


'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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_					:    str          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""pixel_values"""]
 def __init__( self      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = 0.9      ,				__magic_name__ = PILImageResampling.BICUBIC      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = 1 / 2_55      ,				__magic_name__ = True      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							None:
      super().__init__(**__magic_name__						)
      _a			      =							size if size is not None else {'shortest_edge': 2_24}
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      _a			      =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
      _a			      =							get_size_dict(__magic_name__      ,				param_name='crop_size'						)

      _a			      =							do_resize
      _a			      =							size
      _a			      =							crop_pct
      _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_DEFAULT_MEAN
      _a			      =							image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = PILImageResampling.BICUBIC      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      if "shortest_edge" not in size and ("height" not in size or "width" not in size):
           raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
      if crop_pct is not None:
           if "shortest_edge" in size:
                _a			      =							int(size['shortest_edge'] / crop_pct						)
           elif "height" in size and "width" in size:
                if size["height"] == size["width"]:
                     _a			      =							int(size['height'] / crop_pct						)
                else:
                     _a			      =							(int(size['height'] / crop_pct						), int(size['width'] / crop_pct						))
           else:
                raise ValueError('Invalid size for resize: {}'.format(__magic_name__						)						)

           _a			      =							get_resize_output_image_size(__magic_name__      ,				size=__magic_name__      ,				default_to_square=__magic_name__						)
      else:
           if "shortest_edge" in size:
                _a			      =							get_resize_output_image_size(__magic_name__      ,				size=size['shortest_edge']      ,				default_to_square=__magic_name__						)
           elif "height" in size and "width" in size:
                _a			      =							(size['height'], size['width'])
           else:
                raise ValueError('Invalid size for resize: {}'.format(__magic_name__						)						)

      return resize(__magic_name__      ,				size=__magic_name__      ,				resample=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      _a			      =							get_size_dict(__magic_name__						)
      if "height" not in size or "width" not in size:
           raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'						)
      return center_crop(__magic_name__      ,				size=(size['height'], size['width'])      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							int:
      return rescale(__magic_name__      ,				scale=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							np.ndarray:
      return normalize(__magic_name__      ,				mean=__magic_name__      ,				std=__magic_name__      ,				data_format=__magic_name__      ,				**__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = ChannelDimension.FIRST      ,				**__magic_name__      ,				)					->							PIL.Image.Image:
      _a			      =							do_resize if do_resize is not None else self.do_resize
      _a			      =							crop_pct if crop_pct is not None else self.crop_pct
      _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			      =							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			      =							size if size is not None else self.size
      _a			      =							get_size_dict(__magic_name__      ,				default_to_square=__magic_name__						)
      _a			      =							crop_size if crop_size is not None else self.crop_size
      _a			      =							get_size_dict(__magic_name__      ,				param_name='crop_size'						)

      _a			      =							make_list_of_images(__magic_name__						)

      if not valid_images(__magic_name__						):
           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_pct is None:
           raise ValueError('Crop_pct 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(__magic_name__						) for image in images]

      if do_resize:
           _a			      =							[self.resize(image=__magic_name__      ,				size=__magic_name__      ,				crop_pct=__magic_name__      ,				resample=__magic_name__						) for image in images]

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

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

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

      _a			      =							[to_channel_dimension_format(__magic_name__      ,				__magic_name__						) for image in images]

      _a			      =							{'pixel_values': images}
      return BatchFeature(data=__magic_name__      ,				tensor_type=__magic_name__						)





'''simple docstring'''






from typing import TYPE_CHECKING

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


a_					:    str          =    {
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecAudioForAudioFrameClassification",
     "Data2VecAudioForCTC",
     "Data2VecAudioForSequenceClassification",
     "Data2VecAudioForXVector",
     "Data2VecAudioModel",
     "Data2VecAudioPreTrainedModel",
 ]
 a_					:    str          =    [
     "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecTextForCausalLM",
     "Data2VecTextForMaskedLM",
     "Data2VecTextForMultipleChoice",
     "Data2VecTextForQuestionAnswering",
     "Data2VecTextForSequenceClassification",
     "Data2VecTextForTokenClassification",
     "Data2VecTextModel",
     "Data2VecTextPreTrainedModel",
 ]
 a_					:    Tuple          =    [
     "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecVisionForImageClassification",
     "Data2VecVisionForMaskedImageModeling",
     "Data2VecVisionForSemanticSegmentation",
     "Data2VecVisionModel",
     "Data2VecVisionPreTrainedModel",
 ]

if is_tf_available():
 a_					:    Dict          =    [
     "TFData2VecVisionForImageClassification",
     "TFData2VecVisionForSemanticSegmentation",
     "TFData2VecVisionModel",
     "TFData2VecVisionPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

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

