




'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			RoFormerConfig.from_json_file(__UpperCamelCase    )
		print(f'''Building PyTorch model from configuration: {config}'''    )
		_snake_case      			=			RoFormerForMaskedLM(__UpperCamelCase    )

		# Load weights from tf checkpoint
		load_tf_weights_in_roformer(__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase    )

		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
		torch.save(model.state_dict()		,				__UpperCamelCase		,				_use_new_zipfile_serialization=__UpperCamelCase    )


if __name__ == "__main__":
						_lowerCamelCase				:		Any				      =    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(
						    '--bert_config_file',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained BERT 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				:		Any				      =    parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













"""simple docstring"""


import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


class    snake_case						(				tf.keras.layers.Layer		):


   '''simple docstring'''

   def __init__(      self					:	List[str],			_lowerCamelCase					:	List,			_lowerCamelCase					:	bool,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	str = "longest",			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	int = 5_12,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     super().__init__()
     if use_fast_bert_tokenizer:
       __A		     =					FastBertTokenizer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case_nfd_strip_accents=_lowerCamelCase,			**_lowerCamelCase    )
     else:
       __A		     =					tf.lookup.StaticVocabularyTable(
           tf.lookup.KeyValueTensorInitializer(
               keys=_lowerCamelCase,			key_dtype=tf.string,			values=tf.range(tf.size(_lowerCamelCase,			out_type=tf.intaa    ),			dtype=tf.intaa    ),			value_dtype=tf.intaa,			),			num_oov_buckets=1,			)
       __A		     =					BertTokenizerLayer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case=_lowerCamelCase,			**_lowerCamelCase    )

     __A		     =					vocab_list
     __A		     =					do_lower_case
     __A		     =					cls_token_id or vocab_list.index('''[CLS]'''    )
     __A		     =					sep_token_id or vocab_list.index('''[SEP]'''    )
     __A		     =					pad_token_id or vocab_list.index('''[PAD]'''    )
     __A		     =					ShrinkLongestTrimmer(max_length - 3,			axis=1    )  # Allow room for special tokens
     __A		     =					max_length
     __A		     =					padding
     __A		     =					truncation
     __A		     =					pad_to_multiple_of
     __A		     =					return_token_type_ids
     __A		     =					return_attention_mask


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	int,			_lowerCamelCase					:	"PreTrainedTokenizerBase",			**_lowerCamelCase					:	List[Any]    ):  # noqa: F821

     '''simple docstring'''
     __A		     =					kwargs.pop('''do_lower_case''',			_lowerCamelCase    )
     __A		     =					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     __A		     =					kwargs.pop('''cls_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     __A		     =					kwargs.pop('''sep_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     __A		     =					kwargs.pop('''pad_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     __A		     =					tokenizer.get_vocab()
     __A		     =					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()],			key=lambda _lowerCamelCase    : x[1]    )
     __A		     =					[entry[0] for entry in vocab]
     return cls(
         vocab_list=_lowerCamelCase,			do_lower_case=_lowerCamelCase,			cls_token_id=_lowerCamelCase,			sep_token_id=_lowerCamelCase,			pad_token_id=_lowerCamelCase,			**_lowerCamelCase,			)


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	Optional[Any],			_lowerCamelCase					:	Union[str, os.PathLike],			*_lowerCamelCase					:	Any,			**_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     try:
       __A		     =					BertTokenizer.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )
     except:  # noqa: E722
       from .tokenization_bert_fast import BertTokenizerFast

       __A		     =					BertTokenizerFast.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )
     return cls.from_tokenizer(_lowerCamelCase,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     if self.do_lower_case:
       __A		     =					case_fold_utfa(_lowerCamelCase    )
     __A		     =					self.tf_tokenizer.tokenize(_lowerCamelCase    )
     return tokens.merge_dims(1,			-1    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Dict=None,			_lowerCamelCase					:	Optional[int]=None,			_lowerCamelCase					:	Union[str, Any]=None,			_lowerCamelCase					:	Any=None,			_lowerCamelCase					:	List[Any]=None,			_lowerCamelCase					:	str=None,			_lowerCamelCase					:	Dict=None,			):

     '''simple docstring'''
     if padding is None:
       __A		     =					self.padding
     if padding not in ("longest", "max_length"):
       raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''    )
     if max_length is not None and text_pair is not None:
       # Because we have to instantiate a Trimmer to do it properly
       raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''    )
     if max_length is None:
       __A		     =					self.max_length
     if truncation is None:
       __A		     =					self.truncation
     if pad_to_multiple_of is None:
       __A		     =					self.pad_to_multiple_of
     if return_token_type_ids is None:
       __A		     =					self.return_token_type_ids
     if return_attention_mask is None:
       __A		     =					self.return_attention_mask
     if not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     if text_pair is not None and not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     if text_pair is not None:
       if text.shape.rank > 1:
         raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''    )
       if text_pair.shape.rank > 1:
         raise ValueError('''text_pair should not be multidimensional!'''    )
     if text.shape.rank == 2:
       __A     ,       __A		     =					text[:, 0], text[:, 1]
     __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
     if text_pair is None:  # Unpaired text
       if truncation:
         __A		     =					text[:, : max_length - 2]  # Allow room for special tokens
       __A     ,       __A		     =					combine_segments(
           (text,),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     else:  # Paired text
       __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
       if truncation:
         __A     ,       __A		     =					self.paired_trimmer.trim([text, text_pair]    )
       __A     ,       __A		     =					combine_segments(
           (text, text_pair),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     if padding == "longest":
       __A		     =					input_ids.bounding_shape(axis=1    )
       if pad_to_multiple_of is not None:
         # No ceiling division in tensorflow, so we negate floordiv instead
         __A		     =					pad_to_multiple_of * (-tf.math.floordiv(-pad_length,			_lowerCamelCase    ))
     else:
       __A		     =					max_length

     __A     ,       __A		     =					pad_model_inputs(_lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
     __A		     =					{'''input_ids''': input_ids}
     if return_attention_mask:
       __A		     =					attention_mask
     if return_token_type_ids:
       __A     ,       __A		     =					pad_model_inputs(
           _lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
       __A		     =					token_type_ids
     return output






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     return {
         "vocab_list": self.vocab_list,
         "do_lower_case": self.do_lower_case,
         "cls_token_id": self.cls_token_id,
         "sep_token_id": self.sep_token_id,
         "pad_token_id": self.pad_token_id,
     }




