





'''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


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							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()   )


a   	=    {
    # 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))},
}
a   	=    {
    # 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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







   try:
      __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       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(__UpperCAmelCase   )   )
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase = "student"				,						__UpperCAmelCase = None				,						__UpperCAmelCase = None				,						__UpperCAmelCase=False				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						**__UpperCAmelCase				,						)    ->       Tuple[PreTrainedModel, List[int], List[int]]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""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
      __SCREAMING_SNAKE_CASE   				=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase   ).eval()
   else:
      assert isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ), f"""teacher must be a model or string got type {type(__UpperCAmelCase   )}"""
   __SCREAMING_SNAKE_CASE   				=							teacher.config.to_diff_dict()

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








A									=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
