






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


a	: Any      =				logging.get_logger(__name__)
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				int							 =			['pixel_values']
      def __init__(   self      :    str	,   __lowercase      :    bool = True	,   __lowercase      :    Dict[str, int] = None	,   __lowercase      :    bool = True	,   __lowercase      :    Dict[str, int] = None	,   __lowercase      :    PILImageResampling = PILImageResampling.BICUBIC	,   __lowercase      :    bool = True	,   __lowercase      :    Union[int, float] = 1 / 255	,   __lowercase      :    bool = True	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   **__lowercase      :    Union[str, Any]	,   )							->   None:
        super().__init__(**__lowercase      )
        __UpperCAmelCase     =						crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
        __UpperCAmelCase     =						get_size_dict(__lowercase	,   param_name="""crop_size"""      )
        __UpperCAmelCase     =						size if size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase     =						get_size_dict(__lowercase      )

        __UpperCAmelCase     =						do_center_crop
        __UpperCAmelCase     =						crop_size
        __UpperCAmelCase     =						do_resize
        __UpperCAmelCase     =						size
        __UpperCAmelCase     =						resample
        __UpperCAmelCase     =						do_rescale
        __UpperCAmelCase     =						rescale_factor
        __UpperCAmelCase     =						do_normalize
        __UpperCAmelCase     =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __UpperCAmelCase     =						image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 			UpperCAmelCase   (   self      :    Optional[int]	,   __lowercase      :    np.ndarray	,   __lowercase      :    Dict[str, int]	,   __lowercase      :    Optional[int] = None	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    Tuple	,   )							->   np.ndarray:
        __UpperCAmelCase     =						self.size if size is None else size
        __UpperCAmelCase     =						get_size_dict(__lowercase      )
        __UpperCAmelCase     =						get_size_dict(__lowercase	,   param_name="""crop_size"""      )

        __UpperCAmelCase     =						get_image_size(__lowercase      )
        __UpperCAmelCase     =						min(__lowercase	,   __lowercase      )
        __UpperCAmelCase     =						(size["""height"""] / crop_size["""height"""]) * min_dim
        __UpperCAmelCase     =						(size["""width"""] / crop_size["""width"""]) * min_dim
        return center_crop(__lowercase	,   size=(cropped_height, cropped_width)	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    np.ndarray	,   __lowercase      :    Dict[str, int]	,   __lowercase      :    PILImageResampling = PIL.Image.BICUBIC	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    str	,   )							->   np.ndarray:
        __UpperCAmelCase     =						get_size_dict(__lowercase      )
        if "height" not in size or "width" not in size:
          raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""      )
        return resize(
            __lowercase	,   size=(size["""height"""], size["""width"""])	,   resample=__lowercase	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    np.ndarray	,   __lowercase      :    Union[int, float]	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    List[str]	,   )							->   Any:
        return rescale(__lowercase	,   scale=__lowercase	,   data_format=__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    np.ndarray	,   __lowercase      :    Union[float, List[float]]	,   __lowercase      :    Union[float, List[float]]	,   __lowercase      :    Optional[Union[str, ChannelDimension]] = None	,   **__lowercase      :    Tuple	,   )							->   np.ndarray:
        return normalize(__lowercase	,   mean=__lowercase	,   std=__lowercase	,   data_format=__lowercase	,   **__lowercase      )




      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    ImageInput	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[Dict[str, int]] = None	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[Dict[str, int]] = None	,   __lowercase      :    PILImageResampling = None	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[float] = None	,   __lowercase      :    Optional[bool] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[float, List[float]]] = None	,   __lowercase      :    Optional[Union[str, TensorType]] = None	,   __lowercase      :    ChannelDimension = ChannelDimension.FIRST	,   **__lowercase      :    Union[str, Any]	,   )							->   PIL.Image.Image:
        __UpperCAmelCase     =						do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase     =						crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase     =						get_size_dict(__lowercase	,   param_name="""crop_size"""      )
        __UpperCAmelCase     =						do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase     =						size if size is not None else self.size
        __UpperCAmelCase     =						get_size_dict(__lowercase      )
        __UpperCAmelCase     =						resample if resample is not None else self.resample
        __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     =						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_center_crop and crop_size is None:
          raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""      )

        if do_resize and size is None:
          raise ValueError("""Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."""      )

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

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

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

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

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

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

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


