



"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowerCAmelCase    : str							=							logging.get_logger(__name__)


if is_vision_available():
 import PIL






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Any								=			['pixel_values']



      def __init__(					self					:     str					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     bool = True					,     A					:     Union[int, float] = 1 / 2_5_5					,     A					:     bool = True					,     A					:     Optional[Union[float, List[float]]] = None					,     A					:     Optional[Union[float, List[float]]] = None					,     A					:     bool = True					,     **A					:     List[str]					,     ):
           super().__init__(**A						)
           _UpperCAmelCase      :     Optional[int]        =   size if size is not None else {"shortest_edge": 2_2_4}
           _UpperCAmelCase      :     Dict        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     int        =   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase      :     Optional[int]        =   get_size_dict(A					,     default_to_square=A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     List[str]        =   do_resize
           _UpperCAmelCase      :     List[Any]        =   size
           _UpperCAmelCase      :     str        =   resample
           _UpperCAmelCase      :     List[Any]        =   do_center_crop
           _UpperCAmelCase      :     int        =   crop_size
           _UpperCAmelCase      :     int        =   do_rescale
           _UpperCAmelCase      :     Tuple        =   rescale_factor
           _UpperCAmelCase      :     int        =   do_normalize
           _UpperCAmelCase      :     int        =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           _UpperCAmelCase      :     Any        =   image_std if image_std is not None else OPENAI_CLIP_STD
           _UpperCAmelCase      :     Union[str, Any]        =   do_convert_rgb



      def 						snake_case_ (					self					:     int					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Dict					,     ):
           _UpperCAmelCase      :     int        =   get_size_dict(A					,     default_to_square=A						)
           if "shortest_edge" not in size:
                raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'						)
           _UpperCAmelCase      :     int        =   get_resize_output_image_size(A					,     size=size["shortest_edge"]					,     default_to_square=A						)
           return resize(A					,     size=A					,     resample=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     str					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Any					,     ):
           _UpperCAmelCase      :     int        =   get_size_dict(A						)
           if "height" not in size or "width" not in size:
                raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'						)
           return center_crop(A					,     size=(size["height"], size["width"])					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     np.ndarray					,     A					:     Union[int, float]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     str					,     ):
           return rescale(A					,     scale=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Dict					,     A					:     np.ndarray					,     A					:     Union[float, List[float]]					,     A					:     Union[float, List[float]]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Dict					,     ):
           return normalize(A					,     mean=A					,     std=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     ImageInput					,     A					:     bool = None					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = None					,     A					:     bool = None					,     A					:     int = None					,     A					:     bool = None					,     A					:     float = None					,     A					:     bool = None					,     A					:     Optional[Union[float, List[float]]] = None					,     A					:     Optional[Union[float, List[float]]] = None					,     A					:     bool = None					,     A					:     Optional[Union[str, TensorType]] = None					,     A					:     Optional[ChannelDimension] = ChannelDimension.FIRST					,     **A					:     Optional[Any]					,     ):
           _UpperCAmelCase      :     Union[str, Any]        =   do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase      :     int        =   size if size is not None else self.size
           _UpperCAmelCase      :     List[Any]        =   get_size_dict(A					,     param_name="size"					,     default_to_square=A						)
           _UpperCAmelCase      :     str        =   resample if resample is not None else self.resample
           _UpperCAmelCase      :     int        =   do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase      :     Optional[Any]        =   crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase      :     Any        =   get_size_dict(A					,     param_name="crop_size"					,     default_to_square=A						)
           _UpperCAmelCase      :     Optional[int]        =   do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase      :     List[Any]        =   rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase      :     Union[str, Any]        =   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      :     List[str]        =   image_std if image_std is not None else self.image_std
           _UpperCAmelCase      :     int        =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           _UpperCAmelCase      :     Tuple        =   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:
                raise ValueError("Size 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."						)

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                _UpperCAmelCase      :     Union[str, Any]        =   [convert_to_rgb(A						) for image in images]

           # All transformations expect numpy arrays.
           _UpperCAmelCase      :     Union[str, Any]        =   [to_numpy_array(A						) for image in images]

           if do_resize:
                _UpperCAmelCase      :     Any        =   [self.resize(image=A					,     size=A					,     resample=A						) for image in images]

           if do_center_crop:
                _UpperCAmelCase      :     List[str]        =   [self.center_crop(image=A					,     size=A						) for image in images]

           if do_rescale:
                _UpperCAmelCase      :     Dict        =   [self.rescale(image=A					,     scale=A						) for image in images]

           if do_normalize:
                _UpperCAmelCase      :     Dict        =   [self.normalize(image=A					,     mean=A					,     std=A						) for image in images]

           _UpperCAmelCase      :     int        =   [to_channel_dimension_format(A					,     A						) for image in images]

           _UpperCAmelCase      :     Union[str, Any]        =   {"pixel_values": images}
           return BatchFeature(data=A					,     tensor_type=A						)





"""simple docstring"""




from typing import TYPE_CHECKING

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


_lowerCAmelCase    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "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:
 _lowerCAmelCase    : Optional[int]							=							[
     "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:
 _lowerCAmelCase    : List[str]							=							[
     "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

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