
"""simple docstring"""


import colorsys

from PIL import Image  # type: ignore






def 					_snake_case (						snake_case__      :   float		,					snake_case__      :   float		,					snake_case__      :   int   ):
	A        =      x
	A        =      y
	for step in range(snake_case__   ):  # noqa: B007
		A        =      a * a - b * b + x
		A        =      2 * a * b + y
		A        =      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 					_snake_case (						snake_case__      :   float   ):
	if distance == 1:
		return (0, 0, 0)
	else:
		return (255, 255, 255)






def 					_snake_case (						snake_case__      :   float   ):
	if distance == 1:
		return (0, 0, 0)
	else:
		return tuple(round(i * 255   ) for i in colorsys.hsv_to_rgb(snake_case__		,					1		,					1   )   )






def 					_snake_case (						snake_case__      :   int = 800		,					snake_case__      :   int = 600		,					snake_case__      :   float = -0.6		,					snake_case__      :   float = 0		,					snake_case__      :   float = 3.2		,					snake_case__      :   int = 50		,					snake_case__      :   bool = True		,					):
	A        =      Image.new('RGB'		,					(image_width, image_height)   )
	A        =      img.load()

	# loop through the image-coordinates
	for image_x in range(snake_case__   ):
		for image_y in range(snake_case__   ):
			# determine the figure-coordinates based on the image-coordinates
			A        =      figure_width / image_width * image_height
			A        =      figure_center_x + (image_x / image_width - 0.5) * figure_width
			A        =      figure_center_y + (image_y / image_height - 0.5) * figure_height

			A        =      get_distance(snake_case__		,					snake_case__		,					snake_case__   )

			# color the corresponding pixel based on the selected coloring-function
			if use_distance_color_coding:
				A        =      get_color_coded_rgb(snake_case__   )
			else:
				A        =      get_black_and_white_rgb(snake_case__   )

	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()

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase__          =		logging.get_logger(__name__)







@add_end_docstrings(__magic_name__	)
class      A__							(     __magic_name__	):






				def __init__(						self					:      int    ,     *a					:      Dict    ,     **a					:      Union[str, Any]   ):






							'''simple docstring'''







							super().__init__(*a    ,     **a   )
							requires_backends(self    ,     'vision'   )
							self.check_model_type(
							    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							    if self.framework == 'tf'
							    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{}
							if top_k is not None:
										lowerCAmelCase__      :      Tuple         =		top_k
							return {}, {}, postprocess_params






				def __call__(						self					:      Any    ,     a					:      Union[str, List[str], "Image.Image", List["Image.Image"]]    ,     **a					:      List[Any]   ):






							'''simple docstring'''







							return super().__call__(a    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		load_image(a   )
							lowerCAmelCase__      :      Optional[int]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
							return model_inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.model(**a   )
							return model_outputs






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      List[Any]=5   ):






							'''simple docstring'''







							if top_k > self.model.config.num_labels:
										lowerCAmelCase__      :      Optional[int]         =		self.model.config.num_labels

							if self.framework == "pt":
										lowerCAmelCase__      :      List[Any]         =		model_outputs.logits.softmax(-1   )[0]
										lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		probs.topk(a   )
							elif self.framework == "tf":
										lowerCAmelCase__      :      Any         =		stable_softmax(model_outputs.logits    ,     axis=-1   )[0]
										lowerCAmelCase__      :      Any         =		tf.math.top_k(a    ,     k=a   )
										lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		topk.values.numpy(), topk.indices.numpy()
							else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''   )

							lowerCAmelCase__      :      List[Any]         =		scores.tolist()
							lowerCAmelCase__      :      List[str]         =		ids.tolist()
							return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a    ,     a   )]