



'''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_						):





				'''simple docstring'''





				lowerCAmelCase__    			=			(
				    '''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__    			=			'''CIDAS/clipseg-rd64-refined'''
				lowerCAmelCase__    			=			'''image_segmenter'''
				lowerCAmelCase__    			=			CLIPSegForImageSegmentation

				lowerCAmelCase__    			=			['''image''', '''text''']
				lowerCAmelCase__    			=			['''image''']

				def __init__(							self     :     List[str]     ,				*_lowerCAmelCase     :     Union[str, Any]     ,				**_lowerCAmelCase     :     Dict):







											'''simple docstring'''

											requires_backends(self     ,				['vision'])
											super().__init__(*lowerCAmelCase__     ,				**lowerCAmelCase__)

				def  __lowerCamelCase   (							self     :     int     ,				_lowerCAmelCase     :     "Image"     ,				_lowerCAmelCase     :     str):







											'''simple docstring'''

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

				def  __lowerCamelCase   (							self     :     Optional[Any]     ,				_lowerCAmelCase     :     Any):







											'''simple docstring'''

											with torch.no_grad():
																		__lowercase						=self.model(**lowerCAmelCase__).logits
											return logits

				def  __lowerCamelCase   (							self     :     List[Any]     ,				_lowerCAmelCase     :     Optional[int]):







											'''simple docstring'''

											__lowercase						=outputs.cpu().detach().numpy()
											__lowercase						=0
											__lowercase						=1
											return Image.fromarray((array * 2_5_5).astype(np.uinta))






def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       [redshift, radiation_density, matter_density, dark_energy]
						if any(p < 0 for p in parameters    ):
												raise ValueError("All input parameters must be positive"    )

						if any(p > 1 for p in parameters[1:4]    ):
												raise ValueError("Relative densities cannot be greater than one"    )
						else:
												SCREAMING_SNAKE_CASE_: int  		=       1 - (matter_density + radiation_density + dark_energy)

												SCREAMING_SNAKE_CASE_: Dict  		=       (
												    radiation_density * (redshift + 1) ** 4
												    + matter_density * (redshift + 1) ** 3
												    + curvature * (redshift + 1) ** 2
												    + dark_energy
												)

												SCREAMING_SNAKE_CASE_: Any  		=       hubble_constant * e_a ** (1 / 2)
												return hubble


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo LCDM approximation
						lowerCAmelCase							:  List[Any]   	=						0.3

						print(
						    hubble_parameter(
						        hubble_constant=68.3,
						        radiation_density=1E-4,
						        matter_density=matter_density,
						        dark_energy=1 - matter_density,
						        redshift=0,
						    )
						)






