
'''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


snake_case_ :  str     =					logging.get_logger(__name__)



def  __snake_case     (    _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  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())


snake_case_ :  Dict     =					{
    # 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))},
}
snake_case_ :  str     =					{
    # 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  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Union[str, Any]):
   try:
      UpperCamelCase       =  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  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       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(_lowercase))
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Any = "student",      _UpperCAmelCase					:       List[str] = None,      _UpperCAmelCase					:       Dict = None,      _UpperCAmelCase					:       int=False,      _UpperCAmelCase					:       Any=None,      _UpperCAmelCase					:       List[Any]=None,      **_UpperCAmelCase					:       Tuple,      ):
   UpperCamelCase       =  '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
      UpperCamelCase       =  AutoModelForSeqaSeqLM.from_pretrained(_lowercase).eval()
   else:
      assert isinstance(_lowercase,      _lowercase), f'teacher must be a model or string got type {type(_lowercase)}'
   UpperCamelCase       =  teacher.config.to_diff_dict()

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




'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

snake_case_ :  int     =					'▁'

snake_case_ :  str     =					{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

snake_case_ :  Optional[Any]     =					{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

snake_case_ :  Dict     =					{'vinai/bartpho-syllable': 1_024}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _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 ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="<mask>" ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  AddedToken(lowerCamelCase__ ,   lstrip=lowerCamelCase__ ,   rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) else mask_token

      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=lowerCamelCase__ ,   eos_token=lowerCamelCase__ ,   unk_token=lowerCamelCase__ ,   sep_token=lowerCamelCase__ ,   cls_token=lowerCamelCase__ ,   pad_token=lowerCamelCase__ ,   mask_token=lowerCamelCase__ ,   sp_model_kwargs=self.sp_model_kwargs ,   **lowerCamelCase__ ,   )

      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  monolingual_vocab_file
      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(lowerCamelCase__   )   )

      # Load the reduced vocab

      # Keep order of special tokens for backward compatibility
      UpperCamelCase       =  {}
      UpperCamelCase       =  0
      for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
         if str(lowerCamelCase__   ) not in self.fairseq_tokens_to_ids:
            UpperCamelCase       =  cnt
            cnt += 1
      with open(lowerCamelCase__ ,   '''r''' ,   encoding='''utf-8'''   ) as f:
         for line in f.readlines():
            UpperCamelCase       =  line.strip().split()[0]
            UpperCamelCase       =  len(self.fairseq_tokens_to_ids   )
      if str(lowerCamelCase__   ) not in self.fairseq_tokens_to_ids:
         UpperCamelCase       =  len(self.fairseq_tokens_to_ids   )

      UpperCamelCase       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







   def __getstate__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.__dict__.copy()
      UpperCamelCase       =  None
      UpperCamelCase       =  self.sp_model.serialized_model_proto()
      return state







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''   ):
         UpperCamelCase       =  {}

      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase       =  [self.cls_token_id]
      UpperCamelCase       =  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowerCamelCase__ ,   token_ids_a=lowerCamelCase__ ,   already_has_special_tokens=lowerCamelCase__   )

      if token_ids_a is None:
         return [1] + ([0] * len(lowerCamelCase__   )) + [1]
      return [1] + ([0] * len(lowerCamelCase__   )) + [1, 1] + ([0] * len(lowerCamelCase__   )) + [1]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  [self.sep_token_id]
      UpperCamelCase       =  [self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep   ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.fairseq_ids_to_tokens   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      else:
         return self.unk_token_id







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.fairseq_ids_to_tokens[index]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  ''''''.join(lowerCamelCase__   ).replace(lowerCamelCase__ ,   ''' '''   ).strip()
      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] ,   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase__   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(lowerCamelCase__ ,   '''wb'''   ) as fi:
            UpperCamelCase       =  self.sp_model.serialized_model_proto()
            fi.write(lowerCamelCase__   )

      if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
          lowerCamelCase__   ) and os.path.isfile(self.monolingual_vocab_file   ):
         copyfile(self.monolingual_vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.monolingual_vocab_file   ):
         with open(lowerCamelCase__ ,   '''w''' ,   encoding='''utf-8'''   ) as fp:
            for token in self.fairseq_tokens_to_ids:
               if token not in self.all_special_tokens:
                  fp.write(f'{str(lowerCamelCase__   )} \n'   )

      return out_vocab_file, out_monolingual_vocab_file





