
"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_rembert import RemBertTokenizer
else:
					UpperCAmelCase__      :    List[Any]    							= None

UpperCAmelCase__      :    Any    							= logging.get_logger(__name__)
UpperCAmelCase__      :    int    							= {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__      :    List[Any]    							= {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase__      :    Dict    							= {
    "google/rembert": 2_5_6,
}

UpperCAmelCase__      :    Tuple    							= "▁"



class 	lowerCAmelCase_						(SCREAMING_SNAKE_CASE__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Tuple					       =   VOCAB_FILES_NAMES
				__UpperCamelCase      :						Tuple					       =   PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase      :						Optional[Any]					       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase      :						Tuple					       =   RemBertTokenizer



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__="[CLS]"   ,       SCREAMING_SNAKE_CASE__="[SEP]"   ,       SCREAMING_SNAKE_CASE__="<unk>"   ,       SCREAMING_SNAKE_CASE__="[SEP]"   ,       SCREAMING_SNAKE_CASE__="<pad>"   ,       SCREAMING_SNAKE_CASE__="[CLS]"   ,       SCREAMING_SNAKE_CASE__="[MASK]"   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   AddedToken(A__   ,       lstrip=A__   ,       rstrip=A__    ) if isinstance(A__   ,       A__    ) else mask_token

											super().__init__(
											    A__   ,       tokenizer_file=A__   ,       do_lower_case=A__   ,       remove_space=A__   ,       keep_accents=A__   ,       bos_token=A__   ,       eos_token=A__   ,       unk_token=A__   ,       sep_token=A__   ,       pad_token=A__   ,       cls_token=A__   ,       mask_token=A__   ,       **A__   ,       )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   do_lower_case
											SCREAMING_SNAKE_CASE__						:       str           =   remove_space
											SCREAMING_SNAKE_CASE__						:       str           =   keep_accents
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   vocab_file
											SCREAMING_SNAKE_CASE__						:       Dict           =   False if not self.vocab_file else True



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [self.cls_token_id]
											if token_ids_a is None:
																		return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False    )					->		Dict:



											"""simple docstring"""




											if already_has_special_tokens:
																		if token_ids_a is not None:
																									raise ValueError(
																									    """You should not supply a second sequence if the provided sequence of """
																									    """ids is already formatted with special tokens for the model."""    )
																		return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

											if token_ids_a is not None:
																		return [1] + ([0] * len(A__    )) + [1] + ([0] * len(A__    )) + [1]
											return [1] + ([0] * len(A__    )) + [1]



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:       Dict           =   [self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep    ) * [0]
											return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		int:



											"""simple docstring"""




											if not os.path.isdir(A__    ):
																		logger.error("""Vocabulary path ({}) should be a directory""".format(A__    )    )
																		return
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   os.path.join(
											    A__   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

											if os.path.abspath(self.vocab_file    ) != os.path.abspath(A__    ):
																		copyfile(self.vocab_file   ,       A__    )

											return (out_vocab_file,)






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

					lowercase							=						(
					    ('''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(lowerCAmelCase__		):
										os.makedirs(lowerCAmelCase__		)

					lowercase							=						model.state_dict()

					def to_tf_var_name(lowerCAmelCase__		):
										for patt, repl in iter(lowerCAmelCase__		):
															lowercase							=						name.replace(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return f'bert/{name}'

					def create_tf_var(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
										lowercase							=						tf.dtypes.as_dtype(tensor.dtype		)
										lowercase							=						tf.get_variable(dtype=lowerCAmelCase__ ,			shape=tensor.shape ,			name=lowerCAmelCase__ ,			initializer=tf.zeros_initializer()		)
										session.run(tf.variables_initializer([tf_var]		)		)
										session.run(lowerCAmelCase__		)
										return tf_var

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

										lowercase							=						tf.train.Saver(tf.trainable_variables()		)
										saver.save(lowerCAmelCase__ ,			os.path.join(lowerCAmelCase__ ,			model_name.replace('''-''' ,			'''_'''		) + '''.ckpt'''		)		)



def        UpperCamelCase    (							lowerCAmelCase__=None		):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''model name e.g. bert-base-uncased'''		)
					parser.add_argument(
					    '''--cache_dir''' ,			type=lowerCAmelCase__ ,			default=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory containing pytorch model'''		)
					parser.add_argument('''--pytorch_model_path''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''/path/to/<pytorch-model-name>.bin'''		)
					parser.add_argument('''--tf_cache_dir''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory in which to save tensorflow model'''		)
					lowercase							=						parser.parse_args(lowerCAmelCase__		)

					lowercase							=						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=lowerCAmelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name		)


if __name__ == "__main__":
							main()




