




from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase							:						Optional[Any]					       =  numpy.array([0, 0])
lowerCamelCase							:						Optional[int]					       =  numpy.array([0.5, 0.866_0254])
lowerCamelCase							:						Optional[Any]					       =  numpy.array([1, 0])
lowerCamelCase							:						Dict					       =  [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			list[numpy.ndarray]      ,				lowercase      :			int	):


      '''simple docstring'''



      lowerCamelCase_							=       initial_vectors
      for _ in range(lowercase	):
            lowerCamelCase_							=       iteration_step(lowercase	)
      return vectors

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			list[numpy.ndarray]	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      for i, start_vector in enumerate(vectors[:-1]	):
            lowerCamelCase_							=       vectors[i + 1]
            new_vectors.append(lowercase	)
            lowerCamelCase_							=       end_vector - start_vector
            new_vectors.append(start_vector + difference_vector / 3	)
            new_vectors.append(
                start_vector + difference_vector / 3 + rotate(difference_vector / 3      ,				60	)	)
            new_vectors.append(start_vector + difference_vector * 2 / 3	)
      new_vectors.append(vectors[-1]	)
      return new_vectors

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			numpy.ndarray      ,				lowercase      :			float	):


      '''simple docstring'''



      lowerCamelCase_							=       numpy.radians(lowercase	)
      lowerCamelCase_	,							lowerCamelCase_							=       numpy.cos(lowercase	), numpy.sin(lowercase	)
      lowerCamelCase_							=       numpy.array(((c, -s), (s, c))	)
      return numpy.dot(lowercase      ,				lowercase	)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			list[numpy.ndarray]	):


      '''simple docstring'''



      lowerCamelCase_							=       plt.gca()
      axes.set_aspect('equal'	)

      # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
      # y-coordinates as inputs, which are constructed from the vector-list using
      # zip()
      lowerCamelCase_	,							lowerCamelCase_							=       zip(*lowercase	)
      plt.plot(lowercase      ,				lowercase	)
      plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase							:						Union[str, Any]					       =  iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)







"""simple docstring"""


import argparse
from pathlib import Path

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


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Path       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		):
 if config_name_or_path is None:
  lowercase__       :       Tuple				=						"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

 if generator_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						question_encoder_name_or_path

 lowercase__       :       Any				=						RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

 # Save model.
 lowercase__       :       Union[str, Any]				=						RagConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						AutoConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__  )

 lowercase__       :       Union[str, Any]				=						gen_config
 lowercase__       :       Any				=						question_encoder_config

 lowercase__       :       str				=						model_class.from_pretrained_question_encoder_generator(
     lowerCamelCase__       ,		lowerCamelCase__       ,		config=lowerCamelCase__  )
 rag_model.save_pretrained(lowerCamelCase__  )

 # Sanity check.
 model_class.from_pretrained(lowerCamelCase__  )

 # Save tokenizers.
 lowercase__       :       Optional[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""  )
 lowercase__       :       Optional[int]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""  )


if __name__ == "__main__":
   __snake_case    					=					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``'
       ),
   )

   __snake_case    					=					parser.parse_args()

   __snake_case    					=					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,
   )