






from typing import TYPE_CHECKING

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


__lowerCamelCase	:		str										=   {
    """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	:		int										=   ["""RoFormerTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase	:		List[str]										=   [
           """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	:		List[str]										=   [
           """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	:		Dict										=   [
           """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	:		Optional[int]										=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
        raise ValueError("""size_divisor is required for resizing"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

       # All transformations expect numpy arrays.
       snake_case__  :    str							       =[to_numpy_array(a   ) for img in images]

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

       if do_rescale:
        snake_case__  :    Union[str, Any]							       =[self.rescale(a				,			scale=1 / 2_5_5   ) for image in images]

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )





