

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a  :Optional[Any]										=       logging.get_logger(__name__)

class    _a (       _a							):




      """simple docstring"""

      _lowerCamelCase    :       Tuple							=    ["""pixel_values"""]





      def __init__(					self				:							Optional[Any]		,       UpperCAmelCase				:							bool = True		,       UpperCAmelCase				:							Optional[Dict[str, int]] = None		,       UpperCAmelCase				:							PILImageResampling = PILImageResampling.BILINEAR		,       UpperCAmelCase				:							bool = True		,       UpperCAmelCase				:							Dict[str, int] = None		,       UpperCAmelCase				:							bool = True		,       UpperCAmelCase				:							Union[int, float] = 1 / 255		,       UpperCAmelCase				:							bool = True		,       UpperCAmelCase				:							Optional[Union[float, List[float]]] = None		,       UpperCAmelCase				:							Optional[Union[float, List[float]]] = None		,       **UpperCAmelCase				:							Union[str, Any]		,       ):
            super().__init__(**__UpperCAmelCase     )
            A_		   =							size if size is not None else {"shortest_edge": 256}
            A_		   =							get_size_dict(__UpperCAmelCase		,       default_to_square=__UpperCAmelCase     )
            A_		   =							crop_size if crop_size is not None else {"height": 224, "width": 224}
            A_		   =							get_size_dict(__UpperCAmelCase     )
            A_		   =							do_resize
            A_		   =							size
            A_		   =							resample
            A_		   =							do_center_crop
            A_		   =							crop_size
            A_		   =							do_rescale
            A_		   =							rescale_factor
            A_		   =							do_normalize
            A_		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            A_		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





      def 						__A     (					self				:							Dict		,       UpperCAmelCase				:							np.ndarray		,       UpperCAmelCase				:							Dict[str, int]		,       UpperCAmelCase				:							PILImageResampling = PILImageResampling.BICUBIC		,       UpperCAmelCase				:							Optional[Union[str, ChannelDimension]] = None		,       **UpperCAmelCase				:							List[str]		,       ):
            A_		   =							get_size_dict(__UpperCAmelCase		,       default_to_square=__UpperCAmelCase     )
            if "shortest_edge" not in size:
                  raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
            A_		   =							get_resize_output_image_size(__UpperCAmelCase		,       size=size["shortest_edge"]		,       default_to_square=__UpperCAmelCase     )
            return resize(__UpperCAmelCase		,       size=__UpperCAmelCase		,       resample=__UpperCAmelCase		,       data_format=__UpperCAmelCase		,       **__UpperCAmelCase     )





      def 						__A     (					self				:							Dict		,       UpperCAmelCase				:							np.ndarray		,       UpperCAmelCase				:							Dict[str, int]		,       UpperCAmelCase				:							Optional[Union[str, ChannelDimension]] = None		,       **UpperCAmelCase				:							Union[str, Any]		,       ):
            A_		   =							get_size_dict(__UpperCAmelCase     )
            return center_crop(__UpperCAmelCase		,       size=(size["height"], size["width"])		,       data_format=__UpperCAmelCase		,       **__UpperCAmelCase     )





      def 						__A     (					self				:							Union[str, Any]		,       UpperCAmelCase				:							np.ndarray		,       UpperCAmelCase				:							float		,       UpperCAmelCase				:							Optional[Union[str, ChannelDimension]] = None		,       **UpperCAmelCase				:							List[str]     ):
            return rescale(__UpperCAmelCase		,       scale=__UpperCAmelCase		,       data_format=__UpperCAmelCase		,       **__UpperCAmelCase     )





      def 						__A     (					self				:							Any		,       UpperCAmelCase				:							np.ndarray		,       UpperCAmelCase				:							Union[float, List[float]]		,       UpperCAmelCase				:							Union[float, List[float]]		,       UpperCAmelCase				:							Optional[Union[str, ChannelDimension]] = None		,       **UpperCAmelCase				:							Optional[Any]		,       ):
            return normalize(__UpperCAmelCase		,       mean=__UpperCAmelCase		,       std=__UpperCAmelCase		,       data_format=__UpperCAmelCase		,       **__UpperCAmelCase     )







      def 						__A     (					self				:							List[Any]		,       UpperCAmelCase				:							ImageInput		,       UpperCAmelCase				:							Optional[bool] = None		,       UpperCAmelCase				:							Dict[str, int] = None		,       UpperCAmelCase				:							PILImageResampling = None		,       UpperCAmelCase				:							bool = None		,       UpperCAmelCase				:							Dict[str, int] = None		,       UpperCAmelCase				:							Optional[bool] = None		,       UpperCAmelCase				:							Optional[float] = None		,       UpperCAmelCase				:							Optional[bool] = None		,       UpperCAmelCase				:							Optional[Union[float, List[float]]] = None		,       UpperCAmelCase				:							Optional[Union[float, List[float]]] = None		,       UpperCAmelCase				:							Optional[Union[str, TensorType]] = None		,       UpperCAmelCase				:							Union[str, ChannelDimension] = ChannelDimension.FIRST		,       **UpperCAmelCase				:							List[str]		,       ):
            A_		   =							do_resize if do_resize is not None else self.do_resize
            A_		   =							size if size is not None else self.size
            A_		   =							get_size_dict(__UpperCAmelCase		,       default_to_square=__UpperCAmelCase     )
            A_		   =							resample if resample is not None else self.resample
            A_		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
            A_		   =							crop_size if crop_size is not None else self.crop_size
            A_		   =							get_size_dict(__UpperCAmelCase     )
            A_		   =							do_rescale if do_rescale is not None else self.do_rescale
            A_		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
            A_		   =							do_normalize if do_normalize is not None else self.do_normalize
            A_		   =							image_mean if image_mean is not None else self.image_mean
            A_		   =							image_std if image_std is not None else self.image_std

            A_		   =							make_list_of_images(__UpperCAmelCase     )

            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."     )

            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."     )

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

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

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

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

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

            A_		   =							[to_channel_dimension_format(__UpperCAmelCase		,       __UpperCAmelCase     ) for image in images]

            A_		   =							{"pixel_values": images}
            return BatchFeature(data=__UpperCAmelCase		,       tensor_type=__UpperCAmelCase     )



"""simple docstring"""







__lowercase    					=      {
    """Pillow""": """Pillow<10.0.0""",
    """accelerate""": """accelerate>=0.20.3""",
    """av""": """av==9.2.0""",
    """beautifulsoup4""": """beautifulsoup4""",
    """black""": """black~=23.1""",
    """codecarbon""": """codecarbon==1.2.0""",
    """cookiecutter""": """cookiecutter==1.7.3""",
    """dataclasses""": """dataclasses""",
    """datasets""": """datasets!=2.5.0""",
    """decord""": """decord==0.6.0""",
    """deepspeed""": """deepspeed>=0.9.3""",
    """diffusers""": """diffusers""",
    """dill""": """dill<0.3.5""",
    """evaluate""": """evaluate>=0.2.0""",
    """fairscale""": """fairscale>0.3""",
    """faiss-cpu""": """faiss-cpu""",
    """fastapi""": """fastapi""",
    """filelock""": """filelock""",
    """flax""": """flax>=0.4.1,<=0.7.0""",
    """ftfy""": """ftfy""",
    """fugashi""": """fugashi>=1.0""",
    """GitPython""": """GitPython<3.1.19""",
    """hf-doc-builder""": """hf-doc-builder>=0.3.0""",
    """huggingface-hub""": """huggingface-hub>=0.14.1,<1.0""",
    """importlib_metadata""": """importlib_metadata""",
    """ipadic""": """ipadic>=1.0.0,<2.0""",
    """isort""": """isort>=5.5.4""",
    """jax""": """jax>=0.2.8,!=0.3.2,<=0.4.13""",
    """jaxlib""": """jaxlib>=0.1.65,<=0.4.13""",
    """jieba""": """jieba""",
    """kenlm""": """kenlm""",
    """keras-nlp""": """keras-nlp>=0.3.1""",
    """librosa""": """librosa""",
    """nltk""": """nltk""",
    """natten""": """natten>=0.14.6""",
    """numpy""": """numpy>=1.17""",
    """onnxconverter-common""": """onnxconverter-common""",
    """onnxruntime-tools""": """onnxruntime-tools>=1.4.2""",
    """onnxruntime""": """onnxruntime>=1.4.0""",
    """opencv-python""": """opencv-python""",
    """optuna""": """optuna""",
    """optax""": """optax>=0.0.8,<=0.1.4""",
    """packaging""": """packaging>=20.0""",
    """parameterized""": """parameterized""",
    """phonemizer""": """phonemizer""",
    """protobuf""": """protobuf""",
    """psutil""": """psutil""",
    """pyyaml""": """pyyaml>=5.1""",
    """pydantic""": """pydantic<2""",
    """pytest""": """pytest>=7.2.0""",
    """pytest-timeout""": """pytest-timeout""",
    """pytest-xdist""": """pytest-xdist""",
    """python""": """python>=3.8.0""",
    """ray[tune]""": """ray[tune]""",
    """regex""": """regex!=2019.12.17""",
    """requests""": """requests""",
    """rhoknp""": """rhoknp>=1.1.0,<1.3.1""",
    """rjieba""": """rjieba""",
    """rouge-score""": """rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1""",
    """ruff""": """ruff>=0.0.241,<=0.0.259""",
    """sacrebleu""": """sacrebleu>=1.4.12,<2.0.0""",
    """sacremoses""": """sacremoses""",
    """safetensors""": """safetensors>=0.3.1""",
    """sagemaker""": """sagemaker>=2.31.0""",
    """scikit-learn""": """scikit-learn""",
    """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
    """sigopt""": """sigopt""",
    """starlette""": """starlette""",
    """sudachipy""": """sudachipy>=0.6.6""",
    """sudachidict_core""": """sudachidict_core>=20220729""",
    """tensorflow-cpu""": """tensorflow-cpu>=2.6,<2.14""",
    """tensorflow""": """tensorflow>=2.6,<2.14""",
    """tensorflow-text""": """tensorflow-text<2.14""",
    """tf2onnx""": """tf2onnx""",
    """timeout-decorator""": """timeout-decorator""",
    """timm""": """timm""",
    """tokenizers""": """tokenizers>=0.11.1,!=0.11.3,<0.14""",
    """torch""": """torch>=1.9,!=1.12.0""",
    """torchaudio""": """torchaudio""",
    """torchvision""": """torchvision""",
    """pyctcdecode""": """pyctcdecode>=0.4.0""",
    """tqdm""": """tqdm>=4.27""",
    """unidic""": """unidic>=1.0.2""",
    """unidic_lite""": """unidic_lite>=1.0.7""",
    """urllib3""": """urllib3<2.0.0""",
    """uvicorn""": """uvicorn""",
}






