


import os
import unicodedata
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 SPIECE_UNDERLINE, logging


A      = logging.get_logger(__name__)

A      = {"vocab_file": "spiece.model"}

A      = {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}


class     lowercase__  (					_UpperCAmelCase		):






  def __init__(					self      :					Tuple		,						_lowercase      :					Dict		,						_lowercase      :					List[Any]=False		,						_lowercase      :					int=True		,						_lowercase      :					List[Any]=False		,						_lowercase      :					str="<s>"		,						_lowercase      :					List[str]="</s>"		,						_lowercase      :					str="<unk>"		,						_lowercase      :					Optional[Any]="<sep>"		,						_lowercase      :					Dict="<pad>"		,						_lowercase      :					int="<cls>"		,						_lowercase      :					Optional[int]="<mask>"		,						_lowercase      :					Tuple=["<eop>", "<eod>"]		,						_lowercase      :					Tuple = None		,						**_lowercase      :					int		,						):




         """simple docstring"""







         UpperCAmelCase__										=     AddedToken(lowercase__		,						lstrip=lowercase__		,						rstrip=lowercase__ ) if isinstance(lowercase__		,						lowercase__ ) else mask_token

         UpperCAmelCase__										=     {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             do_lower_case=lowercase__		,						remove_space=lowercase__		,						keep_accents=lowercase__		,						bos_token=lowercase__		,						eos_token=lowercase__		,						unk_token=lowercase__		,						sep_token=lowercase__		,						pad_token=lowercase__		,						cls_token=lowercase__		,						mask_token=lowercase__		,						additional_special_tokens=lowercase__		,						sp_model_kwargs=self.sp_model_kwargs		,						**lowercase__		,						)

         UpperCAmelCase__										=     3

         UpperCAmelCase__										=     do_lower_case
         UpperCAmelCase__										=     remove_space
         UpperCAmelCase__										=     keep_accents
         UpperCAmelCase__										=     vocab_file

         UpperCAmelCase__										=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(lowercase__ )

         try:
                import jieba
         except ModuleNotFoundError as error:
                raise error.__class__(
                    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                    "See https://pypi.org/project/jieba/ for installation." )
         UpperCAmelCase__										=     jieba
         UpperCAmelCase__										=     str.maketrans(" \n"		,						"\u2582\u2583" )






  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         return len(self.sp_model )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







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






  def __getstate__(					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.__dict__.copy()
         UpperCAmelCase__										=     None
         return state






  def __setstate__(					self      :					str		,						_lowercase      :					List[Any] ):




         """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.Load(self.vocab_file )






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         if self.remove_space:
                UpperCAmelCase__										=     " ".join(inputs.strip().split() )
         else:
                UpperCAmelCase__										=     inputs
         UpperCAmelCase__										=     outputs.replace("``"		,						"\"" ).replace("''"		,						"\"" )

         if not self.keep_accents:
                UpperCAmelCase__										=     unicodedata.normalize("NFKD"		,						lowercase__ )
                UpperCAmelCase__										=     "".join([c for c in outputs if not unicodedata.combining(lowercase__ )] )
         if self.do_lower_case:
                UpperCAmelCase__										=     outputs.lower()

         return outputs






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.preprocess_text(lowercase__ )
         UpperCAmelCase__										=     self.sp_model.encode(lowercase__		,						out_type=lowercase__ )
         UpperCAmelCase__										=     []
         for piece in pieces:
                if len(lowercase__ ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
                       UpperCAmelCase__										=     self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase__		,						"" ) )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                              if len(cur_pieces[0] ) == 1:
                                     UpperCAmelCase__										=     cur_pieces[1:]
                              else:
                                     UpperCAmelCase__										=     cur_pieces[0][1:]
                       cur_pieces.append(piece[-1] )
                       new_pieces.extend(lowercase__ )
                else:
                       new_pieces.append(lowercase__ )

         return new_pieces






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					int ):




         """simple docstring"""







         return self.sp_model.PieceToId(lowercase__ )






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         return self.sp_model.IdToPiece(lowercase__ )






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     "".join(lowercase__ ).replace(lowercase__		,						" " ).strip()
         return out_string






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					Tuple		,						_lowercase      :					int = None ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					Any		,						_lowercase      :					Union[str, Any] = None		,						_lowercase      :					Optional[Any] = False ):




         """simple docstring"""







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

         if token_ids_a is not None:
                return ([0] * len(lowercase__ )) + [1] + ([0] * len(lowercase__ )) + [1, 1]
         return ([0] * len(lowercase__ )) + [1, 1]






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					List[str]		,						_lowercase      :					List[str] = None ):




         """simple docstring"""







         UpperCAmelCase__										=     [self.sep_token_id]
         UpperCAmelCase__										=     [2]

         if token_ids_a is None:
                return len(token_ids_a + sep ) * [0] + cls_segment_id
         return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					int		,						_lowercase      :					str = None ):




         """simple docstring"""







         if not os.path.isdir(lowercase__ ):
                logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
                return
         UpperCAmelCase__										=     os.path.join(
             lowercase__		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

         return (out_vocab_file,)






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						*_lowercase      :					Dict		,						**_lowercase      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     super()._decode(*lowercase__		,						**lowercase__ )
         UpperCAmelCase__										=     text.replace(" "		,						"" ).replace("\u2582"		,						" " ).replace("\u2583"		,						"\n" )
         return text









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 __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     nn.ModuleList([src_layers[i] for i in layers_to_copy]			)
       assert len(__A			) == len(__A			), F"""{len(__A			)} != {len(__A			)}"""
       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 __UpperCAmelCase       (		__A ,						__A			)						->					int:



       '''simple docstring'''





       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(__A			)			)





def __UpperCAmelCase       (		__A ,						__A			)						->					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(__A			)			)
       elif n_student == 1:
              return [n_teacher - 1]
       else:
              return LAYERS_TO_SUPERVISE[n_teacher][n_student]





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



       '''simple docstring'''





       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(__A ,						__A			):
              AutoTokenizer.from_pretrained(__A			).save_pretrained(__A			)  # purely for convenience
              UpperCAmelCase__										=     AutoModelForSeqaSeqLM.from_pretrained(__A			).eval()
       else:
              assert isinstance(__A ,						__A			), F"""teacher must be a model or string got type {type(__A			)}"""
       UpperCAmelCase__										=     teacher.config.to_diff_dict()

       try:
              UpperCAmelCase__						,			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__						,			UpperCAmelCase__										=     teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
              else:
                     UpperCAmelCase__						,			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(__A			)

       # Copy weights
       UpperCAmelCase__										=     teacher.config_class(**__A			)
       UpperCAmelCase__										=     AutoModelForSeqaSeqLM.from_config(__A			)
       # 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=__A			)
       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__										=     list(range(__A			)			), list(range(__A			)			)
              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(__A			)
              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(__A ,						__A			)
       if d_layers_to_copy is None:
              UpperCAmelCase__										=     pick_layers_to_copy(__A ,						__A			)

       try:
              if hasattr(
                  __A ,						"prophetnet"			):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
                     copy_layers(teacher.prophetnet.encoder.layers ,						student.prophetnet.encoder.layers ,						__A			)
                     copy_layers(teacher.prophetnet.decoder.layers ,						student.prophetnet.decoder.layers ,						__A			)
              else:
                     copy_layers(teacher.model.encoder.layers ,						student.model.encoder.layers ,						__A			)
                     copy_layers(teacher.model.decoder.layers ,						student.model.decoder.layers ,						__A			)
       except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
              copy_layers(teacher.encoder.block ,						student.encoder.block ,						__A			)
              copy_layers(teacher.decoder.block ,						student.decoder.block ,						__A			)
       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(__A			)
       # 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)





