"""simple docstring"""


from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_pt_objects import *  # noqa F403
else:
    from .consistency_models import ConsistencyModelPipeline
    from .dance_diffusion import DanceDiffusionPipeline
    from .ddim import DDIMPipeline
    from .ddpm import DDPMPipeline
    from .dit import DiTPipeline
    from .latent_diffusion import LDMSuperResolutionPipeline
    from .latent_diffusion_uncond import LDMPipeline
    from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
    from .pndm import PNDMPipeline
    from .repaint import RePaintPipeline
    from .score_sde_ve import ScoreSdeVePipeline
    from .stochastic_karras_ve import KarrasVePipeline

try:
    if not (is_torch_available() and is_librosa_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
    from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
    if not (is_torch_available() and is_transformers_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
    from .audioldm import AudioLDMPipeline
    from .controlnet import (
        StableDiffusionControlNetImgaImgPipeline,
        StableDiffusionControlNetInpaintPipeline,
        StableDiffusionControlNetPipeline,
    )
    from .deepfloyd_if import (
        IFImgaImgPipeline,
        IFImgaImgSuperResolutionPipeline,
        IFInpaintingPipeline,
        IFInpaintingSuperResolutionPipeline,
        IFPipeline,
        IFSuperResolutionPipeline,
    )
    from .kandinsky import (
        KandinskyImgaImgPipeline,
        KandinskyInpaintPipeline,
        KandinskyPipeline,
        KandinskyPriorPipeline,
    )
    from .kandinskya_a import (
        KandinskyVaaControlnetImgaImgPipeline,
        KandinskyVaaControlnetPipeline,
        KandinskyVaaImgaImgPipeline,
        KandinskyVaaInpaintPipeline,
        KandinskyVaaPipeline,
        KandinskyVaaPriorEmbaEmbPipeline,
        KandinskyVaaPriorPipeline,
    )
    from .latent_diffusion import LDMTextToImagePipeline
    from .paint_by_example import PaintByExamplePipeline
    from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
    from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
    from .stable_diffusion import (
        CycleDiffusionPipeline,
        StableDiffusionAttendAndExcitePipeline,
        StableDiffusionDepthaImgPipeline,
        StableDiffusionDiffEditPipeline,
        StableDiffusionImageVariationPipeline,
        StableDiffusionImgaImgPipeline,
        StableDiffusionInpaintPipeline,
        StableDiffusionInpaintPipelineLegacy,
        StableDiffusionInstructPixaPixPipeline,
        StableDiffusionLatentUpscalePipeline,
        StableDiffusionLDMaDPipeline,
        StableDiffusionModelEditingPipeline,
        StableDiffusionPanoramaPipeline,
        StableDiffusionParadigmsPipeline,
        StableDiffusionPipeline,
        StableDiffusionPixaPixZeroPipeline,
        StableDiffusionSAGPipeline,
        StableDiffusionUpscalePipeline,
        StableUnCLIPImgaImgPipeline,
        StableUnCLIPPipeline,
    )
    from .stable_diffusion_safe import StableDiffusionPipelineSafe
    from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
    from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
    from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
    from .versatile_diffusion import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
    from .vq_diffusion import VQDiffusionPipeline


try:
    if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
    from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
    if not is_onnx_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_onnx_objects import *  # noqa F403
else:
    from .onnx_utils import OnnxRuntimeModel

try:
    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
    from .stable_diffusion import (
        OnnxStableDiffusionImgaImgPipeline,
        OnnxStableDiffusionInpaintPipeline,
        OnnxStableDiffusionInpaintPipelineLegacy,
        OnnxStableDiffusionPipeline,
        OnnxStableDiffusionUpscalePipeline,
        StableDiffusionOnnxPipeline,
    )

try:
    if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
    from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_flax_objects import *  # noqa F403
else:
    from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
    if not (is_flax_available() and is_transformers_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
    from .controlnet import FlaxStableDiffusionControlNetPipeline
    from .stable_diffusion import (
        FlaxStableDiffusionImgaImgPipeline,
        FlaxStableDiffusionInpaintPipeline,
        FlaxStableDiffusionPipeline,
    )
try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline


"""simple docstring"""


import bsa
import requests







def    UpperCAmelCase       (						UpperCAmelCase   )							-> dict[str, str]:
     return {
         "name": soup.ha.a.text,
         "genre": soup.find('span'		,   class_='genre'   ).text.strip(),
         "rating": soup.strong.text,
         "page_link": f'https://www.imdb.com{soup.a.get("href"   )}',
     }







def    UpperCAmelCase       (						UpperCAmelCase = 5   )							-> tuple:
     snake_case_   			=   int(float(UpperCAmelCase   )   )
     if num_movies < 1:
          return ()
     snake_case_   			=   (
         'https://www.imdb.com/search/title?title_type='
         f'feature&sort=num_votes,desc&count={num_movies}'
     )
     snake_case_   			=   bsa.BeautifulSoup(requests.get(UpperCAmelCase   ).content		,   'html.parser'   )
     return tuple(
         get_movie_data_from_soup(UpperCAmelCase   )
         for movie in source.find_all('div'		,   class_='lister-item mode-advanced'   )   )


if __name__ == "__main__":
    import json

    __UpperCamelCase										=     int(input('''How many movies would you like to see? '''))
    print(
        ''', '''.join(
            json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
        )
    )

