



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 						UpperCamelCase      (  snake_case__			:       nn.ModuleList			,	snake_case__			:       nn.ModuleList			,	snake_case__			:       List[int]      )      ->	List[Any]:
   UpperCamelCase       :    Optional[int]		      =      nn.ModuleList([src_layers[i] for i in layers_to_copy]      )
   assert len(UpperCamelCase_      ) == len(UpperCamelCase_      ), F"""{len(UpperCamelCase_      )} != {len(UpperCamelCase_      )}"""
   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 						UpperCamelCase      (  snake_case__			:       Tuple			,	snake_case__			:       Dict      )      ->	Any:
   try:
      UpperCamelCase       :    List[str]		      =      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(UpperCamelCase_      )      )
def 						UpperCamelCase      (  snake_case__			:       int			,	snake_case__			:       Dict      )      ->	Optional[int]:
   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(UpperCamelCase_      )      )
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def 						UpperCamelCase      (  snake_case__			:       Union[str, PreTrainedModel]			,	snake_case__			:       Union[str, Path] = "student"			,	snake_case__			:       Union[int, None] = None			,	snake_case__			:       Union[int, None] = None			,	snake_case__			:       Optional[int]=False			,	snake_case__			:       Optional[Any]=None			,	snake_case__			:       Tuple=None			,	**snake_case__			:       str			,	)      ->	Any:
   UpperCamelCase       :    Union[str, Any]		      =      '''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(UpperCamelCase_			,	UpperCamelCase_      ):
      AutoTokenizer.from_pretrained(UpperCamelCase_      ).save_pretrained(UpperCamelCase_      )  # purely for convenience
      UpperCamelCase       :    List[Any]		      =      AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_      ).eval()
   else:
      assert isinstance(UpperCamelCase_			,	UpperCamelCase_      ), F"""teacher must be a model or string got type {type(UpperCamelCase_      )}"""
   UpperCamelCase       :    List[Any]		      =      teacher.config.to_diff_dict()

   try:
      UpperCamelCase       :    Tuple		      =      teacher.config.encoder_layers, teacher.config.decoder_layers
      if e is None:
         UpperCamelCase       :    int		      =      teacher_e
      if d is None:
         UpperCamelCase       :    Union[str, Any]		      =      teacher_d
      init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}      )
   except AttributeError:  # T5
      if hasattr(teacher.config			,	'num_encoder_layers'      ):
         UpperCamelCase       :    str		      =      teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
      else:
         UpperCamelCase       :    str		      =      teacher.config.num_layers, teacher.config.num_decoder_layers
      if e is None:
         UpperCamelCase       :    Union[str, Any]		      =      teacher_e
      if d is None:
         UpperCamelCase       :    Any		      =      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(UpperCamelCase_      )

   # Copy weights
   UpperCamelCase       :    Any		      =      teacher.config_class(**UpperCamelCase_      )
   UpperCamelCase       :    List[str]		      =      AutoModelForSeqaSeqLM.from_config(UpperCamelCase_      )
   # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
   UpperCamelCase       :    str		      =      student.load_state_dict(teacher.state_dict()			,	strict=UpperCamelCase_      )
   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       :    Union[str, Any]		      =      list(range(UpperCamelCase_      )      ), list(range(UpperCamelCase_      )      )
      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(UpperCamelCase_      )
      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       :    List[int]		      =      pick_layers_to_copy(UpperCamelCase_			,	UpperCamelCase_      )
   if d_layers_to_copy is None:
      UpperCamelCase       :    List[int]		      =      pick_layers_to_copy(UpperCamelCase_			,	UpperCamelCase_      )

   try:
      if hasattr(
          UpperCamelCase_			,	'prophetnet'      ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
         copy_layers(teacher.prophetnet.encoder.layers			,	student.prophetnet.encoder.layers			,	UpperCamelCase_      )
         copy_layers(teacher.prophetnet.decoder.layers			,	student.prophetnet.decoder.layers			,	UpperCamelCase_      )
      else:
         copy_layers(teacher.model.encoder.layers			,	student.model.encoder.layers			,	UpperCamelCase_      )
         copy_layers(teacher.model.decoder.layers			,	student.model.decoder.layers			,	UpperCamelCase_      )
   except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
      copy_layers(teacher.encoder.block			,	student.encoder.block			,	UpperCamelCase_      )
      copy_layers(teacher.decoder.block			,	student.decoder.block			,	UpperCamelCase_      )
   logger.info(
       F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""      )
   UpperCamelCase       :    Dict		      =      {
       '''teacher_type''': teacher.config.model_type,
       '''copied_encoder_layers''': e_layers_to_copy,
       '''copied_decoder_layers''': d_layers_to_copy,
   }
   student.save_pretrained(UpperCamelCase_      )
   # 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)







'''simple docstring'''





from functools import reduce

__lowerCamelCase												=						(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)


def        a__     (					UpperCamelCase_						:      str = N						):
   return max(
       # mypy cannot properly interpret reduce
       int(reduce(lambda UpperCamelCase_,   UpperCamelCase_						: str(int(UpperCamelCase_						) * int(UpperCamelCase_						)						),   n[i : i + 13]						)						)
       for i in range(len(UpperCamelCase_						) - 12						)						)


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



