






'''simple docstring'''






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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


__A     						= logging.get_logger(__name__)




class a_     (     UpperCamelCase_				):
   _snake_case	   =				["""pixel_values"""]







   def __init__(self       , __a = True       , __a = None       , __a = PIL.Image.NEAREST       , __a = False       , __a = None       , __a = 1 / 2_5_5       , __a = False       , __a = True       , __a = True       , __a = None       , __a = None       , __a = True       , **__a       , )      ->   None:
      """simple docstring"""






      super().__init__(**__a)
      __snake_case					:   Optional[int]  				=			size if size is not None else {'height': 3_4_6, 'width': 3_4_6}
      __snake_case					:   List[str]  				=			get_size_dict(__a)
      __snake_case					:   Union[str, Any]  				=			crop_size if crop_size is not None else {'height': 2_8_9, 'width': 2_8_9}
      __snake_case					:   Tuple  				=			get_size_dict(__a       , param_name='crop_size')

      __snake_case					:   Optional[Any]  				=			do_resize
      __snake_case					:   Optional[Any]  				=			size
      __snake_case					:   str  				=			resample
      __snake_case					:   Optional[Any]  				=			do_center_crop
      __snake_case					:   Dict  				=			crop_size
      __snake_case					:   Any  				=			do_rescale
      __snake_case					:   Optional[Any]  				=			rescale_factor
      __snake_case					:   str  				=			rescale_offset
      __snake_case					:   int  				=			do_normalize
      __snake_case					:   Tuple  				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __snake_case					:   List[str]  				=			image_std if image_std is not None else IMAGENET_STANDARD_STD
      __snake_case					:   Any  				=			include_top







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a = PIL.Image.NEAREST       , __a = None       , **__a       , )      ->   np.ndarray:
      """simple docstring"""






      __snake_case					:   str  				=			get_size_dict(__a)
      if "height" not in size or "width" not in size:
         raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
      return resize(
          __a       , size=(size['height'], size['width'])       , resample=__a       , data_format=__a       , **__a)







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a = None       , **__a       , )      ->   np.ndarray:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			get_size_dict(__a)
      if "height" not in size or "width" not in size:
         raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
      return center_crop(__a       , size=(size['height'], size['width'])       , data_format=__a       , **__a)







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a = True       , __a = None       , **__a       , )      ->   Tuple:
      """simple docstring"""






      if offset:
         __snake_case					:   List[Any]  				=			(image - 127.5) * scale
         if data_format is not None:
            __snake_case					:   List[str]  				=			to_channel_dimension_format(__a       , __a)
         __snake_case					:   Any  				=			rescaled_image.astype(np.floataa)
      else:
         __snake_case					:   Any  				=			rescale(__a       , scale=__a       , data_format=__a       , **__a)
      return rescaled_image







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a = None       , **__a       , )      ->   np.ndarray:
      """simple docstring"""






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







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a = None       , __a = None       , __a=None       , __a = None       , __a = None       , __a = None       , __a = None       , __a = None       , __a = None       , __a = None       , __a = None       , __a = None       , __a = None       , __a = ChannelDimension.FIRST       , **__a       , )      ->   PIL.Image.Image:
      """simple docstring"""






      __snake_case					:   Any  				=			do_resize if do_resize is not None else self.do_resize
      __snake_case					:   str  				=			resample if resample is not None else self.resample
      __snake_case					:   Optional[int]  				=			do_center_crop if do_center_crop is not None else self.do_center_crop
      __snake_case					:   Any  				=			do_rescale if do_rescale is not None else self.do_rescale
      __snake_case					:   Tuple  				=			rescale_factor if rescale_factor is not None else self.rescale_factor
      __snake_case					:   Dict  				=			rescale_offset if rescale_offset is not None else self.rescale_offset
      __snake_case					:   Optional[int]  				=			do_normalize if do_normalize is not None else self.do_normalize
      __snake_case					:   Any  				=			image_mean if image_mean is not None else self.image_mean
      __snake_case					:   List[str]  				=			image_std if image_std is not None else self.image_std
      __snake_case					:   Dict  				=			include_top if include_top is not None else self.include_top

      __snake_case					:   Any  				=			size if size is not None else self.size
      __snake_case					:   Any  				=			get_size_dict(__a)
      __snake_case					:   Union[str, Any]  				=			crop_size if crop_size is not None else self.crop_size
      __snake_case					:   str  				=			get_size_dict(__a       , param_name='crop_size')

      __snake_case					:   List[str]  				=			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 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.
      __snake_case					:   Union[str, Any]  				=			[to_numpy_array(__a) for image in images]

      if do_resize:
         __snake_case					:   Optional[int]  				=			[self.resize(image=__a       , size=__a       , resample=__a) for image in images]

      if do_center_crop:
         __snake_case					:   str  				=			[self.center_crop(image=__a       , size=__a) for image in images]

      if do_rescale:
         __snake_case					:   Union[str, Any]  				=			[self.rescale(image=__a       , scale=__a       , offset=__a) for image in images]

      if do_normalize:
         __snake_case					:   str  				=			[self.normalize(image=__a       , mean=__a       , std=__a) for image in images]

      if include_top:
         __snake_case					:   Optional[Any]  				=			[self.normalize(image=__a       , mean=[0, 0, 0]       , std=__a) for image in images]

      __snake_case					:   Union[str, Any]  				=			[to_channel_dimension_format(__a       , __a) for image in images]

      __snake_case					:   Optional[int]  				=			{'pixel_values': images}
      return BatchFeature(data=__a       , tensor_type=__a)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      __A     						= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)