from __future__ import annotations

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						{1}
       _A=						[1]
       for _ in range(2							,       depth + 1 ):
              _A=						[*previous_coefficients, 0]
              _A=						[0, *previous_coefficients]
              _A=						[]
              for x, y in zip(lowerCAmelCase_							,       lowerCAmelCase_ ):
                     coefficients.add(x + y )
                     previous_coefficients.append(x + y )
       return coefficients

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						set()
       for number in unique_coefficients:
              _A=						2
              _A=						number
              while divisor**2 <= copy_number:
                     _A=						0
                     while copy_number % divisor == 0:
                            copy_number //= divisor
                            multiplicity += 1
                     if multiplicity >= 2:
                            non_squarefrees.add(lowerCAmelCase_ )
                            break
                     divisor += 1

       return unique_coefficients.difference(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_ = 51 )		->    int:




       '''simple docstring'''






       _A=						get_pascal_triangle_unique_coefficients(lowerCAmelCase_ )
       _A=						get_squarefrees(lowerCAmelCase_ )
       return sum(lowerCAmelCase_ )


if __name__ == "__main__":
      print(F"{solution() = }")
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


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

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    CheckpointState:




       '''simple docstring'''






       print(F"Reading saved model from {model_file}" )
       _A=						torch.load(lowerCAmelCase_							,       map_location=lambda lowerCAmelCase_							,       lowerCAmelCase_ : default_restore_location(lowerCAmelCase_							,       'cpu' ) )
       return CheckpointState(**lowerCAmelCase_ )





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__							):
            _A=						src_file



     def       a__	(       self							):
            raise NotImplementedError





     @staticmethod
     def       a__	(       lowerCAmelCase__					,   *lowerCAmelCase__					,   **lowerCAmelCase__							):
            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   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.ctx_encoder, 'ctx_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'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__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR biencoder from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            _A,      _A=						model.question_encoder, 'question_model.'
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{'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__							):
                          _A=						key[len(lowerCAmelCase__							) :]
                          if not key.startswith('encode_proj.'							):
                                 _A=						'bert_model.' + key
                          _A=						value
            encoder.load_state_dict(lowerCAmelCase__							)
            return model





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            _A=						DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
            print(f"Loading DPR reader from {self.src_file}"							)
            _A=						load_states_from_checkpoint(self.src_file							)
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _A=						{
                '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'							):
                          _A=						'encoder.bert_model.' + key[len('encoder.'							) :]
                   _A=						value
            model.span_predictor.load_state_dict(lowerCAmelCase__							)
            return model


def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       _A=						Path(lowerCAmelCase_ )
       dest_dir.mkdir(exist_ok=lowerCAmelCase_ )

       _A=						DPRState.from_type(lowerCAmelCase_							,       src_file=lowerCAmelCase_ )
       _A=						dpr_state.load_dpr_model()
       model.save_pretrained(lowerCAmelCase_ )
       model.from_pretrained(lowerCAmelCase_ )  # sanity check


if __name__ == "__main__":
      UpperCAmelCase_    		=				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.''')
      UpperCAmelCase_    		=				parser.parse_args()

      UpperCAmelCase_    		=				Path(args.src)
      UpperCAmelCase_    		=				F"converted-{src_file.name}" if args.dest is None else args.dest
      UpperCAmelCase_    		=				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)