





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Dict           =  ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				snake_case_						:   Dict           =  (
				    ('''layer.''', '''layer_'''),
				    ('''word_embeddings.weight''', '''word_embeddings'''),
				    ('''position_embeddings.weight''', '''position_embeddings'''),
				    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
				    ('''.''', '''/'''),
				    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
				    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
				    ('''weight''', '''kernel'''),
				)

				if not os.path.isdir(_UpperCamelCase			):
								os.makedirs(_UpperCamelCase			)

				snake_case_						:   List[Any]           =  model.state_dict()

				def to_tf_var_name(_UpperCamelCase			):
								for patt, repl in iter(_UpperCamelCase			):
												snake_case_						:   Optional[int]           =  name.replace(_UpperCamelCase			,    _UpperCamelCase			)
								return f'''bert/{name}'''

				def create_tf_var(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			):
								snake_case_						:   List[str]           =  tf.dtypes.as_dtype(tensor.dtype			)
								snake_case_						:   Union[str, Any]           =  tf.get_variable(dtype=_UpperCamelCase			,    shape=tensor.shape			,    name=_UpperCamelCase			,    initializer=tf.zeros_initializer()			)
								session.run(tf.variables_initializer([tf_var]			)			)
								session.run(_UpperCamelCase			)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												snake_case_						:   Optional[int]           =  to_tf_var_name(_UpperCamelCase			)
												snake_case_						:   List[str]           =  state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose			):
																snake_case_						:   Optional[int]           =  torch_tensor.T
												snake_case_						:   str           =  create_tf_var(tensor=_UpperCamelCase			,    name=_UpperCamelCase			,    session=_UpperCamelCase			)
												tf.keras.backend.set_value(_UpperCamelCase			,    _UpperCamelCase			)
												snake_case_						:   Optional[Any]           =  session.run(_UpperCamelCase			)
												print(f'''Successfully created {tf_name}: {np.allclose(_UpperCamelCase			,    _UpperCamelCase			)}'''			)

								snake_case_						:   str           =  tf.train.Saver(tf.trainable_variables()			)
								saver.save(_UpperCamelCase			,    os.path.join(_UpperCamelCase			,    model_name.replace('''-'''			,    '''_'''			) + '''.ckpt'''			)			)






def 					lowerCamelCase_						(    _UpperCamelCase=None			)		->						int:






				"""simple docstring"""


				snake_case_						:   Any           =  argparse.ArgumentParser()
				parser.add_argument('''--model_name'''			,    type=_UpperCamelCase			,    required=_UpperCamelCase			,    help='''model name e.g. bert-base-uncased'''			)
				parser.add_argument(
				    '''--cache_dir'''			,    type=_UpperCamelCase			,    default=_UpperCamelCase			,    required=_UpperCamelCase			,    help='''Directory containing pytorch model'''			)
				parser.add_argument('''--pytorch_model_path'''			,    type=_UpperCamelCase			,    required=_UpperCamelCase			,    help='''/path/to/<pytorch-model-name>.bin'''			)
				parser.add_argument('''--tf_cache_dir'''			,    type=_UpperCamelCase			,    required=_UpperCamelCase			,    help='''Directory in which to save tensorflow model'''			)
				snake_case_						:   str           =  parser.parse_args(_UpperCamelCase			)

				snake_case_						:   Tuple           =  BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name			,    state_dict=torch.load(args.pytorch_model_path			)			,    cache_dir=args.cache_dir			,    )

				convert_pytorch_checkpoint_to_tf(model=_UpperCamelCase			,    ckpt_dir=args.tf_cache_dir			,    model_name=args.model_name			)


if __name__ == "__main__":
		main()







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase_    =    TypeVar('''KEY''')
lowerCAmelCase_    =    TypeVar('''VAL''')



@dataclass(frozen=_a,					slots=_a					)
class  __lowerCAmelCase	(      Generic[KEY, VAL]					):
			lowerCamelCase_					:					KEY
			lowerCamelCase_					:					VAL




class  __lowerCAmelCase	(      _Item					):






			def __init__(self   )					->       None:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )






			def __bool__(self   )					->       bool:


							'''simple docstring'''



							return False




lowerCAmelCase_    =    _DeletedItem()



class  __lowerCAmelCase	(      MutableMapping[KEY, VAL]					):






			def __init__(self ,							__magic_name__ = 8 ,							__magic_name__ = 0.75   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  initial_block_size
							snake_case_						:   list[_Item | None]           =  [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							snake_case_						:   List[str]           =  capacity_factor
							snake_case_						:   int           =  0






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return hash(__magic_name__   ) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return (ind + 1) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self._buckets[ind]
							if not stored:
											snake_case_						:   Optional[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											self._len += 1
											return True
							elif stored.key == key:
											snake_case_						:   List[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											return True
							else:
											return False






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Any           =  len(self._buckets   ) * self._capacity_factor
							return len(self   ) >= int(__magic_name__   )






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							if len(self._buckets   ) <= self._initial_block_size:
											return False
							snake_case_						:   int           =  len(self._buckets   ) * self._capacity_factor / 2
							return len(self   ) < limit






			def       lowerCamelCase  (self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self._buckets
							snake_case_						:   int           =  [None] * new_size
							snake_case_						:   Optional[int]           =  0
							for item in old_buckets:
											if item:
															self._add_item(item.key ,							item.val   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) * 2   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) // 2   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Iterator[int]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self._get_bucket_index(__magic_name__   )
							for _ in range(len(self._buckets   )   ):
											yield ind
											snake_case_						:   Tuple           =  self._get_next_ind(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											if self._try_set(__magic_name__ ,							__magic_name__ ,							__magic_name__   ):
															break






			def __setitem__(self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							if self._is_full():
											self._size_up()

							self._add_item(__magic_name__ ,							__magic_name__   )






			def __delitem__(self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   Optional[Any]           =  self._buckets[ind]
											if item is None:
															raise KeyError(__magic_name__   )
											if item is _deleted:
															continue
											if item.key == key:
															snake_case_						:   Union[str, Any]           =  _deleted
															self._len -= 1
															break
							if self._is_sparse():
											self._size_down()






			def __getitem__(self ,							__magic_name__   )					->       VAL:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   List[Any]           =  self._buckets[ind]
											if item is None:
															break
											if item is _deleted:
															continue
											if item.key == key:
															return item.val
							raise KeyError(__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self._len






			def __iter__(self   )					->       Iterator[KEY]:


							'''simple docstring'''



							yield from (item.key for item in self._buckets if item)






			def __repr__(self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  ''' ,'''.join(
							    F'''{item.key}: {item.val}''' for item in self._buckets if item   )
							return F'''HashMap({val_string})'''
