



# 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 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   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."""
				)
				UpperCamelCase__			:   str       						="""CIDAS/clipseg-rd64-refined"""
				UpperCamelCase__			:   int       						="""image_segmenter"""
				UpperCamelCase__			:   Any       						=CLIPSegForImageSegmentation

				UpperCamelCase__			:   Optional[int]       						=["""image""", """text"""]
				UpperCamelCase__			:   List[str]       						=["""image"""]




				def __init__(  self   :  Tuple,   *__lowercase   :  Optional[Any],   **__lowercase   :  Dict  ):
							requires_backends(self,   ["vision"]  )
							super().__init__(*__lowercase,   **__lowercase  )




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  "Image",   __lowercase   :  str  ):
							return self.pre_processor(text=[label],   images=[image],   padding=__lowercase,   return_tensors="pt"  )




				def 							A__ (  self   :  Dict,   __lowercase   :  Tuple  ):
							with torch.no_grad():
										lowercase__   					=			self.model(**__lowercase  ).logits
							return logits




				def 							A__ (  self   :  int,   __lowercase   :  str  ):
							lowercase__   					=			outputs.cpu().detach().numpy()
							lowercase__   					=			0
							lowercase__   					=			1
							return Image.fromarray((array * 255).astype(np.uinta  )  )









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			1  # To kept the Calculated Value
			# Since C(n, k) = C(n, n-k)
			if k > (n - k):
						lowercase__   					=			n - k
			# Calculate C(n,k)
			for i in range(SCREAMING_SNAKE_CASE_					):
						result *= n - i
						result //= i + 1
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return binomial_coefficient(2 * node_count  ,					SCREAMING_SNAKE_CASE_					) // (node_count + 1)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if n < 0:
						raise ValueError("factorial() not defined for negative values"					)
			lowercase__   					=			1
			for i in range(1  ,					n + 1					):
						result *= i
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return catalan_number(SCREAMING_SNAKE_CASE_					) * factorial(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							lowercase_						=       int(input("""Enter the number of nodes: """).strip() or 0)
							if node_count <= 0:
														raise ValueError("""We need some nodes to work with.""")
							print(
							    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
							    F'binary trees and {catalan_number(node_count)} binary search trees.'
							)




