
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


_lowerCamelCase       :				Dict       =			logging.get_logger(__name__)






def        a_		(       __lowercase   :	nn.ModuleList       ,					__lowercase   :	nn.ModuleList       ,					__lowercase   :	List[int] ) ->  None:
			_snake_case		=			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() )


_lowerCamelCase       :				List[Any]       =			{
    # 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))},
}
_lowerCamelCase       :				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        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	List[Any] ) ->  int:
			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(__lowercase ) )






def        a_		(       __lowercase   :	Dict       ,					__lowercase   :	Optional[Any] ) ->  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        a_		(       __lowercase   :	Union[str, PreTrainedModel]       ,					__lowercase   :	Union[str, Path] = "student"       ,					__lowercase   :	Union[int, None] = None       ,					__lowercase   :	Union[int, None] = None       ,					__lowercase   :	List[str]=False       ,					__lowercase   :	Tuple=None       ,					__lowercase   :	str=None       ,					**__lowercase   :	Any       ,					) ->  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(__lowercase       ,					__lowercase ):
						AutoTokenizer.from_pretrained(__lowercase ).save_pretrained(__lowercase )  # purely for convenience
						_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(__lowercase ).eval()
			else:
						assert isinstance(__lowercase       ,					__lowercase ), f'''teacher must be a model or string got type {type(__lowercase )}'''
			_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(__lowercase )

			# Copy weights
			_snake_case		=			teacher.config_class(**__lowercase )
			_snake_case		=			AutoModelForSeqaSeqLM.from_config(__lowercase )
			# 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=__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
						_snake_case	,					_snake_case		=			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:
						_snake_case		=			pick_layers_to_copy(__lowercase       ,					__lowercase )
			if d_layers_to_copy is None:
						_snake_case		=			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}''' )
			_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(__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)

def        a_		(       __lowercase   :	str ) ->  int:
			_snake_case		=			hex_num.strip()
			if not hex_num:
						raise ValueError('No value was passed to the function' )

			_snake_case		=			hex_num[0] == '-'
			if is_negative:
						_snake_case		=			hex_num[1:]

			try:
						_snake_case		=			int(__lowercase       ,					16 )
			except ValueError:
						raise ValueError('Invalid value was passed to the function' )

			_snake_case		=			''
			while int_num > 0:
						_snake_case		=			str(int_num % 2 ) + bin_str
						int_num >>= 1

			return int(('-' + bin_str) if is_negative else bin_str )


if __name__ == "__main__":
			import doctest

			doctest.testmod()