






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


lowerCamelCase	:							str				=logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	None:
     UpperCamelCase__			:   Dict       				=  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()			)


lowerCamelCase	:							Union[str, 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))},
}
lowerCamelCase	:							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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Union[str, Any]:
     try:
          UpperCamelCase__			:   List[str]       				=  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 		SCREAMING_SNAKE_CASE							(					__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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = "student"					,    __lowerCAmelCase = None					,    __lowerCAmelCase = None					,    __lowerCAmelCase=False					,    __lowerCAmelCase=None					,    __lowerCAmelCase=None					,    **__lowerCAmelCase					,    ) ->	Tuple[PreTrainedModel, List[int], List[int]]:
     UpperCamelCase__			:   str       				=  "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
          UpperCamelCase__			:   Any       				=  AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase			).eval()
     else:
          assert isinstance(__lowerCAmelCase					,    __lowerCAmelCase			), f'teacher must be a model or string got type {type(__lowerCAmelCase			)}'
     UpperCamelCase__			:   List[Any]       				=  teacher.config.to_diff_dict()

     try:
          UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  teacher.config.encoder_layers, teacher.config.decoder_layers
          if e is None:
               UpperCamelCase__			:   Optional[Any]       				=  teacher_e
          if d is None:
               UpperCamelCase__			:   List[Any]       				=  teacher_d
          init_kwargs.update({"encoder_layers": e, "decoder_layers": d}			)
     except AttributeError:  # T5
          if hasattr(teacher.config					,    "num_encoder_layers"			):
               UpperCamelCase__				,     UpperCamelCase__			:   Any       				=  teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
          else:
               UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  teacher.config.num_layers, teacher.config.num_decoder_layers
          if e is None:
               UpperCamelCase__			:   Union[str, Any]       				=  teacher_e
          if d is None:
               UpperCamelCase__			:   Any       				=  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
     UpperCamelCase__			:   Union[str, Any]       				=  teacher.config_class(**__lowerCAmelCase			)
     UpperCamelCase__			:   Tuple       				=  AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase			)
     # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
     UpperCamelCase__			:   Tuple       				=  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
          UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  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:
          UpperCamelCase__			:   List[int]       				=  pick_layers_to_copy(__lowerCAmelCase					,    __lowerCAmelCase			)
     if d_layers_to_copy is None:
          UpperCamelCase__			:   List[int]       				=  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}'			)
     UpperCamelCase__			:   str       				=  {
         "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)







from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowerCamelCase	:							Tuple				=_symbol_database.Default()


lowerCamelCase	:							List[str]				=_descriptor_pool.Default().AddSerializedFile(
    b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

lowerCamelCase	:							str				=globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
 lowerCamelCase	:							Optional[int]				=None
 lowerCamelCase	:							Tuple				=b'''H\003'''
 # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
 # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
 # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
 # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
 # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
 lowerCamelCase	:							List[str]				=45
 lowerCamelCase	:							List[Any]				=1581
 lowerCamelCase	:							Optional[int]				=1517
 lowerCamelCase	:							Tuple				=1570
 lowerCamelCase	:							Dict				=1584
 lowerCamelCase	:							Optional[Any]				=1793
 lowerCamelCase	:							Dict				=1795
 lowerCamelCase	:							Any				=1916
 lowerCamelCase	:							Dict				=1864
 lowerCamelCase	:							Dict				=1905
 lowerCamelCase	:							Dict				=1919
 lowerCamelCase	:							Union[str, Any]				=2429
 lowerCamelCase	:							List[Any]				=2208
 lowerCamelCase	:							List[Any]				=2418
 lowerCamelCase	:							List[str]				=2323
 lowerCamelCase	:							Dict				=2407
# @@protoc_insertion_point(module_scope)