






import argparse

import torch

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


logging.set_verbosity_info()

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Dict				):
			# Initialise PyTorch model
			__a						:  Any									=     BertConfig.from_json_file(lowerCamelCase_				)
			print(f'''Building PyTorch model from configuration: {config}'''				)
			__a						:  List[str]									=     BertForPreTraining(lowerCamelCase_				)

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			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.'''
							)
							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










from typing import Dict

from .base import GenericTensor, Pipeline


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=None    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						if tokenize_kwargs is None:
										lowerCAmelCase__		  =  {}

						if truncation is not None:
										if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'						)
										lowerCAmelCase__		  =  truncation

						lowerCAmelCase__		  =  tokenize_kwargs

						lowerCAmelCase__		  =  {}
						if return_tensors is not None:
										lowerCAmelCase__		  =  return_tensors

						return preprocess_params, {}, postprocess_params
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.framework
						lowerCAmelCase__		  =  self.tokenizer(a_    ,return_tensors=a_    ,**a_						)
						return model_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model(**a_						)
						return model_outputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						# [0] is the first available tensor, logits or last_hidden_state.
						if return_tensors:
										return model_outputs[0]
						if self.framework == "pt":
										return model_outputs[0].tolist()
						elif self.framework == "tf":
										return model_outputs[0].numpy().tolist()



		def __call__(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return super().__call__(*a_    ,**a_						)
