






'''simple docstring'''


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = BertConfig.from_json_file(_A					)
						print(f"Building PyTorch model from configuration: {config}"					)
						UpperCamelCase__         = BertForPreTraining(_A					)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(_A						,      _A						,      _A					)

						# Save pytorch-model
						print(f"Save PyTorch model to {pytorch_dump_path}"					)
						torch.save(model.state_dict()						,      _A					)


if __name__ == "__main__":
					lowerCamelCase_				: Union[str, 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_				: Dict    	=							parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








'''simple docstring'''


import numpy as np

from transformers import Pipeline







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = np.max(_A						,      axis=-1						,      keepdims=_A					)
						UpperCamelCase__         = np.exp(outputs - maxes					)
						return shifted_exp / shifted_exp.sum(axis=-1						,      keepdims=_A					)







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def 				A      (   self							:						int ,      **lowercase							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = {}
									if "second_text" in kwargs:
															UpperCamelCase__         = kwargs["""second_text"""]
									return preprocess_kwargs, {}, {}






			def 				A      (   self							:						Optional[int] ,      lowercase							:						Union[str, Any] ,      lowercase							:						Any=None			)							->    Optional[int]:

									'''simple docstring'''







									return self.tokenizer(lowercase ,      text_pair=lowercase ,      return_tensors=self.framework			)






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    Any:

									'''simple docstring'''







									return self.model(**lowercase			)







			def 				A      (   self							:						List[str] ,      lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = model_outputs.logits[0].numpy()
									UpperCamelCase__         = softmax(lowercase			)

									UpperCamelCase__         = np.argmax(lowercase			)
									UpperCamelCase__         = self.model.config.idalabel[best_class]
									UpperCamelCase__         = probabilities[best_class].item()
									UpperCamelCase__         = logits.tolist()
									return {"label": label, "score": score, "logits": logits}
