




"""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_        =       logging.get_logger(__name__)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  Union[str, 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()	)


a_        =       {
    # 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_        =       {
    # 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     ( __UpperCamelCase	,				__UpperCamelCase	):
			try:
						__lowercase			:  List[str]								=       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	,				):
			__lowercase			:  int								=       '''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
						__lowercase			:  str								=       AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase	).eval()
			else:
						assert isinstance(__UpperCamelCase	,				__UpperCamelCase	), f"""teacher must be a model or string got type {type(__UpperCamelCase	)}"""
			__lowercase			:  Tuple								=       teacher.config.to_diff_dict()

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






def 			lowerCamelCase__			(       _a):
	if not isinstance(_a				,      _a):
		SCREAMING_SNAKE_CASE :   Tuple					  =							f"Input value of [number={number}] must be an integer"
		raise TypeError(_a)
	if number < 0:
		return False
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()