

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)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         ImageTextPipelineOutput,
         UniDiffuserPipeline,
     )
else:
     from .modeling_text_decoder import UniDiffuserTextDecoder
     from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
     from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

