

'''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


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     List[Any]						 =			nn.ModuleList([src_layers[i] for i in layers_to_copy]     )
 assert len(UpperCAmelCase_     ) == len(UpperCAmelCase_     ), F"""{len(UpperCAmelCase_     )} != {len(UpperCAmelCase_     )}"""
 dest_layers.load_state_dict(layers_to_copy.state_dict()     )


lowercase__         =      {
    # 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))},
}
lowercase__         =      {
    # 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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 try:
  UpperCAmelCase	:     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(UpperCAmelCase_     )     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 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(UpperCAmelCase_     )     )
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_ = "student"  ,	UpperCAmelCase_ = None  ,	UpperCAmelCase_ = None  ,	UpperCAmelCase_=False  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	**UpperCAmelCase_  ,	):
 UpperCAmelCase	:     Any						 =			'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(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  AutoTokenizer.from_pretrained(UpperCAmelCase_     ).save_pretrained(UpperCAmelCase_     )  # purely for convenience
  UpperCAmelCase	:     Optional[int]						 =			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_     ).eval()
 else:
  assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     ), F"""teacher must be a model or string got type {type(UpperCAmelCase_     )}"""
 UpperCAmelCase	:     Optional[Any]						 =			teacher.config.to_diff_dict()

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

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

 try:
  if hasattr(
      UpperCAmelCase_  ,	'prophetnet'     ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
   copy_layers(teacher.prophetnet.encoder.layers  ,	student.prophetnet.encoder.layers  ,	UpperCAmelCase_     )
   copy_layers(teacher.prophetnet.decoder.layers  ,	student.prophetnet.decoder.layers  ,	UpperCAmelCase_     )
  else:
   copy_layers(teacher.model.encoder.layers  ,	student.model.encoder.layers  ,	UpperCAmelCase_     )
   copy_layers(teacher.model.decoder.layers  ,	student.model.decoder.layers  ,	UpperCAmelCase_     )
 except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
  copy_layers(teacher.encoder.block  ,	student.encoder.block  ,	UpperCAmelCase_     )
  copy_layers(teacher.decoder.block  ,	student.decoder.block  ,	UpperCAmelCase_     )
 logger.info(
     F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""     )
 UpperCAmelCase	:     List[str]						 =			{
     'teacher_type': teacher.config.model_type,
     'copied_encoder_layers': e_layers_to_copy,
     'copied_decoder_layers': d_layers_to_copy,
 }
 student.save_pretrained(UpperCAmelCase_     )
 # 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 os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowercase__         =      3




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('Generating primitive root of p'     )
 while True:
  UpperCAmelCase	:     Union[str, Any]						 =			random.randrange(3  ,	UpperCAmelCase_     )
  if pow(UpperCAmelCase_  ,	2  ,	UpperCAmelCase_     ) == 1:
   continue
  if pow(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ) == 1:
   continue
  return g




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('Generating prime p...'     )
 UpperCAmelCase	:     str						 =			rabin_miller.generate_large_prime(UpperCAmelCase_     )  # select large prime number.
 UpperCAmelCase	:     List[str]						 =			primitive_root(UpperCAmelCase_     )  # one primitive root on modulo p.
 UpperCAmelCase	:     List[Any]						 =			random.randrange(3  ,	UpperCAmelCase_     )  # private_key -> have to be greater than 2 for safety.
 UpperCAmelCase	:     List[Any]						 =			cryptomath.find_mod_inverse(pow(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     Tuple						 =			(key_size, e_a, e_a, p)
 UpperCAmelCase	:     Optional[int]						 =			(key_size, d)

 return public_key, private_key




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if os.path.exists(F"""{name}_pubkey.txt"""     ) or os.path.exists(F"""{name}_privkey.txt"""     ):
  print('\nWARNING:'     )
  print(
      F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
      'Use a different name or delete these files and re-run this program.'     )
  sys.exit()

 UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			generate_key(UpperCAmelCase_     )
 print(F"""\nWriting public key to file {name}_pubkey.txt..."""     )
 with open(F"""{name}_pubkey.txt"""  ,	'w'     ) as fo:
  fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""     )

 print(F"""Writing private key to file {name}_privkey.txt..."""     )
 with open(F"""{name}_privkey.txt"""  ,	'w'     ) as fo:
  fo.write(F"""{private_key[0]},{private_key[1]}"""     )




def 				UpperCamelCase(					):
 print('Making key files...'     )
 make_key_files('elgamal'  ,	20_48     )
 print('Key files generation successful'     )


if __name__ == "__main__":
 main()




