




"""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 						_snake_case					( _snake_case							: nn.ModuleList    ,  _snake_case							: nn.ModuleList    ,  _snake_case							: List[int]							)   -> None:






      '''simple docstring'''


      _A        =       nn.ModuleList([src_layers[i] for i in layers_to_copy]							)
      assert len(_snake_case							) == len(_snake_case							), F'''{len(_snake_case							)} != {len(_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 						_snake_case					( _snake_case							: List[Any]    ,  _snake_case							: Union[str, Any]							)   -> List[Any]:






      '''simple docstring'''


      try:
            _A        =       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(_snake_case							)							)






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: List[Any]							)   -> 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(_snake_case							)							)
      elif n_student == 1:
            return [n_teacher - 1]
      else:
            return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def 						_snake_case					( _snake_case							: Union[str, PreTrainedModel]    ,  _snake_case							: Union[str, Path] = "student"    ,  _snake_case							: Union[int, None] = None    ,  _snake_case							: Union[int, None] = None    ,  _snake_case							: Any=False    ,  _snake_case							: Optional[int]=None    ,  _snake_case							: Dict=None    ,  **_snake_case							: str    ,  )   -> Tuple[PreTrainedModel, List[int], List[int]]:






      '''simple docstring'''


      _A        =       '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(_snake_case    ,  _snake_case							):
            AutoTokenizer.from_pretrained(_snake_case							).save_pretrained(_snake_case							)  # purely for convenience
            _A        =       AutoModelForSeqaSeqLM.from_pretrained(_snake_case							).eval()
      else:
            assert isinstance(_snake_case    ,  _snake_case							), F'''teacher must be a model or string got type {type(_snake_case							)}'''
      _A        =       teacher.config.to_diff_dict()

      try:
            _A       ,   _A        =       teacher.config.encoder_layers, teacher.config.decoder_layers
            if e is None:
                  _A        =       teacher_e
            if d is None:
                  _A        =       teacher_d
            init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}							)
      except AttributeError:  # T5
            if hasattr(teacher.config    ,  'num_encoder_layers'							):
                  _A       ,   _A        =       teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
            else:
                  _A       ,   _A        =       teacher.config.num_layers, teacher.config.num_decoder_layers
            if e is None:
                  _A        =       teacher_e
            if d is None:
                  _A        =       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(_snake_case							)

      # Copy weights
      _A        =       teacher.config_class(**_snake_case							)
      _A        =       AutoModelForSeqaSeqLM.from_config(_snake_case							)
      # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
      _A        =       student.load_state_dict(teacher.state_dict()    ,  strict=_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
            _A       ,   _A        =       list(range(_snake_case							)							), list(range(_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(_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:
            _A        =       pick_layers_to_copy(_snake_case    ,  _snake_case							)
      if d_layers_to_copy is None:
            _A        =       pick_layers_to_copy(_snake_case    ,  _snake_case							)

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

from __future__ import annotations

import time

import numpy as np

a         =      [8, 5, 9, 7]
a         =      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a         =      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	list[int]   ,							_UpperCAmelCase	:	list[list[int]]   ,							_UpperCAmelCase	:	list[list[int]]   ,							):
       _A        =       claim_vector
       _A        =       allocated_resources_table
       _A        =       maximum_claim_table

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table		)
           for i in range(len(self.__allocated_resources_table[0]		)		)
       ]

 def       lowerCAmelCase_							(			self	:	Tuple		):
       return np.array(self.__claim_vector		) - np.array(
           self.__processes_resource_summation()		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return [
           list(np.array(self.__maximum_claim_table[i]		) - np.array(_UpperCAmelCase		)		)
           for i, allocated_resource in enumerate(self.__allocated_resources_table		)
       ]

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return {self.__need().index(_UpperCAmelCase		): i for i in self.__need()}

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	int		):
       _A        =       self.__need()
       _A        =       self.__allocated_resources_table
       _A        =       self.__available_resources()
       _A        =       self.__need_index_manager()
       for kw, val in kwargs.items():
             if kw and val is True:
                   self.__pretty_data()
       print('_' * 50 + '\n'		)
       while need_list:
             _A        =       False
             for each_need in need_list:
                   _A        =       True
                   for index, need in enumerate(_UpperCAmelCase		):
                         if need > available_resources[index]:
                               _A        =       False
                               break
                   if execution:
                         _A        =       True
                         # get the original index of the process from ind_ctrl db
                         for original_need_index, need_clone in need_index_manager.items():
                               if each_need == need_clone:
                                     _A        =       original_need_index
                         print(F'''Process {process_number + 1} is executing.'''		)
                         # remove the process run from stack
                         need_list.remove(_UpperCAmelCase		)
                         # update available/freed resources stack
                         _A        =       np.array(_UpperCAmelCase		) + np.array(
                             alloc_resources_table[process_number]		)
                         print(
                             'Updated available resource stack for processes: '
                             + ' '.join([str(_UpperCAmelCase		) for x in available_resources]		)		)
                         break
             if safe:
                   print('The process is in a safe state.\n'		)
             else:
                   print('System in unsafe state. Aborting...\n'		)
                   break



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       print(' ' * 9 + 'Allocated Resource Table'		)
       for item in self.__allocated_resources_table:
             print(
                 F'''P{self.__allocated_resources_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(' ' * 9 + 'System Resource Table'		)
       for item in self.__maximum_claim_table:
             print(
                 F'''P{self.__maximum_claim_table.index(_UpperCAmelCase		) + 1}'''
                 + ' '.join(F'''{it:>8}''' for it in item		)
                 + '\n'		)
       print(
           'Current Usage by Active Processes: '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__claim_vector		)		)
       print(
           'Initial Available Resources:       '
           + ' '.join(str(_UpperCAmelCase		) for x in self.__available_resources()		)		)
       time.sleep(1		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

