






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 				__lowerCamelCase     (							__lowerCAmelCase							:					nn.ModuleList					,							__lowerCAmelCase							:					nn.ModuleList					,							__lowerCAmelCase							:					List[int]							)     ->  None:
   __UpperCamelCase	:			Dict					=					nn.ModuleList([src_layers[i] for i in layers_to_copy]							)
   assert len(__lowerCAmelCase							) == len(__lowerCAmelCase							), f'{len(__lowerCAmelCase							)} != {len(__lowerCAmelCase							)}'
   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 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					Any							)     ->  Union[str, Any]:
   try:
      __UpperCamelCase	:			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(__lowerCAmelCase							)							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Dict							)     ->  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(__lowerCAmelCase							)							)
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]


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

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

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

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











import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



