





"""simple docstring"""





import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


_lowerCamelCase  					=     collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)




def 		lowerCAmelCase_     (  lowercase_       :							Optional[int]				):

 '''simple docstring'''






 print(F'''Reading saved model from {model_file}'''				)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	torch.load(_UpperCamelCase			,				map_location=lambda lowercase_			,				lowercase_				: default_restore_location(_UpperCamelCase			,				'''cpu'''				)				)
 return CheckpointState(**_UpperCamelCase				)





class       snake_case		:

      def __init__(      self       :int						,							_lowerCamelCase       :Path						):
       __SCREAMING_SNAKE_CASE			:      str 							=	src_file

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       raise NotImplementedError



      @staticmethod
      def 						SCREAMING_SNAKE_CASE_      (      _lowerCamelCase       :str						,							*_lowerCamelCase       :Optional[int]						,							**_lowerCamelCase       :List[str]						):






       if comp_type.startswith('''c'''						):
        return DPRContextEncoderState(*_lowerCamelCase						,							**_lowerCamelCase						)
       if comp_type.startswith('''q'''						):
        return DPRQuestionEncoderState(*_lowerCamelCase						,							**_lowerCamelCase						)
       if comp_type.startswith('''r'''						):
        return DPRReaderState(*_lowerCamelCase						,							**_lowerCamelCase						)
       else:
        raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''						)







class       snake_case		(       __UpperCAmelCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''						)[0]						)						)
       print(f'''Loading DPR biencoder from {self.src_file}'''						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	load_states_from_checkpoint(self.src_file						)
       __SCREAMING_SNAKE_CASE			:      int 							=	model.ctx_encoder, "ctx_model."
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       __SCREAMING_SNAKE_CASE			:      Dict 							=	{"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
       for key, value in saved_state.model_dict.items():
        if key.startswith(_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE			:      List[str] 							=	key[len(_lowerCamelCase						) :]
         if not key.startswith('''encode_proj.'''						):
          __SCREAMING_SNAKE_CASE			:      Any 							=	"bert_model." + key
         __SCREAMING_SNAKE_CASE			:      Tuple 							=	value
       encoder.load_state_dict(_lowerCamelCase						)
       return model







class       snake_case		(       __UpperCAmelCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''						)[0]						)						)
       print(f'''Loading DPR biencoder from {self.src_file}'''						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	load_states_from_checkpoint(self.src_file						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	model.question_encoder, "question_model."
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       __SCREAMING_SNAKE_CASE			:      str 							=	{"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
       for key, value in saved_state.model_dict.items():
        if key.startswith(_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	key[len(_lowerCamelCase						) :]
         if not key.startswith('''encode_proj.'''						):
          __SCREAMING_SNAKE_CASE			:      Dict 							=	"bert_model." + key
         __SCREAMING_SNAKE_CASE			:      List[str] 							=	value
       encoder.load_state_dict(_lowerCamelCase						)
       return model







class       snake_case		(       __UpperCAmelCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						):
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''						)[0]						)						)
       print(f'''Loading DPR reader from {self.src_file}'''						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	load_states_from_checkpoint(self.src_file						)
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       __SCREAMING_SNAKE_CASE			:      int 							=	{
           "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
       }
       for key, value in saved_state.model_dict.items():
        if key.startswith('''encoder.'''						) and not key.startswith('''encoder.encode_proj'''						):
         __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	"encoder.bert_model." + key[len('''encoder.'''						) :]
        __SCREAMING_SNAKE_CASE			:      Dict 							=	value
       model.span_predictor.load_state_dict(_lowerCamelCase						)
       return model







def 		lowerCAmelCase_     (  lowercase_       :							Optional[Any]			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]				):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	Path(_UpperCamelCase				)
 dest_dir.mkdir(exist_ok=_UpperCamelCase				)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	DPRState.from_type(_UpperCamelCase			,				src_file=_UpperCamelCase				)
 __SCREAMING_SNAKE_CASE			:      Any 							=	dpr_state.load_dpr_model()
 model.save_pretrained(_UpperCamelCase				)
 model.from_pretrained(_UpperCamelCase				)  # sanity check


if __name__ == "__main__":
    _lowerCamelCase  					=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
    )
    parser.add_argument(
        '''--src''',
        type=str,
        help=(
            '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
            ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
            ''' \'retriever\' checkpoints.'''
        ),
    )
    parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
    _lowerCamelCase  					=     parser.parse_args()

    _lowerCamelCase  					=     Path(args.src)
    _lowerCamelCase  					=     f'converted-{src_file.name}' if args.dest is None else args.dest
    _lowerCamelCase  					=     Path(dest_dir)
    assert src_file.exists()
    assert (
        args.type is not None
    ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
    convert(args.type, src_file, dest_dir)








"""simple docstring"""





def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Any			,				lowercase_       :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	index
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	2 * index + 1
 __SCREAMING_SNAKE_CASE			:      Any 							=	2 * index + 2
 if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      str 							=	left_index

 if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	right_index

 if largest != index:
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[str] 							=	unsorted[index], unsorted[largest]
  heapify(lowercase_			,				lowercase_			,				lowercase_		)




def 		lowerCAmelCase_     (  lowercase_       :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	len(lowercase_		)
 for i in range(n // 2 - 1			,				-1			,				-1		):
  heapify(lowercase_			,				lowercase_			,				lowercase_		)
 for i in range(n - 1			,				0			,				-1		):
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	unsorted[i], unsorted[0]
  heapify(lowercase_			,				0			,				lowercase_		)
 return unsorted


if __name__ == "__main__":
    _lowerCamelCase  					=     input('''Enter numbers separated by a comma:\n''').strip()
    _lowerCamelCase  					=     [int(item) for item in user_input.split(''',''')]
    print(heap_sort(unsorted))

