
'''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    =		logging.get_logger(__name__)


def A				(__lowerCamelCase				:nn.ModuleList			,     __lowerCamelCase				:nn.ModuleList			,     __lowerCamelCase				:List[int]		):
						_lowerCAmelCase  						=			nn.ModuleList([src_layers[i] for i in layers_to_copy]		)
						assert len(__lowerCamelCase		) == len(__lowerCamelCase		), f'{len(__lowerCamelCase		)} != {len(__lowerCamelCase		)}'
						dest_layers.load_state_dict(layers_to_copy.state_dict()		)


_lowercase    =		{
    # 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    =		{
    # 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 A				(__lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Optional[Any]		):
						try:
												_lowerCAmelCase  						=			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(__lowerCamelCase		)		)


def A				(__lowerCamelCase				:Dict			,     __lowerCamelCase				: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(__lowerCamelCase		)		)
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def A				(__lowerCamelCase				:Union[str, PreTrainedModel]			,     __lowerCamelCase				:Union[str, Path] = "student"			,     __lowerCamelCase				:Union[int, None] = None			,     __lowerCamelCase				:Union[int, None] = None			,     __lowerCamelCase				:List[str]=False			,     __lowerCamelCase				:Optional[int]=None			,     __lowerCamelCase				:List[str]=None			,     **__lowerCamelCase				:Any			,     ):
						_lowerCAmelCase  						=			"""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(__lowerCamelCase			,     __lowerCamelCase		):
												AutoTokenizer.from_pretrained(__lowerCamelCase		).save_pretrained(__lowerCamelCase		)  # purely for convenience
												_lowerCAmelCase  						=			AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase		).eval()
						else:
												assert isinstance(__lowerCamelCase			,     __lowerCamelCase		), f'teacher must be a model or string got type {type(__lowerCamelCase		)}'
						_lowerCAmelCase  						=			teacher.config.to_diff_dict()

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

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

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

from transformers import PretrainedConfig


_lowercase    =		logging.getLogger(__name__)


_lowercase    =		{
    """bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       '''bertabs'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=512   ,       _lowercase=6   ,       _lowercase=512   ,       _lowercase=8   ,       _lowercase=512   ,       _lowercase=0.2   ,       _lowercase=6   ,       _lowercase=768   ,       _lowercase=8   ,       _lowercase=2_048   ,       _lowercase=0.2   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(**_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			max_pos

												_lowerCAmelCase  						=			enc_layers
												_lowerCAmelCase  						=			enc_hidden_size
												_lowerCAmelCase  						=			enc_heads
												_lowerCAmelCase  						=			enc_ff_size
												_lowerCAmelCase  						=			enc_dropout

												_lowerCAmelCase  						=			dec_layers
												_lowerCAmelCase  						=			dec_hidden_size
												_lowerCAmelCase  						=			dec_heads
												_lowerCAmelCase  						=			dec_ff_size
												_lowerCAmelCase  						=			dec_dropout
