
from typing import TYPE_CHECKING

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


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          '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:
      lowercase__       =[
          '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:
      lowercase__       =[
          '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

      lowercase__       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


import colorsys

from PIL import Image  # type: ignore




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  x
 __a :  List[Any]						=  y
 for step in range(lowerCAmelCase__  ):  # noqa: B007
  __a :  List[Any]						=  a * a - b * b + x
  __a :  Tuple						=  2 * a * b + y
  __a :  Optional[int]						=  a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return (2_5_5, 2_5_5, 2_5_5)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 2_5_5  ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__  ,   1  ,   1  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 8_0_0  ,   lowerCAmelCase__							:				int = 6_0_0  ,   lowerCAmelCase__							:				float = -0.6  ,   lowerCAmelCase__							:				float = 0  ,   lowerCAmelCase__							:				float = 3.2  ,   lowerCAmelCase__							:				int = 5_0  ,   lowerCAmelCase__							:				bool = True  ,   ):
 __a :  int						=  Image.new('''RGB'''  ,   (image_width, image_height)  )
 __a :  Dict						=  img.load()

 # loop through the image-coordinates
 for image_x in range(lowerCAmelCase__  ):
  for image_y in range(lowerCAmelCase__  ):
   # determine the figure-coordinates based on the image-coordinates
   __a :  Optional[Any]						=  figure_width / image_width * image_height
   __a :  str						=  figure_center_x + (image_x / image_width - 0.5) * figure_width
   __a :  str						=  figure_center_y + (image_y / image_height - 0.5) * figure_height

   __a :  Tuple						=  get_distance(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __a :  Optional[int]						=  get_color_coded_rgb(lowerCAmelCase__  )
   else:
    __a :  Optional[Any]						=  get_black_and_white_rgb(lowerCAmelCase__  )

 return img


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # colored version, full figure
      lowercase__       =get_image()

      # uncomment for colored version, different section, zoomed in
      # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
      # figure_width = 0.8)

      # uncomment for black and white version, full figure
      # img = get_image(use_distance_color_coding = False)

      # uncomment to save the image
      # img.save("mandelbrot.png")

      img.show()
