





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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->None:
	snake_case__	       =   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()							)


a__					:			Tuple          =     {
    # 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__					:			str          =     {
    # 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			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[Any]:







	try:
		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(__UpperCAmelCase							)							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[int]:







	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = "student"					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = None					,				UpperCAmelCase_=False					,				UpperCAmelCase_=None					,				UpperCAmelCase_=None					,				**UpperCAmelCase_					,				)   ->Tuple[PreTrainedModel, List[int], List[int]]:
	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(__UpperCAmelCase					,				__UpperCAmelCase							):
		AutoTokenizer.from_pretrained(__UpperCAmelCase							).save_pretrained(__UpperCAmelCase							)  # purely for convenience
		snake_case__	       =   AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							).eval()
	else:
		assert isinstance(__UpperCAmelCase					,				__UpperCAmelCase							), f'''teacher must be a model or string got type {type(__UpperCAmelCase							)}'''
	snake_case__	       =   teacher.config.to_diff_dict()

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

	# Copy weights
	snake_case__	       =   teacher.config_class(**__UpperCAmelCase							)
	snake_case__	       =   AutoModelForSeqaSeqLM.from_config(__UpperCAmelCase							)
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	snake_case__	       =   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
		snake_case__       ,  snake_case__	       =   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:
		snake_case__	       =   pick_layers_to_copy(__UpperCAmelCase					,				__UpperCAmelCase							)
	if d_layers_to_copy is None:
		snake_case__	       =   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}'''							)
	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(__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
from math import logaa

def     __magic_name__							(				__UpperCAmelCase = "base_exp.txt"   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0
   __SCREAMING_SNAKE_CASE   				=							0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__UpperCAmelCase   )				,						__UpperCAmelCase   )   )   ):
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							list(map(__UpperCAmelCase				,						line.split(""","""   )   )   )
      if x * logaa(__UpperCAmelCase   ) > largest:
         __SCREAMING_SNAKE_CASE   				=							x * logaa(__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							i + 1
   return result


if __name__ == "__main__":
    print(solution())





