




import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase = None			,						_lowercase = None			,						_lowercase = None			,						):

     '''simple docstring'''


     if config_name_or_path is None:
          UpperCAmelCase_      :							Optional[Any]       =      '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

     if generator_tokenizer_name_or_path is None:
          UpperCAmelCase_      :							List[str]       =      generator_name_or_path

     if question_encoder_tokenizer_name_or_path is None:
          UpperCAmelCase_      :							str       =      question_encoder_name_or_path

     UpperCAmelCase_      :							Dict       =      RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

     # Save model.
     UpperCAmelCase_      :							Union[str, Any]       =      RagConfig.from_pretrained(_lowercase				)
     UpperCAmelCase_      :							Optional[int]       =      AutoConfig.from_pretrained(_lowercase				)
     UpperCAmelCase_      :							str       =      AutoConfig.from_pretrained(_lowercase				)

     UpperCAmelCase_      :							Any       =      gen_config
     UpperCAmelCase_      :							Optional[int]       =      question_encoder_config

     UpperCAmelCase_      :							List[str]       =      model_class.from_pretrained_question_encoder_generator(
         _lowercase			,						_lowercase			,						config=_lowercase				)
     rag_model.save_pretrained(_lowercase				)

     # Sanity check.
     model_class.from_pretrained(_lowercase				)

     # Save tokenizers.
     UpperCAmelCase_      :							int       =      AutoTokenizer.from_pretrained(_lowercase				)
     gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''				)
     UpperCAmelCase_      :							Optional[Any]       =      AutoTokenizer.from_pretrained(_lowercase				)
     question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     parser.add_argument(
         '--model_type',
         choices=['rag_sequence', 'rag_token'],
         required=True,
         type=str,
         help='RAG model type: rag_sequence, rag_token',
     )
     parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
     parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
     parser.add_argument(
         '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
     )

     parser.add_argument(
         '--generator_tokenizer_name_or_path',
         type=str,
         help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
     )
     parser.add_argument(
         '--question_encoder_tokenizer_name_or_path',
         type=str,
         help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
     )
     parser.add_argument(
         '--config_name_or_path',
         type=str,
         help=(
             'Identifier of the model config to use, if not provided, resolves to a base config for a given'
             ' ``model_type``'
         ),
     )

     __a          =			parser.parse_args()

     __a          =			Path(args.dest)
     dest_dir.mkdir(exist_ok=True)

     consolidate(
         args.model_type,
         args.generator_name_or_path,
         args.question_encoder_name_or_path,
         dest_dir,
         args.config_name_or_path,
         args.generator_tokenizer_name_or_path,
         args.question_encoder_tokenizer_name_or_path,
     )





def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def lowerCamelCase__       (      _lowercase			,						_lowercase=0				):

     '''simple docstring'''


     return sorted(_lowercase			,						key=lambda _lowercase				: x[column]				)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(points_counts - 1				):
          for j in range(i + 1			,						_lowercase				):
               UpperCAmelCase_      :							Optional[int]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[Any]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(min(6			,						points_counts - 1				)			,						_lowercase				):
          for j in range(max(0			,						i - 6				)			,						_lowercase				):
               UpperCAmelCase_      :							List[str]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[int]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase			,						_lowercase				)

     # recursion
     UpperCAmelCase_      :							Optional[int]       =      points_counts // 2
     UpperCAmelCase_      :							List[Any]       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[:mid]			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
     UpperCAmelCase_      :							Union[str, Any]       =      min(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							str       =      []
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
               cross_strip.append(_lowercase				)

     UpperCAmelCase_      :							Optional[Any]       =      dis_between_closest_in_strip(
         _lowercase			,						len(_lowercase				)			,						_lowercase				)
     return min(_lowercase			,						_lowercase				)







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      column_based_sort(_lowercase			,						column=0				)
     UpperCAmelCase_      :							List[Any]       =      column_based_sort(_lowercase			,						column=1				)
     return (
         closest_pair_of_points_sqr(
             _lowercase			,						_lowercase			,						_lowercase				)
     ) ** 0.5


if __name__ == "__main__":
     __a          =			[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
     print('Distance:', closest_pair_of_points(points, len(points)))