



"""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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							None:
       A         =					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()			)


A    =			{
    # 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))},
}
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]},
    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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Union[str, Any]:
       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(lowerCamelCase__			)			)

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

       # Copy weights
       A         =					teacher.config_class(**lowerCamelCase__			)
       A         =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase__			)
       # 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=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
              A      ,  A         =					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:
              A         =					pick_layers_to_copy(lowerCamelCase__				,      lowerCamelCase__			)
       if d_layers_to_copy is None:
              A         =					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}"""			)
       A         =					{
           '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)








"""simple docstring"""







import re

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							list:
							return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]'				,      str_			)]

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							A         =					split_input(str_			)
							return "".join(
							    [''.join([char.capitalize() for char in sub_str]			) for sub_str in string_split]			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							try:
														A         =					split_input(lowerCamelCase__			)
														if upper:
																					A         =					''.join(
																					    [
																					        separator.join([char.upper() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														else:
																					A         =					''.join(
																					    [
																					        separator.join([char.lower() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														return res_str
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							return to_simple_case(lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							try:
														A         =					to_simple_case(lowerCamelCase__			)
														return res_str[0].lower() + res_str[1:]
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '_'			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '-'			)


if __name__ == "__main__":
							__import__('doctest').testmod()



