


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


snake_case							:       Dict     			=      logging.get_logger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				nn.ModuleList					,    __lowerCAmelCase							:				nn.ModuleList					,    __lowerCAmelCase							:				List[int]   ):
							a__             =     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()   )


snake_case							:       str     			=      {
    # 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))},
}
snake_case							:       int     			=      {
    # 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 							__lowercase   ( __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				List[str]   ):
							try:
														a__             =     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 							__lowercase   ( __lowerCAmelCase							:				Optional[int]					,    __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 							__lowercase   ( __lowerCAmelCase							:				Union[str, PreTrainedModel]					,    __lowerCAmelCase							:				Union[str, Path] = "student"					,    __lowerCAmelCase							:				Union[int, None] = None					,    __lowerCAmelCase							:				Union[int, None] = None					,    __lowerCAmelCase							:				Union[str, Any]=False					,    __lowerCAmelCase							:				List[Any]=None					,    __lowerCAmelCase							:				Any=None					,    **__lowerCAmelCase							:				Tuple					,    ):
							a__             =     '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
														a__             =     AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase   ).eval()
							else:
														assert isinstance(__lowerCAmelCase					,    __lowerCAmelCase   ), F'teacher must be a model or string got type {type(__lowerCAmelCase   )}'
							a__             =     teacher.config.to_diff_dict()

							try:
														a__      ,					a__             =     teacher.config.encoder_layers, teacher.config.decoder_layers
														if e is None:
																					a__             =     teacher_e
														if d is None:
																					a__             =     teacher_d
														init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}   )
							except AttributeError:  # T5
														if hasattr(teacher.config					,    'num_encoder_layers'   ):
																					a__      ,					a__             =     teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
														else:
																					a__      ,					a__             =     teacher.config.num_layers, teacher.config.num_decoder_layers
														if e is None:
																					a__             =     teacher_e
														if d is None:
																					a__             =     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
							a__             =     teacher.config_class(**__lowerCAmelCase   )
							a__             =     AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase   )
							# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
							a__             =     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
														a__      ,					a__             =     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:
														a__             =     pick_layers_to_copy(__lowerCAmelCase					,    __lowerCAmelCase   )
							if d_layers_to_copy is None:
														a__             =     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}'   )
							a__             =     {
							    '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)








def 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float(moles / volume   ) * nfactor   )






def 							__lowercase   ( __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float((moles * 0.0_821 * temperature) / (volume)   )   )






def 							__lowercase   ( __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float((moles * 0.0_821 * temperature) / (pressure)   )   )






def 							__lowercase   ( __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							return round(float((pressure * volume) / (0.0_821 * moles)   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




