





import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)
def      A__       (    SCREAMING_SNAKE_CASE_   :      nn.ModuleList				,	SCREAMING_SNAKE_CASE_   :      nn.ModuleList				,	SCREAMING_SNAKE_CASE_   :      List[int]						)  ->  None:






   """simple docstring"""







   _UpperCAmelCase           =   nn.ModuleList([src_layers[i] for i in layers_to_copy]						)
   assert len(SCREAMING_SNAKE_CASE_						) == len(SCREAMING_SNAKE_CASE_						), F'''{len(SCREAMING_SNAKE_CASE_						)} != {len(SCREAMING_SNAKE_CASE_						)}'''
   dest_layers.load_state_dict(layers_to_copy.state_dict()						)


UpperCAmelCase_           =	{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
UpperCAmelCase_           =	{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Any						)  ->  str:






   """simple docstring"""







   try:
      _UpperCAmelCase           =   LAYERS_TO_COPY[n_teacher][n_student]
      return val
   except KeyError:
      if n_student != n_teacher:
         warnings.warn(
             F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
             F''' {n_student}'''						)
      return list(range(SCREAMING_SNAKE_CASE_						)						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  List[int]:






   """simple docstring"""







   if n_student > n_teacher:
      raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'''						)
   elif n_teacher == n_student:
      return list(range(SCREAMING_SNAKE_CASE_						)						)
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, PreTrainedModel]				,	SCREAMING_SNAKE_CASE_   :      Union[str, Path] = "student"				,	SCREAMING_SNAKE_CASE_   :      Union[int, None] = None				,	SCREAMING_SNAKE_CASE_   :      Union[int, None] = None				,	SCREAMING_SNAKE_CASE_   :      List[Any]=False				,	SCREAMING_SNAKE_CASE_   :      Dict=None				,	SCREAMING_SNAKE_CASE_   :      Union[str, Any]=None				,	**SCREAMING_SNAKE_CASE_   :      int				,	)  ->  Tuple[PreTrainedModel, List[int], List[int]]:






   """simple docstring"""







   _UpperCAmelCase           =   '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
   assert (e is not None) or (d is not None), _msg
   if isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_						).save_pretrained(SCREAMING_SNAKE_CASE_						)  # purely for convenience
      _UpperCAmelCase           =   AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_						).eval()
   else:
      assert isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						), F'''teacher must be a model or string got type {type(SCREAMING_SNAKE_CASE_						)}'''
   _UpperCAmelCase           =   teacher.config.to_diff_dict()

   try:
      _UpperCAmelCase       ,      _UpperCAmelCase           =   teacher.config.encoder_layers, teacher.config.decoder_layers
      if e is None:
         _UpperCAmelCase           =   teacher_e
      if d is None:
         _UpperCAmelCase           =   teacher_d
      init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}						)
   except AttributeError:  # T5
      if hasattr(teacher.config				,	'''num_encoder_layers'''						):
         _UpperCAmelCase       ,      _UpperCAmelCase           =   teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
      else:
         _UpperCAmelCase       ,      _UpperCAmelCase           =   teacher.config.num_layers, teacher.config.num_decoder_layers
      if e is None:
         _UpperCAmelCase           =   teacher_e
      if d is None:
         _UpperCAmelCase           =   teacher_d
      if hasattr(teacher.config				,	'''num_encoder_layers'''						):
         init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}						)
      else:
         init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}						)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
   init_kwargs.update(SCREAMING_SNAKE_CASE_						)

   # Copy weights
   _UpperCAmelCase           =   teacher.config_class(**SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   AutoModelForSeqaSeqLM.from_config(SCREAMING_SNAKE_CASE_						)
   # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
   _UpperCAmelCase           =   student.load_state_dict(teacher.state_dict()				,	strict=SCREAMING_SNAKE_CASE_						)
   assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

   if copy_first_teacher_layers:  # Our copying is done. We just log and save
      _UpperCAmelCase       ,      _UpperCAmelCase           =   list(range(SCREAMING_SNAKE_CASE_						)						), list(range(SCREAMING_SNAKE_CASE_						)						)
      logger.info(
          F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
          F''' {save_path}'''						)
      student.save_pretrained(SCREAMING_SNAKE_CASE_						)
      return student, e_layers_to_copy, d_layers_to_copy

   # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
   if e_layers_to_copy is None:
      _UpperCAmelCase           =   pick_layers_to_copy(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
   if d_layers_to_copy is None:
      _UpperCAmelCase           =   pick_layers_to_copy(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   try:
      if hasattr(
          SCREAMING_SNAKE_CASE_				,	'''prophetnet'''						):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
         copy_layers(teacher.prophetnet.encoder.layers				,	student.prophetnet.encoder.layers				,	SCREAMING_SNAKE_CASE_						)
         copy_layers(teacher.prophetnet.decoder.layers				,	student.prophetnet.decoder.layers				,	SCREAMING_SNAKE_CASE_						)
      else:
         copy_layers(teacher.model.encoder.layers				,	student.model.encoder.layers				,	SCREAMING_SNAKE_CASE_						)
         copy_layers(teacher.model.decoder.layers				,	student.model.decoder.layers				,	SCREAMING_SNAKE_CASE_						)
   except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
      copy_layers(teacher.encoder.block				,	student.encoder.block				,	SCREAMING_SNAKE_CASE_						)
      copy_layers(teacher.decoder.block				,	student.decoder.block				,	SCREAMING_SNAKE_CASE_						)
   logger.info(
       F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''						)
   _UpperCAmelCase           =   {
       '''teacher_type''': teacher.config.model_type,
       '''copied_encoder_layers''': e_layers_to_copy,
       '''copied_decoder_layers''': d_layers_to_copy,
   }
   student.save_pretrained(SCREAMING_SNAKE_CASE_						)
   # Save information about copying for easier reproducibility

   return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
   fire.Fire(create_student_by_copying_alternating_layers)






def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 2_00_00_00						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   [0 for i in range(n + 1						)]
   _UpperCAmelCase           =   1
   _UpperCAmelCase           =   1

   for i in range(2				,	int(n**0.5						) + 1						):
      if primality_list[i] == 0:
         for j in range(i * i				,	n + 1				,	SCREAMING_SNAKE_CASE_						):
            _UpperCAmelCase           =   1
   _UpperCAmelCase           =   0
   for i in range(SCREAMING_SNAKE_CASE_						):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


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