




from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__a							     =					299_792_458

# Symbols
__a     ,__a     ,__a     ,__a							     =					symbols('ct x y z')







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if velocity > c:
          raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError('''Speed must be greater than or equal to 1!''' )

     return velocity / c







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return 1 / sqrt(1 - beta(_lowercase ) ** 2 )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return np.array(
         [
             [gamma(_lowercase ), -gamma(_lowercase ) * beta(_lowercase ), 0, 0],
             [-gamma(_lowercase ) * beta(_lowercase ), gamma(_lowercase ), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ] )







def lowerCamelCase__  (     _lowercase					,		_lowercase = None ):

     '''simple docstring'''


     if event is None:
          UpperCAmelCase_     :				Optional[Any]  =     np.array([ct, x, y, z] )  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(_lowercase ) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __a							     =					transform(29_979_245)
     print('Example of four vector: ')
     print(F"""ct' = {four_vector[0]}""")
     print(F"""x' = {four_vector[1]}""")
     print(F"""y' = {four_vector[2]}""")
     print(F"""z' = {four_vector[3]}""")

     # Substitute symbols with numerical values
     __a							     =					{ct: c, x: 1, y: 1, z: 1}
     __a							     =					[four_vector[i].subs(sub_dict) for i in range(4)]

     print(F"""\n{numerical_vector}""")





# 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     __a(	_a  ):






    """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  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						Dict:
         requires_backends(self  ,['''vision''']    )
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         return self.pre_processor(text=[label]  ,images=[image]  ,padding=_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         with torch.no_grad():
              UpperCAmelCase_     :				Dict  =     self.model(**_SCREAMING_SNAKE_CASE    ).logits
         return logits





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         UpperCAmelCase_     :				Dict  =     outputs.cpu().detach().numpy()
         UpperCAmelCase_     :				Any  =     0
         UpperCAmelCase_     :				List[Any]  =     1
         return Image.fromarray((array * 255).astype(np.uinta    )    )