
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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE__							=				logging.get_logger(__name__)


if is_vision_available():
							import PIL
class 	A__   (						UpperCAmelCase__				):
							lowerCAmelCase__		:						Optional[int]    			=							["pixel_values"]





							def __init__( self  :   Dict     , _UpperCAmelCase  :   bool = True     , _UpperCAmelCase  :   Dict[str, int] = None     , _UpperCAmelCase  :   PILImageResampling = PILImageResampling.BICUBIC     , _UpperCAmelCase  :   bool = True     , _UpperCAmelCase  :   Dict[str, int] = None     , _UpperCAmelCase  :   bool = True     , _UpperCAmelCase  :   Union[int, float] = 1 / 2_55     , _UpperCAmelCase  :   bool = True     , _UpperCAmelCase  :   Optional[Union[float, List[float]]] = None     , _UpperCAmelCase  :   Optional[Union[float, List[float]]] = None     , _UpperCAmelCase  :   bool = True     , **_UpperCAmelCase  :   Any     , )						->		List[str]:



											"""simple docstring"""


											super().__init__(**lowercase_				)
											__lowercase								=					size if size is not None else {'''shortest_edge''': 2_24}
											__lowercase								=					get_size_dict(lowercase_     , default_to_square=lowercase_				)
											__lowercase								=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
											__lowercase								=					get_size_dict(lowercase_     , default_to_square=lowercase_     , param_name='crop_size'				)

											__lowercase								=					do_resize
											__lowercase								=					size
											__lowercase								=					resample
											__lowercase								=					do_center_crop
											__lowercase								=					crop_size
											__lowercase								=					do_rescale
											__lowercase								=					rescale_factor
											__lowercase								=					do_normalize
											__lowercase								=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											__lowercase								=					image_std if image_std is not None else OPENAI_CLIP_STD
											__lowercase								=					do_convert_rgb





							def 			a__ ( self  :   List[Any]     , _UpperCAmelCase  :   np.ndarray     , _UpperCAmelCase  :   Dict[str, int]     , _UpperCAmelCase  :   PILImageResampling = PILImageResampling.BICUBIC     , _UpperCAmelCase  :   Optional[Union[str, ChannelDimension]] = None     , **_UpperCAmelCase  :   Optional[Any]     , )						->		Any:



											"""simple docstring"""


											__lowercase								=					get_size_dict(lowercase_     , default_to_square=lowercase_				)
											if "shortest_edge" not in size:
															raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
											__lowercase								=					get_resize_output_image_size(lowercase_     , size=size['shortest_edge']     , default_to_square=lowercase_				)
											return resize(lowercase_     , size=lowercase_     , resample=lowercase_     , data_format=lowercase_     , **lowercase_				)





							def 			a__ ( self  :   str     , _UpperCAmelCase  :   np.ndarray     , _UpperCAmelCase  :   Dict[str, int]     , _UpperCAmelCase  :   Optional[Union[str, ChannelDimension]] = None     , **_UpperCAmelCase  :   Dict     , )						->		Dict:



											"""simple docstring"""


											__lowercase								=					get_size_dict(lowercase_				)
											if "height" not in size or "width" not in size:
															raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
											return center_crop(lowercase_     , size=(size['height'], size['width'])     , data_format=lowercase_     , **lowercase_				)





							def 			a__ ( self  :   Optional[Any]     , _UpperCAmelCase  :   np.ndarray     , _UpperCAmelCase  :   Union[int, float]     , _UpperCAmelCase  :   Optional[Union[str, ChannelDimension]] = None     , **_UpperCAmelCase  :   List[Any]     , )						->		Optional[int]:



											"""simple docstring"""


											return rescale(lowercase_     , scale=lowercase_     , data_format=lowercase_     , **lowercase_				)





							def 			a__ ( self  :   Any     , _UpperCAmelCase  :   np.ndarray     , _UpperCAmelCase  :   Union[float, List[float]]     , _UpperCAmelCase  :   Union[float, List[float]]     , _UpperCAmelCase  :   Optional[Union[str, ChannelDimension]] = None     , **_UpperCAmelCase  :   Dict     , )						->		Union[str, Any]:



											"""simple docstring"""


											return normalize(lowercase_     , mean=lowercase_     , std=lowercase_     , data_format=lowercase_     , **lowercase_				)





							def 			a__ ( self  :   Dict     , _UpperCAmelCase  :   ImageInput     , _UpperCAmelCase  :   bool = None     , _UpperCAmelCase  :   Dict[str, int] = None     , _UpperCAmelCase  :   PILImageResampling = None     , _UpperCAmelCase  :   bool = None     , _UpperCAmelCase  :   int = None     , _UpperCAmelCase  :   bool = None     , _UpperCAmelCase  :   float = None     , _UpperCAmelCase  :   bool = None     , _UpperCAmelCase  :   Optional[Union[float, List[float]]] = None     , _UpperCAmelCase  :   Optional[Union[float, List[float]]] = None     , _UpperCAmelCase  :   bool = None     , _UpperCAmelCase  :   Optional[Union[str, TensorType]] = None     , _UpperCAmelCase  :   Optional[ChannelDimension] = ChannelDimension.FIRST     , **_UpperCAmelCase  :   Union[str, Any]     , )						->		Tuple:



											"""simple docstring"""


											__lowercase								=					do_resize if do_resize is not None else self.do_resize
											__lowercase								=					size if size is not None else self.size
											__lowercase								=					get_size_dict(lowercase_     , param_name='size'     , default_to_square=lowercase_				)
											__lowercase								=					resample if resample is not None else self.resample
											__lowercase								=					do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowercase								=					crop_size if crop_size is not None else self.crop_size
											__lowercase								=					get_size_dict(lowercase_     , param_name='crop_size'     , default_to_square=lowercase_				)
											__lowercase								=					do_rescale if do_rescale is not None else self.do_rescale
											__lowercase								=					rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowercase								=					do_normalize if do_normalize is not None else self.do_normalize
											__lowercase								=					image_mean if image_mean is not None else self.image_mean
											__lowercase								=					image_std if image_std is not None else self.image_std
											__lowercase								=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											__lowercase								=					make_list_of_images(lowercase_				)

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

											if do_resize and size is None:
															raise ValueError('Size 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.'				)

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
															__lowercase								=					[convert_to_rgb(lowercase_				) for image in images]

											# All transformations expect numpy arrays.
											__lowercase								=					[to_numpy_array(lowercase_				) for image in images]

											if do_resize:
															__lowercase								=					[self.resize(image=lowercase_     , size=lowercase_     , resample=lowercase_				) for image in images]

											if do_center_crop:
															__lowercase								=					[self.center_crop(image=lowercase_     , size=lowercase_				) for image in images]

											if do_rescale:
															__lowercase								=					[self.rescale(image=lowercase_     , scale=lowercase_				) for image in images]

											if do_normalize:
															__lowercase								=					[self.normalize(image=lowercase_     , mean=lowercase_     , std=lowercase_				) for image in images]

											__lowercase								=					[to_channel_dimension_format(lowercase_     , lowercase_				) for image in images]

											__lowercase								=					{'''pixel_values''': images}
											return BatchFeature(data=lowercase_     , tensor_type=lowercase_				)













"""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,
)


