




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


lowerCamelCase	:	int				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       None:
   snake_case				:    Dict           =	nn.ModuleList([src_layers[i] for i in layers_to_copy]					)
   assert len(lowercase					) == len(lowercase					), f"""{len(lowercase					)} != {len(lowercase					)}"""
   dest_layers.load_state_dict(layers_to_copy.state_dict()					)


lowerCamelCase	:	Optional[Any]				=   {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowerCamelCase	:	Dict				=   {
    # 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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Dict:
   try:
      snake_case				:    Union[str, Any]           =	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(lowercase					)					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[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(lowercase					)					)
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase = "student"						,lowercase = None						,lowercase = None						,lowercase=False						,lowercase=None						,lowercase=None						,**lowercase						,)  ->       Tuple[PreTrainedModel, List[int], List[int]]:
   snake_case				:    Optional[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(lowercase						,lowercase					):
      AutoTokenizer.from_pretrained(lowercase					).save_pretrained(lowercase					)  # purely for convenience
      snake_case				:    Optional[int]           =	AutoModelForSeqaSeqLM.from_pretrained(lowercase					).eval()
   else:
      assert isinstance(lowercase						,lowercase					), f"""teacher must be a model or string got type {type(lowercase					)}"""
   snake_case				:    Any           =	teacher.config.to_diff_dict()

   try:
      snake_case     , snake_case				:    str           =	teacher.config.encoder_layers, teacher.config.decoder_layers
      if e is None:
         snake_case				:    Tuple           =	teacher_e
      if d is None:
         snake_case				:    Optional[int]           =	teacher_d
      init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d}					)
   except AttributeError:  # T5
      if hasattr(teacher.config						,"""num_encoder_layers"""					):
         snake_case     , snake_case				:    Optional[int]           =	teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
      else:
         snake_case     , snake_case				:    int           =	teacher.config.num_layers, teacher.config.num_decoder_layers
      if e is None:
         snake_case				:    Dict           =	teacher_e
      if d is None:
         snake_case				:    int           =	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(lowercase					)

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

   try:
      if hasattr(
          lowercase						,"""prophetnet"""					):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
         copy_layers(teacher.prophetnet.encoder.layers						,student.prophetnet.encoder.layers						,lowercase					)
         copy_layers(teacher.prophetnet.decoder.layers						,student.prophetnet.decoder.layers						,lowercase					)
      else:
         copy_layers(teacher.model.encoder.layers						,student.model.encoder.layers						,lowercase					)
         copy_layers(teacher.model.decoder.layers						,student.model.decoder.layers						,lowercase					)
   except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
      copy_layers(teacher.encoder.block						,student.encoder.block						,lowercase					)
      copy_layers(teacher.decoder.block						,student.decoder.block						,lowercase					)
   logger.info(
       f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""					)
   snake_case				:    List[str]           =	{
       """teacher_type""": teacher.config.model_type,
       """copied_encoder_layers""": e_layers_to_copy,
       """copied_decoder_layers""": d_layers_to_copy,
   }
   student.save_pretrained(lowercase					)
   # 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 						SCREAMING_SNAKE_CASE__ (   lowercase = 1000					)  ->       int:
   snake_case				:    Optional[int]           =	3
   snake_case				:    List[Any]           =	0
   while a < n:
      if a % 3 == 0 or a % 5 == 0:
         result += a
      elif a % 15 == 0:
         result -= a
      a += 1
   return result


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





