'''simple docstring'''

from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # We need to create solution object to save path.
      lowercase_     =      [[0 for _ in range(__lowerCamelCase				)] for _ in range(__lowerCamelCase				)]
      lowercase_     =      run_maze(__lowerCamelCase ,		0 ,		0 ,		__lowerCamelCase				)
      if solved:
            print("\n".join(str(__lowerCamelCase				) for row in solutions				)				)
      else:
            print("No solution exists!"				)
      return solved


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # Final check point.
      if i == j == (size - 1):
            lowercase_     =      1
            return True

      lowercase_     =      (not i < 0) and (not j < 0)  # Check lower bounds
      lowercase_     =      (i < size) and (j < size)  # Check upper bounds

      if lower_flag and upper_flag:
            # check for already visited and block points.
            lowercase_     =      (not solutions[i][j]) and (not maze[i][j])
            if block_flag:
                  # check visited
                  lowercase_     =      1

                  # check for directions
                  if (
                      run_maze(__lowerCamelCase ,		i + 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j + 1 ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		i - 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j - 1 ,		__lowerCamelCase				)
                  ):
                        return True

                  lowercase_     =      0
                  return False
      return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()






