)					->Tuple:


          """simple docstring"""


          if key in self.state_dict:
              return self.state_dict[key]
          _lowerCamelCase  :		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

              _lowerCamelCase  :		Any			    =    """cpu""" if self.device is None else self.device
              with safe_open(weight_info["""safetensors_file"""]     ,	framework="""pt"""     ,	device=_UpperCamelCase) as f:
                  _lowerCamelCase  :		Tuple			    =    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

          _lowerCamelCase  :		Optional[Any]			    =    os.path.join(self.save_folder     ,	F"""{key}.dat""")
          return load_offloaded_weight(_UpperCamelCase     ,	_UpperCamelCase)



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


          """simple docstring"""


          return iter(self.all_keys)




      def __len__(       self  :					int)					->Any:


          """simple docstring"""


          return len(self.all_keys)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		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




