



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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







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


SCREAMING_SNAKE_CASE     				=						{
    # 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))},
}
SCREAMING_SNAKE_CASE     				=						{
    # 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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							str:
		try:
				UpperCAmelCase_						=				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(__SCREAMING_SNAKE_CASE ) )







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







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

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

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

		try:
				if hasattr(
				    __SCREAMING_SNAKE_CASE			,     "prophetnet" ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
						copy_layers(teacher.prophetnet.encoder.layers			,     student.prophetnet.encoder.layers			,     __SCREAMING_SNAKE_CASE )
						copy_layers(teacher.prophetnet.decoder.layers			,     student.prophetnet.decoder.layers			,     __SCREAMING_SNAKE_CASE )
				else:
						copy_layers(teacher.model.encoder.layers			,     student.model.encoder.layers			,     __SCREAMING_SNAKE_CASE )
						copy_layers(teacher.model.decoder.layers			,     student.model.decoder.layers			,     __SCREAMING_SNAKE_CASE )
		except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
				copy_layers(teacher.encoder.block			,     student.encoder.block			,     __SCREAMING_SNAKE_CASE )
				copy_layers(teacher.decoder.block			,     student.decoder.block			,     __SCREAMING_SNAKE_CASE )
		logger.info(
		    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
		UpperCAmelCase_						=				{
		    "teacher_type": teacher.config.model_type,
		    "copied_encoder_layers": e_layers_to_copy,
		    "copied_decoder_layers": d_layers_to_copy,
		}
		student.save_pretrained(__SCREAMING_SNAKE_CASE )
		# 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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						res += a

				a += a
				b >>= 1

		return res







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						UpperCAmelCase_						=				((res % c) + (a % c)) % c

				a += a
				b >>= 1

		return res

