


"""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


lowerCamelCase_   :   Union[str, Any]									=  logging.get_logger(__name__)
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=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[str]									=  {
    # 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))},
}
lowerCamelCase_   :   Union[str, Any]									=  {
    # 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 _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    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(lowercase		)		)
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    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       ,    lowercase = "student"       ,    lowercase = None       ,    lowercase = None       ,    lowercase=False       ,    lowercase=None       ,    lowercase=None       ,    **lowercase       ,    ):







    """simple docstring"""
    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(lowercase       ,    lowercase		):
        AutoTokenizer.from_pretrained(lowercase		).save_pretrained(lowercase		)  # purely for convenience
        a 		=AutoModelForSeqaSeqLM.from_pretrained(lowercase		).eval()
    else:
        assert isinstance(lowercase       ,    lowercase		), f'''teacher must be a model or string got type {type(lowercase		)}'''
    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(lowercase		)

    # Copy weights
    a 		=teacher.config_class(**lowercase		)
    a 		=AutoModelForSeqaSeqLM.from_config(lowercase		)
    # 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=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
        a		,      a 		=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:
        a 		=pick_layers_to_copy(lowercase       ,    lowercase		)
    if d_layers_to_copy is None:
        a 		=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}'''		)
    a 		={
        '''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)




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_:Any             =						[
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

SCREAMING_SNAKE_CASE_:Optional[int]             =						[
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2""", """rougeL"""]		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2"""]		)
    assert (
        pd.DataFrame(no_aggregation["""rouge2"""]		).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]		).fmeasure.mean()
    )




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   """rougeLsum"""
    A :       str          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    assert score > score_no_sep




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   ["""rouge1""", """rouge2""", """rougeL"""]
    A :       Dict          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    A :       List[str]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    assert score_sep == score_no_sep




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Optional[Any]          =   [
        """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
    ]
    A :       Optional[Any]          =   [
        """Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
        """ the final seconds on board Flight 9525.""",
    ]
    assert calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		) == calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		)




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   [
        """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
    ]
    A :       Union[str, Any]          =   [
        """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
    ]

    A :       int          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]      ,     newline_sep=_lowerCAmelCase		)["""rougeLsum"""]
    A :       Optional[Any]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]		)["""rougeLsum"""]
    assert new_score > prev_score




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Tuple          =   Path("""examples/seq2seq/test_data/wmt_en_ro"""		)
    A :       Optional[Any]          =   calculate_rouge_path(data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       List[Any]          =   calculate_rouge_path(
        data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)      ,     bootstrap_aggregation=_lowerCAmelCase		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)


