



"""simple docstring"""

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image




class        _UpperCAmelCase       (			UpperCAmelCase_):
 _lowerCAmelCase   :			List[str]							=		(
     'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
     'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
     'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
 )
 _lowerCAmelCase   :			Tuple							=		'CIDAS/clipseg-rd64-refined'
 _lowerCAmelCase   :			str							=		'image_segmenter'
 _lowerCAmelCase   :			Any							=		CLIPSegForImageSegmentation

 _lowerCAmelCase   :			str							=		['image', 'text']
 _lowerCAmelCase   :			Tuple							=		['image']

 def __init__(     self		:       Union[str, Any]  ,     *lowercase_		:       int  ,     **lowercase_		:       Dict				):
     requires_backends(self  ,     ['''vision''']				)
     super().__init__(*__UpperCAmelCase  ,     **__UpperCAmelCase				)

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       "Image"  ,     lowercase_		:       str				):
     return self.pre_processor(text=[label]  ,     images=[image]  ,     padding=__UpperCAmelCase  ,     return_tensors='''pt'''				)

 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       str				):



     with torch.no_grad():
         snake_case_ :     Optional[int]				=	self.model(**__UpperCAmelCase				).logits
     return logits






 def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       int				):
     snake_case_ :     Union[str, Any]				=	outputs.cpu().detach().numpy()
     snake_case_ :     Union[str, Any]				=	0
     snake_case_ :     List[str]				=	1
     return Image.fromarray((array * 255).astype(np.uinta				)				)






from __future__ import annotations

from scipy.special import comb  # type: ignore





class      A							:


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						list[tuple[float, float]]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  list_of_points
         # Degree determines the flexibility of the curve.
         # Degree = 1 will produce a straight line.
         UpperCAmelCase__								=  len(__UpperCAmelCase		) - 1


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						float		)						->				list[float]:
         """simple docstring"""



         assert 0 <= t <= 1, "Time t must be between 0 and 1."
         UpperCAmelCase__								=  []
         for i in range(len(self.list_of_points		)		):
               # basis function for each i
               output_values.append(
                   comb(self.degree       ,  __UpperCAmelCase		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
         # the basis must sum up to 1 for it to produce a valid Bezier curve.
         assert round(sum(__UpperCAmelCase		)       ,  5		) == 1
         return output_values


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						float		)						->				tuple[float, float]:
         """simple docstring"""



         assert 0 <= t <= 1, "Time t must be between 0 and 1."

         UpperCAmelCase__								=  self.basis_function(__UpperCAmelCase		)
         UpperCAmelCase__								=  0.0
         UpperCAmelCase__								=  0.0
         for i in range(len(self.list_of_points		)		):
               # For all points, sum up the product of i-th basis function and i-th point.
               x += basis_function[i] * self.list_of_points[i][0]
               y += basis_function[i] * self.list_of_points[i][1]
         return (x, y)





   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						float = 0.01		)						->				Optional[int]:
         """simple docstring"""



         from matplotlib import pyplot as plt  # type: ignore

         UpperCAmelCase__								=  []  # x coordinates of points to plot
         UpperCAmelCase__								=  []  # y coordinates of points to plot

         UpperCAmelCase__								=  0.0
         while t <= 1:
               UpperCAmelCase__								=  self.bezier_curve_function(__UpperCAmelCase		)
               to_plot_x.append(value[0]		)
               to_plot_y.append(value[1]		)
               t += step_size

         UpperCAmelCase__								=  [i[0] for i in self.list_of_points]
         UpperCAmelCase__								=  [i[1] for i in self.list_of_points]

         plt.plot(
             __UpperCAmelCase       ,  __UpperCAmelCase       ,  color="blue"       ,  label="Curve of Degree " + str(self.degree		)       ,  )
         plt.scatter(__UpperCAmelCase       ,  __UpperCAmelCase       ,  color="red"       ,  label="Control Points"		)
         plt.legend()
         plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
      BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
      BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



