

"""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


__UpperCamelCase  :      int							      =		logging.get_logger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						int  				=	nn.ModuleList([src_layers[i] for i in layers_to_copy] )
	assert len(__a ) == len(__a ), f'{len(__a )} != {len(__a )}'
	dest_layers.load_state_dict(layers_to_copy.state_dict() )


__UpperCamelCase  :      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))},
}
__UpperCamelCase  :      List[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   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):






	try:
		lowerCAmelCase__	:						Tuple  				=	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(__a ) )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):






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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ = "student"						,     A_ = None						,     A_ = None						,     A_=False						,     A_=None						,     A_=None						,     **A_						,     ):
	lowerCAmelCase__	:						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(__a						,     __a ):
		AutoTokenizer.from_pretrained(__a ).save_pretrained(__a )  # purely for convenience
		lowerCAmelCase__	:						Tuple  				=	AutoModelForSeqaSeqLM.from_pretrained(__a ).eval()
	else:
		assert isinstance(__a						,     __a ), f'teacher must be a model or string got type {type(__a )}'
	lowerCAmelCase__	:						Tuple  				=	teacher.config.to_diff_dict()

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

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

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

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


UpperCAmelCase_   : Any   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  Union[str, Any]						,	*lowercase_     :  List[str]						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          warnings.warn(
              '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
              ''' Please use SegformerImageProcessor instead.'''						,	lowercase_						,	)
          super().__init__(*lowercase_						,	**lowercase_)


