





"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


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

__UpperCamelCase     :						str						       =  {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

__UpperCamelCase     :						str						       =  {
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

__UpperCamelCase     :						Optional[Any]						       =  {
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Any			):
 with open(_UpperCAmelCase  ,     'r'  ,     encoding='utf-8'			) as f:
  lowerCAmelCase    				= json.loads(f.read()			)

 lowerCAmelCase    				= collections.OrderedDict()
 lowerCAmelCase    				= collections.OrderedDict()
 lowerCAmelCase    				= collections.OrderedDict()
 with open(_UpperCAmelCase  ,     'r'  ,     encoding='utf-8'			) as f:
  lowerCAmelCase    				= f.readlines()
 lowerCAmelCase    				= [[t.rstrip('\n'			)] if (t == ',' or ',' not in t) else t.rstrip('\n'			).split(','			) for t in token]
 for idx, b in enumerate(_UpperCAmelCase			):
  lowerCAmelCase    				= b
  lowerCAmelCase    				= idx
  for wd in b:
   lowerCAmelCase    				= idx

 return vocab, raw_vocab, ids_to_tokens, emoji







class       a							(   a__  ):
 snake_case__             =       VOCAB_FILES_NAMES
 snake_case__             =       PRETRAINED_VOCAB_FILES_MAP
 snake_case__             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case__             =       ['''input_ids''', '''attention_mask''']



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case="<|endoftext|>"				,       _snake_case="<|endoftext|>"				,       _snake_case="<|startoftext|>"				,       _snake_case="<|endoftext|>"				,       _snake_case=False				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(
      unk_token=_snake_case				,       pad_token=_snake_case				,       bos_token=_snake_case				,       eos_token=_snake_case				,       do_clean_text=_snake_case				,       **_snake_case				,       )
  if not os.path.isfile(_snake_case     ):
   raise ValueError(
       F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
       ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'     )
  if not os.path.isfile(_snake_case     ):
   raise ValueError(
       F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
       ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'     )
  lowerCAmelCase    				= do_clean_text
  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= load_vocab_and_emoji(_snake_case				,       _snake_case     )
  lowerCAmelCase    				= SubWordJapaneseTokenizer(
      vocab=self.vocab				,       ids_to_tokens=self.ids_to_tokens				,       emoji=self.emoji     )



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return len(self.raw_vocab     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return dict(self.raw_vocab				,       **self.added_tokens_encoder     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.subword_tokenizer.tokenize(_snake_case				,       clean=self.do_clean_text     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.vocab.get(_snake_case				,       self.vocab.get(self.unk_token     )     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.subword_tokenizer.convert_id_to_token(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= ''.join(_snake_case     ).strip()
  return out_string



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(_snake_case				,       add_special_tokens=_snake_case     ) + [self.eos_token_id]     )

  if len(_snake_case     ) > self.model_max_length:
   lowerCAmelCase    				= input_ids[-self.model_max_length :]
  return input_ids



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= 0
  if os.path.isdir(_snake_case     ):
   lowerCAmelCase    				= os.path.join(
       _snake_case				,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
   lowerCAmelCase    				= os.path.join(
       _snake_case				,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']     )
  else:
   lowerCAmelCase    				= (
       (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
   )
   lowerCAmelCase    				= (
       (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
   )
  with open(_snake_case				,       'w'				,       encoding='utf-8'     ) as writer:
   for token_index, token in self.ids_to_tokens.items():
    if index != token_index:
     logger.warning(
         F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
         ' Please check that the vocabulary is not corrupted!'     )
     lowerCAmelCase    				= token_index
    writer.write(','.join(_snake_case     ) + '\n'     )
    index += 1
  with open(_snake_case				,       'w'				,       encoding='utf-8'     ) as writer:
   json.dump(self.emoji				,       _snake_case     )
  return vocab_file, emoji_file







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= vocab  # same as swe
  lowerCAmelCase    				= ids_to_tokens  # same as bpe
  lowerCAmelCase    				= emoji
  lowerCAmelCase    				= np.max([len(_snake_case     ) for w in self.vocab.keys()]     )
  lowerCAmelCase    				= re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'     )
  lowerCAmelCase    				= re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'     )
  lowerCAmelCase    				= re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'     )
  lowerCAmelCase    				= re.compile(
      r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'     )
  lowerCAmelCase    				= re.compile(
      r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'     )
  lowerCAmelCase    				= re.compile(
      r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'     )
  lowerCAmelCase    				= '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
  lowerCAmelCase    				= '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
  lowerCAmelCase    				= str.maketrans({k: '<BLOCK>' for k in keisen + blocks}     )



 def __len__(     self     ):







  """simple docstring"""






  return len(self.ids_to_tokens     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.content_repattera.sub('<URL>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<EMAIL>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<TEL>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<DATE>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<DATE>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<PRICE>'				,       _snake_case     )
  lowerCAmelCase    				= content.translate(self.content_transa     )
  while "<BLOCK><BLOCK>" in content:
   lowerCAmelCase    				= content.replace('<BLOCK><BLOCK>'				,       '<BLOCK>'     )
  return content



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=False     ):







  """simple docstring"""






  lowerCAmelCase    				= text.replace(' '				,       '<SP>'     )
  lowerCAmelCase    				= text.replace('　'				,       '<SP>'     )
  lowerCAmelCase    				= text.replace('\r\n'				,       '<BR>'     )
  lowerCAmelCase    				= text.replace('\n'				,       '<BR>'     )
  lowerCAmelCase    				= text.replace('\r'				,       '<BR>'     )
  lowerCAmelCase    				= text.replace('\t'				,       '<TAB>'     )
  lowerCAmelCase    				= text.replace('—'				,       'ー'     )
  lowerCAmelCase    				= text.replace('−'				,       'ー'     )
  for k, v in self.emoji["emoji"].items():
   if k in text:
    lowerCAmelCase    				= text.replace(_snake_case				,       _snake_case     )
  if clean:
   lowerCAmelCase    				= self.clean_text(_snake_case     )

  def check_simbol(_snake_case     ):
   lowerCAmelCase    				= x.encode()
   if len(_snake_case     ) == 1 and len(_snake_case     ) == 2:
    lowerCAmelCase    				= (int(e[0]     ) << 8) + int(e[1]     )
    if (
        (c >= 0xc_2a1 and c <= 0xc_2bf)
        or (c >= 0xc_780 and c <= 0xc_783)
        or (c >= 0xc_ab9 and c <= 0xc_bbf)
        or (c >= 0xc_c80 and c <= 0xc_da2)
    ):
     return True
   return False

  def checkuae(_snake_case     ):
   lowerCAmelCase    				= x.encode()
   if len(_snake_case     ) == 1 and len(_snake_case     ) == 3:
    lowerCAmelCase    				= (int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
    if c >= 0xe28_080 and c <= 0xe2b_07f:
     return True
   return False

  lowerCAmelCase    				= 0
  lowerCAmelCase    				= []
  while pos < len(_snake_case     ):
   lowerCAmelCase    				= min(len(_snake_case     )				,       pos + self.maxlen + 1     ) if text[pos] == '<' else pos + 3
   lowerCAmelCase    				= []  # (token_id, token, pos)
   for e in range(_snake_case				,       _snake_case				,       -1     ):
    lowerCAmelCase    				= text[pos:e]
    if wd in self.vocab:
     if wd[0] == "<" and len(_snake_case     ) > 2:
      lowerCAmelCase    				= [(self.vocab[wd], wd, e)]
      break
     else:
      candidates.append((self.vocab[wd], wd, e)     )
   if len(_snake_case     ) > 0:
    # the smallest token_id is adopted
    lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= sorted(_snake_case				,       key=lambda _snake_case     : x[0]     )[0]
    result.append(_snake_case     )
    lowerCAmelCase    				= e
   else:
    lowerCAmelCase    				= pos + 1
    lowerCAmelCase    				= text[pos:end]
    if check_simbol(_snake_case     ):
     result.append('<KIGOU>'     )
    elif checkuae(_snake_case     ):
     result.append('<U2000U2BFF>'     )
    else:
     for i in wd.encode('utf-8'     ):
      result.append('<|byte%d|>' % i     )
    lowerCAmelCase    				= end
  return result



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case="\n"     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  lowerCAmelCase    				= []
  lowerCAmelCase    				= self.ids_to_tokens[index][0]
  if word[:6] == "<|byte" and word[-2:] == "|>":
   byte_tokens.append(int(word[6:-2]     )     )
  else:
   if len(_snake_case     ) > 0:
    words.append(bytearray(_snake_case     ).decode('utf-8'				,       errors='replace'     )     )
    lowerCAmelCase    				= []
   if word[:7] == "<|emoji" and word[-2:] == "|>":
    words.append(self.emoji['emoji_inv'][word]     )
   elif word == "<SP>":
    words.append(' '     )
   elif word == "<BR>":
    words.append(_snake_case     )
   elif word == "<TAB>":
    words.append('\t'     )
   elif word == "<BLOCK>":
    words.append('▀'     )
   elif word == "<KIGOU>":
    words.append('ǀ'     )
   elif word == "<U2000U2BFF>":
    words.append('‖'     )
   else:
    words.append(_snake_case     )
  if len(_snake_case     ) > 0:
   words.append(bytearray(_snake_case     ).decode('utf-8'				,       errors='replace'     )     )
  lowerCAmelCase    				= ''.join(_snake_case     )
  return text












"""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     :						List[Any]						       =  logging.get_logger(__name__)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    List[int]			):
 lowerCAmelCase    				= 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()			)


__UpperCamelCase     :						Optional[Any]						       =  {
    # 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     :						int						       =  {
    # 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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    List[Any]			):
 try:
  lowerCAmelCase    				= 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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Dict			):
 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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, PreTrainedModel]  ,     _UpperCAmelCase					:    Union[str, Path] = "student"  ,     _UpperCAmelCase					:    Union[int, None] = None  ,     _UpperCAmelCase					:    Union[int, None] = None  ,     _UpperCAmelCase					:    List[str]=False  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     **_UpperCAmelCase					:    str  ,     ):
 lowerCAmelCase    				= '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
  lowerCAmelCase    				= AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase			).eval()
 else:
  assert isinstance(_UpperCAmelCase  ,     _UpperCAmelCase			), F'teacher must be a model or string got type {type(_UpperCAmelCase			)}'
 lowerCAmelCase    				= teacher.config.to_diff_dict()

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





