




'''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 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  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    __lowercase						(  __magic_name__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**UpperCamelCase      )
     __a			=					size if size is not None else {'shortest_edge': 224}
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     __a			=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
     __a			=					get_size_dict(UpperCamelCase      ,							param_name='crop_size'      )

     __a			=					do_resize
     __a			=					size
     __a			=					do_center_crop
     __a			=					crop_size
     __a			=					resample
     __a			=					do_rescale
     __a			=					rescale_factor
     __a			=					do_normalize
     __a			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a			=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     if "shortest_edge" in size:
       __a			=					get_resize_output_image_size(UpperCamelCase      ,							size['shortest_edge']      ,							default_to_square=UpperCamelCase      )
     elif "height" in size and "width" in size:
       __a			=					(size['height'], size['width'])
     else:
       raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"      )
     return resize(UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      )
     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(UpperCamelCase      ,							size=(size['height'], size['width'])      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Optional[Any]:
     return rescale(UpperCamelCase      ,							scale=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     return normalize(UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							)				->    np.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.
     __a			=					to_numpy_array(UpperCamelCase      )

     if do_resize:
       __a			=					self.resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      )

     if do_center_crop:
       __a			=					self.center_crop(UpperCamelCase      ,							size=UpperCamelCase      )

     if do_rescale:
       __a			=					self.rescale(image=UpperCamelCase      ,							scale=UpperCamelCase      )

     if do_normalize:
       __a			=					self.normalize(image=UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      )

     __a			=					to_channel_dimension_format(UpperCamelCase      ,							UpperCamelCase      )
     return image







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							**UpperCamelCase      ,							)				->    PIL.Image.Image:
     __a			=					do_resize if do_resize is not None else self.do_resize
     __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(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     __a			=					crop_size if crop_size is not None else self.crop_size
     __a			=					get_size_dict(UpperCamelCase      ,							param_name='crop_size'      )

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

     __a			=					make_batched(UpperCamelCase      )

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

     __a			=					{'pixel_values': videos}
     return BatchFeature(data=UpperCamelCase      ,							tensor_type=UpperCamelCase      )









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ 		=      {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      ["RoFormerTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoFormerForCausalLM",
      "RoFormerForMaskedLM",
      "RoFormerForMultipleChoice",
      "RoFormerForQuestionAnswering",
      "RoFormerForSequenceClassification",
      "RoFormerForTokenClassification",
      "RoFormerLayer",
      "RoFormerModel",
      "RoFormerPreTrainedModel",
      "load_tf_weights_in_roformer",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRoFormerForCausalLM",
      "TFRoFormerForMaskedLM",
      "TFRoFormerForMultipleChoice",
      "TFRoFormerForQuestionAnswering",
      "TFRoFormerForSequenceClassification",
      "TFRoFormerForTokenClassification",
      "TFRoFormerLayer",
      "TFRoFormerModel",
      "TFRoFormerPreTrainedModel",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlaxRoFormerForMaskedLM",
      "FlaxRoFormerForMultipleChoice",
      "FlaxRoFormerForQuestionAnswering",
      "FlaxRoFormerForSequenceClassification",
      "FlaxRoFormerForTokenClassification",
      "FlaxRoFormerModel",
      "FlaxRoFormerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

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



