


"""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


_snake_case          =							logging.get_logger(__name__)



def   snake_case						(				_a:     nn.ModuleList		,   _a:     nn.ModuleList		,   _a:     List[int]							)->		None:
      '''simple docstring'''


      lowerCamelCase__           =		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()							)


_snake_case          =							{
    # 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))},
}
_snake_case          =							{
    # 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   snake_case						(				_a:     int		,   _a:     List[Any]							)->		List[str]:
      '''simple docstring'''


      try:
            lowerCamelCase__           =		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   snake_case						(				_a:     List[str]		,   _a:     Optional[int]							)->		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   snake_case						(				_a:     Union[str, PreTrainedModel]		,   _a:     Union[str, Path] = "student"		,   _a:     Union[int, None] = None		,   _a:     Union[int, None] = None		,   _a:     Optional[Any]=False		,   _a:     int=None		,   _a:     str=None		,   **_a:     Optional[int]		,   )->		Tuple[PreTrainedModel, List[int], List[int]]:
      '''simple docstring'''


      lowerCamelCase__           =		'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
            lowerCamelCase__           =		AutoModelForSeqaSeqLM.from_pretrained(_a							).eval()
      else:
            assert isinstance(_a		,   _a							), F'teacher must be a model or string got type {type(_a							)}'
      lowerCamelCase__           =		teacher.config.to_diff_dict()

      try:
            lowerCamelCase__    ,  lowerCamelCase__           =		teacher.config.encoder_layers, teacher.config.decoder_layers
            if e is None:
                  lowerCamelCase__           =		teacher_e
            if d is None:
                  lowerCamelCase__           =		teacher_d
            init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}							)
      except AttributeError:  # T5
            if hasattr(teacher.config		,   'num_encoder_layers'							):
                  lowerCamelCase__    ,  lowerCamelCase__           =		teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
            else:
                  lowerCamelCase__    ,  lowerCamelCase__           =		teacher.config.num_layers, teacher.config.num_decoder_layers
            if e is None:
                  lowerCamelCase__           =		teacher_e
            if d is None:
                  lowerCamelCase__           =		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
      lowerCamelCase__           =		teacher.config_class(**_a							)
      lowerCamelCase__           =		AutoModelForSeqaSeqLM.from_config(_a							)
      # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
      lowerCamelCase__           =		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
            lowerCamelCase__    ,  lowerCamelCase__           =		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:
            lowerCamelCase__           =		pick_layers_to_copy(_a		,   _a							)
      if d_layers_to_copy is None:
            lowerCamelCase__           =		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}'							)
      lowerCamelCase__           =		{
          '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)







"""simple docstring"""


def   snake_case						(				_a:     int = 4000000							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0, 1]
      lowerCamelCase__           =		0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]							)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCamelCase__           =		0
      for j in range(len(_a							) - 1							):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


if __name__ == "__main__":
   print(f"""{solution() = }""")



