



'''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				:    str     						=logging.get_logger(__name__)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			None:
	_lowercase					:       List[Any]       =		nn.ModuleList([src_layers[i] for i in layers_to_copy]					)
	assert len(_lowercase					) == len(_lowercase					), f'''{len(_lowercase					)} != {len(_lowercase					)}'''
	dest_layers.load_state_dict(layers_to_copy.state_dict()					)


_A				:    Dict     						={
    # 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				:    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]},
    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				(							_lowercase,				_lowercase					) ->			Any:
	try:
		_lowercase					:       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(_lowercase					)					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			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(_lowercase					)					)
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]







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

	try:
		_lowercase  ,    _lowercase					:       int       =		teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			_lowercase					:       Optional[int]       =		teacher_e
		if d is None:
			_lowercase					:       List[str]       =		teacher_d
		init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}					)
	except AttributeError:  # T5
		if hasattr(teacher.config,				'num_encoder_layers'					):
			_lowercase  ,    _lowercase					:       List[str]       =		teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			_lowercase  ,    _lowercase					:       List[Any]       =		teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			_lowercase					:       Any       =		teacher_e
		if d is None:
			_lowercase					:       Tuple       =		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(_lowercase					)

	# Copy weights
	_lowercase					:       List[Any]       =		teacher.config_class(**_lowercase					)
	_lowercase					:       Optional[Any]       =		AutoModelForSeqaSeqLM.from_config(_lowercase					)
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	_lowercase					:       Tuple       =		student.load_state_dict(teacher.state_dict(),				strict=_lowercase					)
	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					:       Dict       =		list(range(_lowercase					)					), list(range(_lowercase					)					)
		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(_lowercase					)
		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(_lowercase,				_lowercase					)
	if d_layers_to_copy is None:
		_lowercase					:       List[int]       =		pick_layers_to_copy(_lowercase,				_lowercase					)

	try:
		if hasattr(
		    _lowercase,				'prophetnet'					):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
			copy_layers(teacher.prophetnet.encoder.layers,				student.prophetnet.encoder.layers,				_lowercase					)
			copy_layers(teacher.prophetnet.decoder.layers,				student.prophetnet.decoder.layers,				_lowercase					)
		else:
			copy_layers(teacher.model.encoder.layers,				student.model.encoder.layers,				_lowercase					)
			copy_layers(teacher.model.decoder.layers,				student.model.decoder.layers,				_lowercase					)
	except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
		copy_layers(teacher.encoder.block,				student.encoder.block,				_lowercase					)
		copy_layers(teacher.decoder.block,				student.decoder.block,				_lowercase					)
	logger.info(
	    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''					)
	_lowercase					:       List[str]       =		{
	    'teacher_type': teacher.config.model_type,
	    'copied_encoder_layers': e_layers_to_copy,
	    'copied_decoder_layers': d_layers_to_copy,
	}
	student.save_pretrained(_lowercase					)
	# 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_A				:    Dict     						=logging.get_logger(__name__)

_A				:    Dict     						={
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""megatron-bert"""




					def __init__(  self				:	int     ,  UpperCamelCase_				:	int=2_9056     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[Any]=24     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=4096     ,  UpperCamelCase_				:	Optional[Any]="gelu"     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	int=512     ,  UpperCamelCase_				:	Dict=2     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	Any=1E-12     ,  UpperCamelCase_				:	Tuple=0     ,  UpperCamelCase_				:	Optional[int]="absolute"     ,  UpperCamelCase_				:	Optional[Any]=True     ,  **UpperCamelCase_				:	Any     ,  )  ->       List[Any]:



						'''simple docstring'''





						super().__init__(pad_token_id=UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Any       =		hidden_size
						_lowercase					:       Union[str, Any]       =		num_hidden_layers
						_lowercase					:       Dict       =		num_attention_heads
						_lowercase					:       Dict       =		hidden_act
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		hidden_dropout_prob
						_lowercase					:       Optional[Any]       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       str       =		type_vocab_size
						_lowercase					:       Optional[Any]       =		initializer_range
						_lowercase					:       List[str]       =		layer_norm_eps
						_lowercase					:       List[Any]       =		position_embedding_type
						_lowercase					:       Optional[Any]       =		use_cache
