

from typing import TYPE_CHECKING

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


UpperCamelCase__	:   str								=     {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   Union[str, Any]								=     [
           "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ViTHybridForImageClassification",
           "ViTHybridModel",
           "ViTHybridPreTrainedModel",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   Tuple								=     ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
       from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit_hybrid import (
                  VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTHybridForImageClassification,
                  ViTHybridModel,
                  ViTHybridPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
       import sys

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





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 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


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						Tuple    			=	['pixel_values']

  def __init__(       self    ,		_A = True    ,		_A = None    ,		_A = PILImageResampling.BILINEAR    ,		_A = True    ,		_A = 1 / 255    ,		_A = True    ,		_A = None    ,		_A = None    ,		**_A    ,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_						=    size if size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      SCREAMING_SNAKE_CASE_						=    do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize
      SCREAMING_SNAKE_CASE_						=    size
      SCREAMING_SNAKE_CASE_						=    resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else IMAGENET_STANDARD_STD

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = PILImageResampling.BILINEAR    ,		_A = None    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      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(
          _A    ,		size=(size['height'], size['width'])    ,		resample=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = None    ,		**_A):
      return rescale(_A    ,		scale=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A    ,		_A = None    ,		**_A    ,		):
      return normalize(_A    ,		mean=_A    ,		std=_A    ,		data_format=_A    ,		**_A)


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = ChannelDimension.FIRST    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize if do_normalize is not None else self.do_normalize
      SCREAMING_SNAKE_CASE_						=    resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else self.image_mean
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else self.image_std

      SCREAMING_SNAKE_CASE_						=    size if size is not None else self.size
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)

      SCREAMING_SNAKE_CASE_						=    make_list_of_images(_A)

      if not valid_images(_A):
          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_rescale and rescale_factor is None:
          raise ValueError('Rescale factor must be specified if do_rescale is True.')

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

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

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

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

      SCREAMING_SNAKE_CASE_						=    [to_channel_dimension_format(_A    ,		_A) for image in images]

      SCREAMING_SNAKE_CASE_						=    {'pixel_values': images}
      return BatchFeature(data=_A    ,		tensor_type=_A)


