






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


SCREAMING_SNAKE_CASE__      :			Any							=		logging.get_logger(__name__)

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      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() )


SCREAMING_SNAKE_CASE__      :			int							=		{
    # 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))},
}
SCREAMING_SNAKE_CASE__      :			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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				try:
								__magic_name__		:Optional[int]        =      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      __lowercase					( snake_case,  snake_case ):







				"""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      __lowercase					( snake_case,  snake_case = "student",  snake_case = None,  snake_case = None,  snake_case=False,  snake_case=None,  snake_case=None,  **snake_case,  ):







				"""simple docstring"""






				__magic_name__		: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(snake_case,  snake_case ):
								AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )  # purely for convenience
								__magic_name__		:Dict        =      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 )}'''
				__magic_name__		:Dict        =      teacher.config.to_diff_dict()

				try:
								__magic_name__     ,				__magic_name__		:Dict        =      teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												__magic_name__		:Union[str, Any]        =      teacher_e
								if d is None:
												__magic_name__		:int        =      teacher_d
								init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d} )
				except AttributeError:  # T5
								if hasattr(teacher.config,  '''num_encoder_layers''' ):
												__magic_name__     ,				__magic_name__		:Any        =      teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												__magic_name__     ,				__magic_name__		:Any        =      teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												__magic_name__		:Optional[int]        =      teacher_e
								if d is None:
												__magic_name__		:List[str]        =      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
				__magic_name__		:Tuple        =      teacher.config_class(**snake_case )
				__magic_name__		:Any        =      AutoModelForSeqaSeqLM.from_config(snake_case )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				__magic_name__		:Any        =      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
								__magic_name__     ,				__magic_name__		:Optional[int]        =      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:
								__magic_name__		:List[int]        =      pick_layers_to_copy(snake_case,  snake_case )
				if d_layers_to_copy is None:
								__magic_name__		:List[int]        =      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}''' )
				__magic_name__		:Any        =      {
				    '''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)














from __future__ import annotations

from math import pi, sqrt

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative''' )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative''' )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






