



'''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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)




def        lowerCamelCase  (				_snake_case  :						nn.ModuleList     ,_snake_case  :						nn.ModuleList     ,_snake_case  :						List[int]       ):





    '''simple docstring'''


    lowercase__		       =						nn.ModuleList([src_layers[i] for i in layers_to_copy]       )
    assert len(_snake_case       ) == len(_snake_case       ), f'''{len(_snake_case       )} != {len(_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        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    try:
        lowercase__		       =						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(_snake_case       )       )




def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						Optional[int]       ):





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




def        lowerCamelCase  (				_snake_case  :						Union[str, PreTrainedModel]     ,_snake_case  :						Union[str, Path] = "student"     ,_snake_case  :						Union[int, None] = None     ,_snake_case  :						Union[int, None] = None     ,_snake_case  :						Optional[Any]=False     ,_snake_case  :						Any=None     ,_snake_case  :						List[Any]=None     ,**_snake_case  :						Optional[int]     ,):





    '''simple docstring'''


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

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

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

    try:
        if hasattr(
            _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     ,_snake_case       )
            copy_layers(teacher.prophetnet.decoder.layers     ,student.prophetnet.decoder.layers     ,_snake_case       )
        else:
            copy_layers(teacher.model.encoder.layers     ,student.model.encoder.layers     ,_snake_case       )
            copy_layers(teacher.model.decoder.layers     ,student.model.decoder.layers     ,_snake_case       )
    except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
        copy_layers(teacher.encoder.block     ,student.encoder.block     ,_snake_case       )
        copy_layers(teacher.decoder.block     ,student.decoder.block     ,_snake_case       )
    logger.info(
        f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''       )
    lowercase__		       =						{
        "teacher_type": teacher.config.model_type,
        "copied_encoder_layers": e_layers_to_copy,
        "copied_decoder_layers": d_layers_to_copy,
    }
    student.save_pretrained(_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)









'''simple docstring'''





import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE__      					=     "1"
SCREAMING_SNAKE_CASE__      					=     "0"
SCREAMING_SNAKE_CASE__      					=     "1"

SCREAMING_SNAKE_CASE__      					=     ort.SessionOptions()
SCREAMING_SNAKE_CASE__      					=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
SCREAMING_SNAKE_CASE__      					=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
SCREAMING_SNAKE_CASE__      					=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE__      					=     ort.RunOptions()

SCREAMING_SNAKE_CASE__      					=     128
SCREAMING_SNAKE_CASE__      					=     1
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
SCREAMING_SNAKE_CASE__      					=     time.time()
SCREAMING_SNAKE_CASE__      					=     2000
SCREAMING_SNAKE_CASE__      					=     {}
for iter in range(max_iters):
 SCREAMING_SNAKE_CASE__      					=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))




