


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

import sentencepiece as spm

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


A      = logging.get_logger(__name__)

A      = "▁"

A      = {"vocab_file": "sentencepiece.bpe.model"}

A      = {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

A      = {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
A      = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  VOCAB_FILES_NAMES
  A__=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__=  PRETRAINED_VOCAB_FILES_MAP
  A__=  ['input_ids', 'attention_mask']

  A__=  []
  A__=  []






  def __init__(					self      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					List[str]="<s>"		,						_lowercase      :					Tuple="</s>"		,						_lowercase      :					Optional[Any]="</s>"		,						_lowercase      :					List[Any]="<s>"		,						_lowercase      :					Any="<unk>"		,						_lowercase      :					List[Any]="<pad>"		,						_lowercase      :					str="<mask>"		,						_lowercase      :					Union[str, Any]=None		,						_lowercase      :					Union[str, Any]=None		,						_lowercase      :					Dict=None		,						_lowercase      :					Optional[Any] = None		,						_lowercase      :					Union[str, Any]=None		,						**_lowercase      :					Optional[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__(
             bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						tokenizer_file=_lowercase		,						src_lang=_lowercase		,						tgt_lang=_lowercase		,						additional_special_tokens=_lowercase		,						sp_model_kwargs=self.sp_model_kwargs		,						**_lowercase		,						)

         UpperCAmelCase__										=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(_lowercase ) )
         UpperCAmelCase__										=     vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         UpperCAmelCase__										=     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         UpperCAmelCase__										=     1

         UpperCAmelCase__										=     len(self.sp_model )
         UpperCAmelCase__										=     {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowercase )
         }
         UpperCAmelCase__										=     {v: k for k, v in self.lang_code_to_id.items()}
         UpperCAmelCase__										=     len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
         UpperCAmelCase__										=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         UpperCAmelCase__										=     list(self.lang_code_to_id.keys() )

         if additional_special_tokens is not None:
                # Only add those special tokens if they are not already there.
                self._additional_special_tokens.extend(
                    [t for t in additional_special_tokens if t not in self._additional_special_tokens] )

         UpperCAmelCase__										=     src_lang if src_lang is not None else "en_XX"
         UpperCAmelCase__										=     self.lang_code_to_id[self._src_lang]
         UpperCAmelCase__										=     tgt_lang
         self.set_src_lang_special_tokens(self._src_lang )






  def __getstate__(					self      :					List[str] ):




         """simple docstring"""







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






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




         """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 )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         return self._src_lang






  @src_lang.setter
  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )






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

         UpperCAmelCase__										=     [1] * len(self.prefix_tokens )
         UpperCAmelCase__										=     [1] * len(self.suffix_tokens )
         if token_ids_a is None:
                return prefix_ones + ([0] * len(_lowercase )) + suffix_ones
         return prefix_ones + ([0] * len(_lowercase )) + ([0] * len(_lowercase )) + suffix_ones






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Any		,						_lowercase      :					Dict = None ):




         """simple docstring"""







         if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					Dict = 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]






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




         """simple docstring"""







         if src_lang is None or tgt_lang is None:
                raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
         UpperCAmelCase__										=     src_lang
         UpperCAmelCase__										=     self(_lowercase		,						add_special_tokens=_lowercase		,						return_tensors=_lowercase		,						**_lowercase )
         UpperCAmelCase__										=     self.convert_tokens_to_ids(_lowercase )
         UpperCAmelCase__										=     tgt_lang_id
         return inputs






  def 			_UpperCAmelCase  (					self      :					int ):




         """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 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					Any ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					Any ):




         """simple docstring"""







         if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase__										=     self.sp_model.PieceToId(_lowercase )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






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




         """simple docstring"""







         if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset )






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




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					Optional[Any] = 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      :					Union[str, Any]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[int] = "en_XX"		,						_lowercase      :					Optional[int] = None		,						_lowercase      :					Any = "ro_RO"		,						**_lowercase      :					List[str]		,						):




         """simple docstring"""







         UpperCAmelCase__										=     src_lang
         UpperCAmelCase__										=     tgt_lang
         return super().prepare_seqaseq_batch(_lowercase		,						_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         return self.set_src_lang_special_tokens(self.src_lang )






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         return self.set_tgt_lang_special_tokens(self.tgt_lang )






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




         """simple docstring"""







         UpperCAmelCase__										=     self.lang_code_to_id[src_lang]
         UpperCAmelCase__										=     []
         UpperCAmelCase__										=     [self.eos_token_id, self.cur_lang_code]






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




         """simple docstring"""







         UpperCAmelCase__										=     self.lang_code_to_id[lang]
         UpperCAmelCase__										=     []
         UpperCAmelCase__										=     [self.eos_token_id, self.cur_lang_code]









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)





