


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,
)













from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


snake_case__   :						str													=   logging.get_logger(__name__)






def      _snake_case     (__lowercase):
   if isinstance(__lowercase     ,   (list, tuple)) and isinstance(videos[0]     ,   (list, tuple)) and is_valid_image(videos[0][0]):
      return videos

   elif isinstance(__lowercase     ,   (list, tuple)) and is_valid_image(videos[0]):
      return [videos]

   elif is_valid_image(__lowercase):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}""")


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = ["""pixel_values"""]




  def __init__(  self		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = PILImageResampling.BILINEAR		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		_UpperCAmelCase = 1 / 255		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			None:
     super().__init__(**_UpperCAmelCase			)
     UpperCamelCase_						=  size if size is not None else {'shortest_edge': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		param_name='crop_size'			)

     UpperCamelCase_						=  do_resize
     UpperCamelCase_						=  size
     UpperCamelCase_						=  do_center_crop
     UpperCamelCase_						=  crop_size
     UpperCamelCase_						=  resample
     UpperCamelCase_						=  do_rescale
     UpperCamelCase_						=  rescale_factor
     UpperCamelCase_						=  do_normalize
     UpperCamelCase_						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     UpperCamelCase_						=  image_std if image_std is not None else IMAGENET_STANDARD_STD




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = PILImageResampling.BILINEAR		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     if "shortest_edge" in size:
        UpperCamelCase_						=  get_resize_output_image_size(_UpperCAmelCase		,		size['shortest_edge']		,		default_to_square=_UpperCAmelCase			)
     elif "height" in size and "width" in size:
        UpperCamelCase_						=  (size['height'], size['width'])
     else:
        raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""			)
     return resize(_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase			)
     if "height" not in size or "width" not in size:
        raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""			)
     return center_crop(_UpperCAmelCase		,		size=(size['height'], size['width'])		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			int:
     return rescale(_UpperCAmelCase		,		scale=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     return normalize(_UpperCAmelCase		,		mean=_UpperCAmelCase		,		std=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		)      ->			np.ndarray:
     if do_resize and size is None or resample is None:
        raise ValueError('Size and resample must be specified if do_resize is True.'			)

     if do_center_crop and crop_size is None:
        raise ValueError('Crop size must be specified if do_center_crop is True.'			)

     if do_rescale and rescale_factor is None:
        raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

     # All transformations expect numpy arrays.
     UpperCamelCase_						=  to_numpy_array(_UpperCAmelCase			)

     if do_resize:
        UpperCamelCase_						=  self.resize(image=_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase			)

     if do_center_crop:
        UpperCamelCase_						=  self.center_crop(_UpperCAmelCase		,		size=_UpperCAmelCase			)

     if do_rescale:
        UpperCamelCase_						=  self.rescale(image=_UpperCAmelCase		,		scale=_UpperCAmelCase			)

     if do_normalize:
        UpperCamelCase_						=  self.normalize(image=_UpperCAmelCase		,		mean=_UpperCAmelCase		,		std=_UpperCAmelCase			)

     UpperCamelCase_						=  to_channel_dimension_format(_UpperCAmelCase		,		_UpperCAmelCase			)
     return image




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		**_UpperCAmelCase		,		)      ->			PIL.Image.Image:
     UpperCamelCase_						=  do_resize if do_resize is not None else self.do_resize
     UpperCamelCase_						=  resample if resample is not None else self.resample
     UpperCamelCase_						=  do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCamelCase_						=  do_rescale if do_rescale is not None else self.do_rescale
     UpperCamelCase_						=  rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCamelCase_						=  do_normalize if do_normalize is not None else self.do_normalize
     UpperCamelCase_						=  image_mean if image_mean is not None else self.image_mean
     UpperCamelCase_						=  image_std if image_std is not None else self.image_std

     UpperCamelCase_						=  size if size is not None else self.size
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else self.crop_size
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		param_name='crop_size'			)

     if not valid_images(_UpperCAmelCase			):
        raise ValueError(
            'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
            'torch.Tensor, tf.Tensor or jax.ndarray.'			)

     UpperCamelCase_						=  make_batched(_UpperCAmelCase			)

     UpperCamelCase_						=  [
         [
             self._preprocess_image(
                 image=_UpperCAmelCase		,		do_resize=_UpperCAmelCase		,		size=_UpperCAmelCase		,		resample=_UpperCAmelCase		,		do_center_crop=_UpperCAmelCase		,		crop_size=_UpperCAmelCase		,		do_rescale=_UpperCAmelCase		,		rescale_factor=_UpperCAmelCase		,		do_normalize=_UpperCAmelCase		,		image_mean=_UpperCAmelCase		,		image_std=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		)
             for img in video
         ]
         for video in videos
     ]

     UpperCamelCase_						=  {'pixel_values': videos}
     return BatchFeature(data=_UpperCAmelCase		,		tensor_type=_UpperCAmelCase			)





