


"""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__    :							List[Any]            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    nn.ModuleList([src_layers[i] for i in layers_to_copy]       )
					assert len(_SCREAMING_SNAKE_CASE       ) == len(_SCREAMING_SNAKE_CASE       ), f"""{len(_SCREAMING_SNAKE_CASE       )} != {len(_SCREAMING_SNAKE_CASE       )}"""
					dest_layers.load_state_dict(layers_to_copy.state_dict()       )


a__    :							Tuple            =					{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
a__    :							Union[str, 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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					try:
										__SCREAMING_SNAKE_CASE						       =    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(_SCREAMING_SNAKE_CASE       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''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(_SCREAMING_SNAKE_CASE       )       )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_ = "student"  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_=False  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						**lowerCAmelCase_  ,						):




					'''simple docstring'''


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

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

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

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

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return math.sqrt(sum(pow(a - b  ,						2       ) for a, b in zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Wrong input data's dimensions... "
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(lowerCAmelCase_       )

					try:
										if dataset.shape[1] != value_array.shape[1]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "Wrong input data's shape... "
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(lowerCAmelCase_       )
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError("Wrong shape"       )

					if dataset.dtype != value_array.dtype:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

					for value in value_array:
										__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						dataset[0]       )
										__SCREAMING_SNAKE_CASE						       =    dataset[0].tolist()

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

															if dist > temp_dist:
																				__SCREAMING_SNAKE_CASE						       =    temp_dist
																				__SCREAMING_SNAKE_CASE						       =    dataset_value.tolist()

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return np.dot(lowerCAmelCase_  ,						lowerCAmelCase_       ) / (norm(lowerCAmelCase_       ) * norm(lowerCAmelCase_       ))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
