






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


if is_vision_available():
						import PIL


UpperCamelCase							=logging.get_logger(__name__)



def 							snake_case					(     a_							: Optional[int]	)       ->				List[List[ImageInput]]:





						"""simple docstring"""

						if isinstance(a_    ,   (list, tuple)	) and isinstance(videos[0]    ,   (list, tuple)	) and is_valid_image(videos[0][0]	):
												return videos

						elif isinstance(a_    ,   (list, tuple)	) and is_valid_image(videos[0]	):
												return [videos]

						elif is_valid_image(a_	):
												return [[videos]]

						raise ValueError(f"Could not make batched video from {videos}"	)




class     A    (      SCREAMING_SNAKE_CASE__						):






	"""simple docstring"""





	__a			:						Optional[int]      =						['''pixel_values''']
	def __init__(							self			,     __lowerCAmelCase = True			,     __lowerCAmelCase = None			,     __lowerCAmelCase = PILImageResampling.BILINEAR			,     __lowerCAmelCase = True			,     __lowerCAmelCase = None			,     __lowerCAmelCase = True			,     __lowerCAmelCase = 1 / 2_55			,     __lowerCAmelCase = True			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
							super().__init__(**__lowerCAmelCase )
							UpperCamelCase_				:   List[Any]					    =							size if size is not None else {"""shortest_edge""": 2_24}
							UpperCamelCase_				:   Any					    =							get_size_dict(__lowerCAmelCase			,     default_to_square=__lowerCAmelCase )
							UpperCamelCase_				:   Dict					    =							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
							UpperCamelCase_				:   Optional[int]					    =							get_size_dict(__lowerCAmelCase			,     param_name="""crop_size""" )

							UpperCamelCase_				:   int					    =							do_resize
							UpperCamelCase_				:   Dict					    =							size
							UpperCamelCase_				:   Dict					    =							do_center_crop
							UpperCamelCase_				:   Optional[Any]					    =							crop_size
							UpperCamelCase_				:   Union[str, Any]					    =							resample
							UpperCamelCase_				:   Dict					    =							do_rescale
							UpperCamelCase_				:   str					    =							rescale_factor
							UpperCamelCase_				:   Dict					    =							do_normalize
							UpperCamelCase_				:   Optional[Any]					    =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase_				:   Optional[Any]					    =							image_std if image_std is not None else IMAGENET_STANDARD_STD
	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = PILImageResampling.BILINEAR			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
							UpperCamelCase_				:   Union[str, Any]					    =							get_size_dict(__lowerCAmelCase			,     default_to_square=__lowerCAmelCase )
							if "shortest_edge" in size:
													UpperCamelCase_				:   int					    =							get_resize_output_image_size(__lowerCAmelCase			,     size["""shortest_edge"""]			,     default_to_square=__lowerCAmelCase )
							elif "height" in size and "width" in size:
													UpperCamelCase_				:   int					    =							(size["""height"""], size["""width"""])
							else:
													raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
							return resize(__lowerCAmelCase			,     size=__lowerCAmelCase			,     resample=__lowerCAmelCase			,     data_format=__lowerCAmelCase			,     **__lowerCAmelCase )
	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
							UpperCamelCase_				:   Any					    =							get_size_dict(__lowerCAmelCase )
							if "height" not in size or "width" not in size:
													raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
							return center_crop(__lowerCAmelCase			,     size=(size["""height"""], size["""width"""])			,     data_format=__lowerCAmelCase			,     **__lowerCAmelCase )
	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
							return rescale(__lowerCAmelCase			,     scale=__lowerCAmelCase			,     data_format=__lowerCAmelCase			,     **__lowerCAmelCase )
	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
							return normalize(__lowerCAmelCase			,     mean=__lowerCAmelCase			,     std=__lowerCAmelCase			,     data_format=__lowerCAmelCase			,     **__lowerCAmelCase )
	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = ChannelDimension.FIRST			,     ):
							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.
							UpperCamelCase_				:   Optional[int]					    =							to_numpy_array(__lowerCAmelCase )

							if do_resize:
													UpperCamelCase_				:   Optional[int]					    =							self.resize(image=__lowerCAmelCase			,     size=__lowerCAmelCase			,     resample=__lowerCAmelCase )

							if do_center_crop:
													UpperCamelCase_				:   List[Any]					    =							self.center_crop(__lowerCAmelCase			,     size=__lowerCAmelCase )

							if do_rescale:
													UpperCamelCase_				:   List[str]					    =							self.rescale(image=__lowerCAmelCase			,     scale=__lowerCAmelCase )

							if do_normalize:
													UpperCamelCase_				:   Tuple					    =							self.normalize(image=__lowerCAmelCase			,     mean=__lowerCAmelCase			,     std=__lowerCAmelCase )

							UpperCamelCase_				:   str					    =							to_channel_dimension_format(__lowerCAmelCase			,     __lowerCAmelCase )
							return image





	def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = ChannelDimension.FIRST			,     **__lowerCAmelCase			,     ):
							UpperCamelCase_				:   int					    =							do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_				:   int					    =							resample if resample is not None else self.resample
							UpperCamelCase_				:   Optional[Any]					    =							do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase_				:   str					    =							do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase_				:   int					    =							rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase_				:   int					    =							do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_				:   Tuple					    =							image_mean if image_mean is not None else self.image_mean
							UpperCamelCase_				:   Tuple					    =							image_std if image_std is not None else self.image_std

							UpperCamelCase_				:   int					    =							size if size is not None else self.size
							UpperCamelCase_				:   int					    =							get_size_dict(__lowerCAmelCase			,     default_to_square=__lowerCAmelCase )
							UpperCamelCase_				:   str					    =							crop_size if crop_size is not None else self.crop_size
							UpperCamelCase_				:   Any					    =							get_size_dict(__lowerCAmelCase			,     param_name="""crop_size""" )

							if not valid_images(__lowerCAmelCase ):
													raise ValueError(
													    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
													    """torch.Tensor, tf.Tensor or jax.ndarray.""" )

							UpperCamelCase_				:   str					    =							make_batched(__lowerCAmelCase )

							UpperCamelCase_				:   Dict					    =							[
							    [
							        self._preprocess_image(
							            image=__lowerCAmelCase			,     do_resize=__lowerCAmelCase			,     size=__lowerCAmelCase			,     resample=__lowerCAmelCase			,     do_center_crop=__lowerCAmelCase			,     crop_size=__lowerCAmelCase			,     do_rescale=__lowerCAmelCase			,     rescale_factor=__lowerCAmelCase			,     do_normalize=__lowerCAmelCase			,     image_mean=__lowerCAmelCase			,     image_std=__lowerCAmelCase			,     data_format=__lowerCAmelCase			,     )
							        for img in video
							    ]
							    for video in videos
							]

							UpperCamelCase_				:   Optional[int]					    =							{"""pixel_values""": videos}
							return BatchFeature(data=__lowerCAmelCase			,     tensor_type=__lowerCAmelCase )














'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase							={
    "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:
      UpperCamelCase							=[
          "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecAudioForAudioFrameClassification",
          "Data2VecAudioForCTC",
          "Data2VecAudioForSequenceClassification",
          "Data2VecAudioForXVector",
          "Data2VecAudioModel",
          "Data2VecAudioPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecTextForCausalLM",
          "Data2VecTextForMaskedLM",
          "Data2VecTextForMultipleChoice",
          "Data2VecTextForQuestionAnswering",
          "Data2VecTextForSequenceClassification",
          "Data2VecTextForTokenClassification",
          "Data2VecTextModel",
          "Data2VecTextPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecVisionForImageClassification",
          "Data2VecVisionForMaskedImageModeling",
          "Data2VecVisionForSemanticSegmentation",
          "Data2VecVisionModel",
          "Data2VecVisionPreTrainedModel",
      ]

if is_tf_available():
      UpperCamelCase							=[
          "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

      UpperCamelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






