


'''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      :      List[str]            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      nn.ModuleList				,       A       :      nn.ModuleList				,       A       :      List[int]					):




						'''simple docstring'''






						UpperCAmelCase					    =     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()					)


_lowercase      :      Any            =					{
    # 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      :      List[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]},
    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 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      Dict					):




						'''simple docstring'''






						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(A					)					)


def 				lowerCamelCase__				(	A       :      int				,       A       :      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 				lowerCamelCase__				(	A       :      Union[str, PreTrainedModel]				,       A       :      Union[str, Path] = "student"				,       A       :      Union[int, None] = None				,       A       :      Union[int, None] = None				,       A       :      Dict=False				,       A       :      int=None				,       A       :      Union[str, Any]=None				,       **A       :      str				,       ):




						'''simple docstring'''






						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(A				,       A					):
												AutoTokenizer.from_pretrained(A					).save_pretrained(A					)  # purely for convenience
												UpperCAmelCase					    =     AutoModelForSeqaSeqLM.from_pretrained(A					).eval()
						else:
												assert isinstance(A				,       A					), f"""teacher must be a model or string got type {type(A					)}"""
						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(A					)

						# Copy weights
						UpperCAmelCase					    =     teacher.config_class(**A					)
						UpperCAmelCase					    =     AutoModelForSeqaSeqLM.from_config(A					)
						# 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=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
												UpperCAmelCase    ,    UpperCAmelCase					    =     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:
												UpperCAmelCase					    =     pick_layers_to_copy(A				,       A					)
						if d_layers_to_copy is None:
												UpperCAmelCase					    =     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}"""					)
						UpperCAmelCase					    =     {
						    '''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 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						return int(input_a == input_a == 0					)


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						print('''Truth Table of NOR Gate:'''					)
						print('''|   Input 1   |  Input 2  | Output |'''					)
						print(f"""|      0      |    0      |  {nor_gate(0				,       0					)}     |"""					)
						print(f"""|      0      |    1      |  {nor_gate(0				,       1					)}     |"""					)
						print(f"""|      1      |    0      |  {nor_gate(1				,       0					)}     |"""					)
						print(f"""|      1      |    1      |  {nor_gate(1				,       1					)}     |"""					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





