



"""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      __magic_name__				(							lowercase ):
   if isinstance(lowercase							,			(list, tuple) ) and isinstance(videos[0]							,			(list, tuple) ) and is_valid_image(videos[0][0] ):
      return videos

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

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

   raise ValueError(f'''Could not make batched video from {videos}''' )
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				str     						=					['pixel_values']



  def __init__(						self :					List[str]       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = PILImageResampling.BILINEAR       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Union[int, float] = 1 / 255       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		**lowerCAmelCase :					List[Any]       ,		)     ->					None:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=size if size is not None else {"""shortest_edge""": 224}
     SCREAMING_SNAKE_CASE_: Tuple     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""		)

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



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					PILImageResampling = PILImageResampling.BILINEAR       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Union[str, Any]       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     if "shortest_edge" in size:
        SCREAMING_SNAKE_CASE_: str     			=get_resize_output_image_size(lowerCAmelCase       ,		size["""shortest_edge"""]       ,		default_to_square=lowerCAmelCase		)
     elif "height" in size and "width" in size:
        SCREAMING_SNAKE_CASE_: Optional[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 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Optional[int]       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[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 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Union[int, float]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					int       ,		)     ->					List[str]:


     '''simple docstring'''




     return rescale(lowerCAmelCase       ,		scale=lowerCAmelCase       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Union[float, List[float]]       ,		lowerCAmelCase :					Union[float, List[float]]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					int       ,		)     ->					np.ndarray:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					ImageInput       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					float = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		lowerCAmelCase :					Optional[ChannelDimension] = ChannelDimension.FIRST       ,		)     ->					np.ndarray:


     '''simple docstring'''




     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.
     SCREAMING_SNAKE_CASE_: Any     			=to_numpy_array(lowerCAmelCase		)

     if do_resize:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.resize(image=lowerCAmelCase       ,		size=lowerCAmelCase       ,		resample=lowerCAmelCase		)

     if do_center_crop:
        SCREAMING_SNAKE_CASE_: Tuple     			=self.center_crop(lowerCAmelCase       ,		size=lowerCAmelCase		)

     if do_rescale:
        SCREAMING_SNAKE_CASE_: Tuple     			=self.rescale(image=lowerCAmelCase       ,		scale=lowerCAmelCase		)

     if do_normalize:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.normalize(image=lowerCAmelCase       ,		mean=lowerCAmelCase       ,		std=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: str     			=to_channel_dimension_format(lowerCAmelCase       ,		lowerCAmelCase		)
     return image





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					ImageInput       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					float = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		lowerCAmelCase :					Optional[Union[str, TensorType]] = None       ,		lowerCAmelCase :					ChannelDimension = ChannelDimension.FIRST       ,		**lowerCAmelCase :					List[str]       ,		)     ->					PIL.Image.Image:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: Optional[int]     			=size if size is not None else self.size
     SCREAMING_SNAKE_CASE_: Dict     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE_: List[str]     			=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."""		)

     SCREAMING_SNAKE_CASE_: Tuple     			=make_batched(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=[
         [
             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
     ]

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={"""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_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMForMultipleChoice""",
       """XLMForQuestionAnswering""",
       """XLMForQuestionAnsweringSimple""",
       """XLMForSequenceClassification""",
       """XLMForTokenClassification""",
       """XLMModel""",
       """XLMPreTrainedModel""",
       """XLMWithLMHeadModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMForMultipleChoice""",
       """TFXLMForQuestionAnsweringSimple""",
       """TFXLMForSequenceClassification""",
       """TFXLMForTokenClassification""",
       """TFXLMMainLayer""",
       """TFXLMModel""",
       """TFXLMPreTrainedModel""",
       """TFXLMWithLMHeadModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

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



