






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


lowercase_			=  logging.get_logger(__name__)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     nn.ModuleList   ,      SCREAMING_SNAKE_CASE__      :     nn.ModuleList   ,      SCREAMING_SNAKE_CASE__      :     List[int]				)							->				None:





    '''simple docstring'''






    A__   				=       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()				)


lowercase_			=  {
    # 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))},
}
lowercase_			=  {
    # 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__      :     str   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]				)							->				Dict:





    '''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(SCREAMING_SNAKE_CASE__				)				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     Tuple				)							->				List[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(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__      :     Union[str, PreTrainedModel]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Path] = "student"   ,      SCREAMING_SNAKE_CASE__      :     Union[int, None] = None   ,      SCREAMING_SNAKE_CASE__      :     Union[int, None] = None   ,      SCREAMING_SNAKE_CASE__      :     int=False   ,      SCREAMING_SNAKE_CASE__      :     Tuple=None   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]=None   ,      **SCREAMING_SNAKE_CASE__      :     Tuple   ,      )							->				Tuple[PreTrainedModel, List[int], List[int]]:





    '''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(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				):
        AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__				).save_pretrained(SCREAMING_SNAKE_CASE__				)  # purely for convenience
        A__   				=       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__				)}'
    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(SCREAMING_SNAKE_CASE__				)

    # Copy weights
    A__   				=       teacher.config_class(**SCREAMING_SNAKE_CASE__				)
    A__   				=       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.
    A__   				=       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
        A__				,						A__   				=       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:
        A__   				=       pick_layers_to_copy(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    if d_layers_to_copy is None:
        A__   				=       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}'				)
    A__   				=       {
        '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)




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_			=  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''simple docstring'''






    A__   				=       parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE__				) for x in param.args				)				)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase_			=  list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       self.run_trainer(
            stage=lowercase_,model_name=lowercase_,eval_steps=lowercase_,num_train_epochs=1,distributed=lowercase_,fpaa=lowercase_,)

        self.do_checks(lowercase_		)

        return output_dir


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

        if fpaa:
            args.extend(['--fp16']		)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        A__   				=       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
        A__   				=       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
        A__   				=       self.get_launcher(lowercase_		)

        A__   				=       launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(lowercase_,env=self.get_env()		)

        return output_dir


    def 						snake_case__					(				self			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







        A__   				=       min(2,get_gpu_count()		) if distributed else 1
        return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

