from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def 		__lowerCamelCase							(UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							bool = False					):
					if radian_mode:
										return [magnitude * cos(UpperCAmelCase__					), magnitude * sin(UpperCAmelCase__					)]
					return [magnitude * cos(radians(UpperCAmelCase__					)					), magnitude * sin(radians(UpperCAmelCase__					)					)]





def 		__lowerCamelCase							(UpperCAmelCase__       :							NDArray[floataa] ,	UpperCAmelCase__       :							NDArray[floataa] ,	UpperCAmelCase__       :							float = 1_0**-1					):
					SCREAMING_SNAKE_CASE           =    cross(UpperCAmelCase__ ,	UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    sum(UpperCAmelCase__					)
					return abs(UpperCAmelCase__					) < eps


if __name__ == "__main__":
							# Test to check if it works
							_lowerCamelCase   :				Dict     			=						array(
							    [
							        polar_force(718.4, 1_80 - 30),
							        polar_force(879.54, 45),
							        polar_force(1_00, -90),
							    ]
							)

							_lowerCamelCase   :				NDArray[floataa]     			=						array([[0, 0], [0, 0], [0, 0]])

							assert in_static_equilibrium(forces, location)

							# Problem 1 in image_data/2D_problems.jpg
							_lowerCamelCase   :				Optional[Any]     			=						array(
							    [
							        polar_force(30 * 9.81, 15),
							        polar_force(2_15, 1_80 - 45),
							        polar_force(2_64, 90 - 30),
							    ]
							)

							_lowerCamelCase   :				Tuple     			=						array([[0, 0], [0, 0], [0, 0]])

							assert in_static_equilibrium(forces, location)

							# Problem in image_data/2D_problems_1.jpg
							_lowerCamelCase   :				int     			=						array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

							_lowerCamelCase   :				Any     			=						array([[0, 0], [6, 0], [10, 0], [12, 0]])

							assert in_static_equilibrium(forces, location)

							import doctest

							doctest.testmod()





# 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   lowercase      (       a       ):
							lowercase__  :    Optional[Any]									=							(
							    """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."""
							)
							lowercase__  :    Optional[int]									=							"""CIDAS/clipseg-rd64-refined"""
							lowercase__  :    Tuple									=							"""image_segmenter"""
							lowercase__  :    Optional[Any]									=							CLIPSegForImageSegmentation

							lowercase__  :    int									=							["""image""", """text"""]
							lowercase__  :    List[str]									=							["""image"""]




							def __init__(  self				:   str		,							*_UpperCamelCase				:   str		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												requires_backends(self		,							["vision"] )
												super().__init__(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   "Image"		,							_UpperCamelCase				:   str )   ->			Optional[int]:



												'''simple docstring'''

												return self.pre_processor(text=[label]		,							images=[image]		,							padding=_UpperCamelCase		,							return_tensors="pt" )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    self.model(**_UpperCamelCase ).logits
												return logits





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    outputs.cpu().detach().numpy()
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    1
												return Image.fromarray((array * 255).astype(np.uinta ) )




