






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






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   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__   :						Tuple													=   {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
snake_case__   :						Any													=   {
    # 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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}






def      _snake_case     (__lowercase     ,   __lowercase):
   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     (__lowercase     ,   __lowercase):
   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     (__lowercase     ,   __lowercase = "student"     ,   __lowercase = None     ,   __lowercase = None     ,   __lowercase=False     ,   __lowercase=None     ,   __lowercase=None     ,   **__lowercase     ,   ):
   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_		,   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(__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_		,   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)













from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @slow
  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'		,		'prajjwal1/bert-tiny'			)
     UpperCamelCase_						=  BertTokenizer.from_pretrained('bert-base-uncased'			)

     UpperCamelCase_						=  bertabert.config.encoder.vocab_size
     UpperCamelCase_						=  tokenizer.sep_token_id
     UpperCamelCase_						=  tokenizer.cls_token_id
     UpperCamelCase_						=  128

     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='train[:1%]'			)
     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='validation[:1%]'			)

     UpperCamelCase_						=  train_dataset.select(range(32			)			)
     UpperCamelCase_						=  val_dataset.select(range(16			)			)

     UpperCamelCase_						=  4

     def _map_to_encoder_decoder_inputs(_UpperCAmelCase			):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        UpperCamelCase_						=  tokenizer(batch['article']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=512			)
        UpperCamelCase_						=  tokenizer(batch['highlights']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=128			)
        UpperCamelCase_						=  inputs.input_ids
        UpperCamelCase_						=  inputs.attention_mask

        UpperCamelCase_						=  outputs.input_ids
        UpperCamelCase_						=  outputs.input_ids.copy()
        UpperCamelCase_						=  [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
        ]
        UpperCamelCase_						=  outputs.attention_mask

        assert all(len(_UpperCAmelCase			) == 512 for x in inputs.input_ids			)
        assert all(len(_UpperCAmelCase			) == 128 for x in outputs.input_ids			)

        return batch

     def _compute_metrics(_UpperCAmelCase			):
        UpperCamelCase_						=  pred.label_ids
        UpperCamelCase_						=  pred.predictions

        # all unnecessary tokens are removed
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)

        UpperCamelCase_						=  sum([int(pred_str[i] == label_str[i]			) for i in range(len(_UpperCAmelCase			)			)]			) / len(_UpperCAmelCase			)

        return {"accuracy": accuracy}

     # map train dataset
     UpperCamelCase_						=  train_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     train_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     # same for validation dataset
     UpperCamelCase_						=  val_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     val_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     UpperCamelCase_						=  self.get_auto_remove_tmp_dir()

     UpperCamelCase_						=  SeqaSeqTrainingArguments(
         output_dir=_UpperCAmelCase		,		per_device_train_batch_size=_UpperCAmelCase		,		per_device_eval_batch_size=_UpperCAmelCase		,		predict_with_generate=_UpperCAmelCase		,		evaluation_strategy='steps'		,		do_train=_UpperCAmelCase		,		do_eval=_UpperCAmelCase		,		warmup_steps=0		,		eval_steps=2		,		logging_steps=2		,		)

     # instantiate trainer
     UpperCamelCase_						=  SeqaSeqTrainer(
         model=_UpperCAmelCase		,		args=_UpperCAmelCase		,		compute_metrics=_compute_metrics		,		train_dataset=_UpperCAmelCase		,		eval_dataset=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		)

     # start training
     trainer.train()





