



'''simple docstring'''




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_       :  Optional[int]					=  logging.get_logger(__name__)






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase   ,					_lowercase			):

       """simple docstring"""

       a__								= 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()			)


UpperCamelCase_       :  Tuple					=  {
    # 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_       :  Any					=  {
    # 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 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

       try:
              a__								= 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 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """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(__lowercase			)			)
       elif n_student == 1:
              return [n_teacher - 1]
       else:
              return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase = "student"   ,					_lowercase = None   ,					_lowercase = None   ,					_lowercase=False   ,					_lowercase=None   ,					_lowercase=None   ,					**_lowercase   ,					):

       """simple docstring"""

       a__								= "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
              a__								= AutoModelForSeqaSeqLM.from_pretrained(__lowercase			).eval()
       else:
              assert isinstance(__lowercase   ,					__lowercase			), F'teacher must be a model or string got type {type(__lowercase			)}'
       a__								= teacher.config.to_diff_dict()

       try:
              a__						,					a__								= teacher.config.encoder_layers, teacher.config.decoder_layers
              if e is None:
                     a__								= teacher_e
              if d is None:
                     a__								= teacher_d
              init_kwargs.update({"encoder_layers": e, "decoder_layers": d}			)
       except AttributeError:  # T5
              if hasattr(teacher.config   ,					"num_encoder_layers"			):
                     a__						,					a__								= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
              else:
                     a__						,					a__								= teacher.config.num_layers, teacher.config.num_decoder_layers
              if e is None:
                     a__								= teacher_e
              if d is None:
                     a__								= 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
       a__								= teacher.config_class(**__lowercase			)
       a__								= AutoModelForSeqaSeqLM.from_config(__lowercase			)
       # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
       a__								= 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
              a__						,					a__								= 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:
              a__								= pick_layers_to_copy(__lowercase   ,					__lowercase			)
       if d_layers_to_copy is None:
              a__								= 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}'			)
       a__								= {
           "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)








from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

snake_case__   :						Dict													=   TypeVar("""T""")


class     _a							(	Generic[T]     ):






  """simple docstring"""





  A_						       = 42  # Cache store of keys
  A_						       = 42  # References of the keys in cache
  A_						       = 10  # Maximum capacity of cache




  def __init__(  self		,		_UpperCAmelCase			)      ->			None:
     UpperCamelCase_						=  deque()
     UpperCamelCase_						=  set()
     if not n:
        UpperCamelCase_						=  sys.maxsize
     elif n < 0:
        raise ValueError('n should be an integer greater than 0.'			)
     else:
        UpperCamelCase_						=  n




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     if x not in self.key_reference:
        if len(self.dq_store			) == LRUCache._MAX_CAPACITY:
           UpperCamelCase_						=  self.dq_store.pop()
           self.key_reference.remove(_UpperCAmelCase			)
     else:
        self.dq_store.remove(_UpperCAmelCase			)

     self.dq_store.appendleft(_UpperCAmelCase			)
     self.key_reference.add(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			None:
     for k in self.dq_store:
        print(_UpperCAmelCase			)




  def __repr__(  self			)      ->			str:
     return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store			)}"""


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 snake_case__   :						LRUCache[str | int]													=   LRUCache(4)
 lru_cache.refer("""A""")
 lru_cache.refer(2)
 lru_cache.refer(3)
 lru_cache.refer("""A""")
 lru_cache.refer(4)
 lru_cache.refer(5)
 lru_cache.display()

 print(lru_cache)
 assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





