





"""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


__A											=		logging.get_logger(__name__)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->None:





  """simple docstring"""


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





  """simple docstring"""


  try:
    lowerCAmelCase__			:Optional[Any]											=							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(_SCREAMING_SNAKE_CASE			)			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->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(_SCREAMING_SNAKE_CASE			)			)
  elif n_student == 1:
    return [n_teacher - 1]
  else:
    return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE = "student"     ,   _SCREAMING_SNAKE_CASE = None     ,   _SCREAMING_SNAKE_CASE = None     ,   _SCREAMING_SNAKE_CASE=False     ,   _SCREAMING_SNAKE_CASE=None     ,   _SCREAMING_SNAKE_CASE=None     ,   **_SCREAMING_SNAKE_CASE     ,   )					->Tuple[PreTrainedModel, List[int], List[int]]:





  """simple docstring"""


  lowerCAmelCase__			:Dict											=							'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(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			):
    AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			).save_pretrained(_SCREAMING_SNAKE_CASE			)  # purely for convenience
    lowerCAmelCase__			:List[Any]											=							AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE			).eval()
  else:
    assert isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			), F"teacher must be a model or string got type {type(_SCREAMING_SNAKE_CASE			)}"
  lowerCAmelCase__			:List[Any]											=							teacher.config.to_diff_dict()

  try:
    lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							teacher.config.encoder_layers, teacher.config.decoder_layers
    if e is None:
      lowerCAmelCase__			:Tuple											=							teacher_e
    if d is None:
      lowerCAmelCase__			:Optional[Any]											=							teacher_d
    init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}			)
  except AttributeError:  # T5
    if hasattr(teacher.config     ,   'num_encoder_layers'			):
      lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
    else:
      lowerCAmelCase__							,				lowerCAmelCase__			:List[str]											=							teacher.config.num_layers, teacher.config.num_decoder_layers
    if e is None:
      lowerCAmelCase__			:Any											=							teacher_e
    if d is None:
      lowerCAmelCase__			:List[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(_SCREAMING_SNAKE_CASE			)

  # Copy weights
  lowerCAmelCase__			:Optional[Any]											=							teacher.config_class(**_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_config(_SCREAMING_SNAKE_CASE			)
  # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
  lowerCAmelCase__			:Tuple											=							student.load_state_dict(teacher.state_dict()     ,   strict=_SCREAMING_SNAKE_CASE			)
  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
    lowerCAmelCase__							,				lowerCAmelCase__			:Optional[int]											=							list(range(_SCREAMING_SNAKE_CASE			)			), list(range(_SCREAMING_SNAKE_CASE			)			)
    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(_SCREAMING_SNAKE_CASE			)
    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:
    lowerCAmelCase__			:List[int]											=							pick_layers_to_copy(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  if d_layers_to_copy is None:
    lowerCAmelCase__			:List[int]											=							pick_layers_to_copy(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  try:
    if hasattr(
        _SCREAMING_SNAKE_CASE     ,   'prophetnet'			):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
      copy_layers(teacher.prophetnet.encoder.layers     ,   student.prophetnet.encoder.layers     ,   _SCREAMING_SNAKE_CASE			)
      copy_layers(teacher.prophetnet.decoder.layers     ,   student.prophetnet.decoder.layers     ,   _SCREAMING_SNAKE_CASE			)
    else:
      copy_layers(teacher.model.encoder.layers     ,   student.model.encoder.layers     ,   _SCREAMING_SNAKE_CASE			)
      copy_layers(teacher.model.decoder.layers     ,   student.model.decoder.layers     ,   _SCREAMING_SNAKE_CASE			)
  except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
    copy_layers(teacher.encoder.block     ,   student.encoder.block     ,   _SCREAMING_SNAKE_CASE			)
    copy_layers(teacher.decoder.block     ,   student.decoder.block     ,   _SCREAMING_SNAKE_CASE			)
  logger.info(
      F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"			)
  lowerCAmelCase__			:Dict											=							{
      'teacher_type': teacher.config.model_type,
      'copied_encoder_layers': e_layers_to_copy,
      'copied_decoder_layers': d_layers_to_copy,
  }
  student.save_pretrained(_SCREAMING_SNAKE_CASE			)
  # 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 tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
  import torch






@require_torch
@require_optimum
@slow
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

         self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							model.reverse_bettertransformer()

         self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

           self.assertFalse(
               any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

         with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__UpperCAmelCase      ):
             model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
