


'''simple docstring'''
from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 				lowerCamelCase_   (   __UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					bool		)				->  torch.Tensor:







     """simple docstring"""
     # The first operation in a checkpoint can't be in-place, but it's
     # nice to have in-place addition during inference. Thus...
     if not inplace:
          _A				=       ma + ma
     else:
          ma += ma

     return ma

def 				lowerCamelCase_   (   __UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					List[int]		)				->  torch.Tensor:







     """simple docstring"""
     _A				=       -1 * len(__UpperCamelCase		)
     _A				=       list(range(len(tensor.shape[:zero_index]		)		)		)
     return tensor.permute(first_inds + [zero_index + i for i in inds]		)

def 				lowerCamelCase_   (   __UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					int		)				->  torch.Tensor:







     """simple docstring"""
     return t.reshape(t.shape[:-no_dims] + (-1,)		)

def 				lowerCamelCase_   (   __UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					float = 1e-4		)				->  torch.Tensor:







     """simple docstring"""
     _A				=       mask.expand(*value.shape		)
     return torch.sum(mask * value       ,						dim=__UpperCamelCase		) / (eps + torch.sum(__UpperCamelCase       ,						dim=__UpperCamelCase		))

def 				lowerCamelCase_   (   __UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					torch.types.Number = 2.31_25       ,						__UpperCamelCase				:					torch.types.Number = 21.68_75       ,						__UpperCamelCase				:					int = 6_4		)				->  torch.Tensor:







     """simple docstring"""
     _A				=       torch.linspace(__UpperCamelCase       ,						__UpperCamelCase       ,						no_bins - 1       ,						device=pts.device		)
     _A				=       torch.sqrt(torch.sum((pts.unsqueeze(-2		) - pts.unsqueeze(-3		)) ** 2       ,						dim=-1		)		)
     return torch.bucketize(__UpperCamelCase       ,						__UpperCamelCase		)

def 				lowerCamelCase_   (   __UpperCamelCase				:					Callable[[list], Any]       ,						__UpperCamelCase				:					List[dict]		)				->  dict:







     """simple docstring"""
     _A				=       dicts[0]
     _A				=       {}
     for k, v in first.items():
          _A				=       [d[k] for d in dicts]
          if isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
               _A				=       dict_multimap(__UpperCamelCase       ,						__UpperCamelCase		)
          else:
               _A				=       fn(__UpperCamelCase		)

     return new_dict

def 				lowerCamelCase_   (   __UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					torch.Tensor		)				->  torch.Tensor:







     """simple docstring"""
     _A				=       v_bins.view(((1,) * len(x.shape		)) + (len(__UpperCamelCase		),)		)
     _A				=       x[..., None] - reshaped_bins
     _A				=       torch.argmin(torch.abs(__UpperCamelCase		)       ,						dim=-1		)
     return nn.functional.one_hot(__UpperCamelCase       ,						num_classes=len(__UpperCamelCase		)		).float()

def 				lowerCamelCase_   (   __UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					torch.Tensor       ,						__UpperCamelCase				:					int = 0       ,						__UpperCamelCase				:					int = 0		)				->  torch.Tensor:







     """simple docstring"""
     _A				=       []
     for i, s in enumerate(data.shape[:no_batch_dims]		):
          _A				=       torch.arange(__UpperCamelCase		)
          _A				=       r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape		) - i - 1)))		)
          ranges.append(__UpperCamelCase		)

     _A				=       [slice(__UpperCamelCase		) for _ in range(len(data.shape		) - no_batch_dims		)]
     _A				=       inds
     ranges.extend(__UpperCamelCase		)
     # Matt note: Editing this to get around the behaviour of using a list as an array index changing
     # in recent Numpy versions
     return data[tuple(__UpperCamelCase		)]


lowerCAmelCase  =				TypeVar("""T""")

def 				lowerCamelCase_   (   __UpperCamelCase				:					Callable[[T], Any]       ,						__UpperCamelCase				:					Dict[Any, Union[dict, list, tuple, T]]       ,						__UpperCamelCase				:					Type[T]		)				->  Dict[Any, Union[dict, list, tuple, Any]]:







     """simple docstring"""
     _A				=       {}
     for k, v in dic.items():
          if isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
               _A				=       dict_map(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          else:
               _A				=       tree_map(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)

     return new_dict

@overload
def 				lowerCamelCase_   (   __UpperCamelCase				:					Callable[[T], Any]       ,						__UpperCamelCase				:					T       ,						__UpperCamelCase				:					Type[T]		)				->  Any:







     """simple docstring"""
     ...

@overload
def 				lowerCamelCase_   (   __UpperCamelCase				:					Callable[[T], Any]       ,						__UpperCamelCase				:					dict       ,						__UpperCamelCase				:					Type[T]		)				->  dict:







     """simple docstring"""
     ...

@overload
def 				lowerCamelCase_   (   __UpperCamelCase				:					Callable[[T], Any]       ,						__UpperCamelCase				:					list       ,						__UpperCamelCase				:					Type[T]		)				->  list:







     """simple docstring"""
     ...

@overload
def 				lowerCamelCase_   (   __UpperCamelCase				:					Callable[[T], Any]       ,						__UpperCamelCase				:					tuple       ,						__UpperCamelCase				:					Type[T]		)				->  tuple:







     """simple docstring"""
     ...

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[Any]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict		)				->  Optional[int]:







     """simple docstring"""
     if isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          return dict_map(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
     elif isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          return [tree_map(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		) for x in tree]
     elif isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          return tuple(tree_map(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		) for x in tree		)
     elif isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          return fn(__UpperCamelCase		)
     else:
          print(type(__UpperCamelCase		)		)
          raise ValueError('Not supported'		)
lowerCAmelCase  =				partial(tree_map, leaf_type=torch.Tensor)









'''simple docstring'''
import argparse

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


logging.set_verbosity_info()

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					str		)				->  List[str]:







     """simple docstring"""
     # Initialise PyTorch model
     _A				=       BertConfig.from_pretrained(
         'bert-large-cased'       ,						vocab_size=__UpperCamelCase       ,						max_position_embeddings=5_1_2       ,						is_decoder=__UpperCamelCase       ,						add_cross_attention=__UpperCamelCase       ,						)
     _A				=       bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _A				=       BertGenerationConfig(**__UpperCamelCase		)
     if is_encoder:
          _A				=       BertGenerationEncoder(__UpperCamelCase		)
     else:
          _A				=       BertGenerationDecoder(__UpperCamelCase		)
     print(F'Building PyTorch model from configuration: {config}'		)

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

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


if __name__ == "__main__":
    lowerCAmelCase  =				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=50_358, type=int, help="""Vocab size of model""")
    lowerCAmelCase  =				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,
    )





