

import math
import sys

import cva
import numpy as np






def 					lowerCamelCase_     (							UpperCAmelCase_							:     np.ndarray							,       UpperCAmelCase_							:     float	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		int			=  math.sqrt(_lowerCamelCase	)
	_UpperCamelCase   :		Any			=  1 / (sigma * math.sqrt(2 * math.pi	))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     np.ndarray							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int	)						->				Dict:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=  kernel_size // 2
	return img[x - half : x + half + 1, y - half : y + half + 1]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     float	)						->				Dict:




	'''simple docstring'''




	_UpperCamelCase   :		int			=  np.zeros((kernel_size, kernel_size)	)
	for i in range(0							,       _lowerCamelCase	):
		for j in range(0							,       _lowerCamelCase	):
			_UpperCamelCase   :		Any			=  math.sqrt(
			    abs(i - kernel_size // 2	) ** 2 + abs(j - kernel_size // 2	) ** 2	)
	return vec_gaussian(_lowerCamelCase							,       _lowerCamelCase	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     np.ndarray							,       UpperCAmelCase_							:     float							,       UpperCAmelCase_							:     float							,       UpperCAmelCase_							:     int							,       )						->				Dict:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=  np.zeros(img.shape	)
	_UpperCamelCase   :		Optional[int]			=  get_gauss_kernel(_lowerCamelCase							,       _lowerCamelCase	)
	_UpperCamelCase   :		int			=  img.shape
	for i in range(kernel_size // 2							,       size_x - kernel_size // 2	):
		for j in range(kernel_size // 2							,       size_y - kernel_size // 2	):
			_UpperCamelCase   :		List[str]			=  get_slice(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
			_UpperCamelCase   :		Any			=  img_s - img_s[kernel_size // 2, kernel_size // 2]
			_UpperCamelCase   :		Optional[Any]			=  vec_gaussian(_lowerCamelCase							,       _lowerCamelCase	)
			_UpperCamelCase   :		Optional[Any]			=  np.multiply(_lowerCamelCase							,       _lowerCamelCase	)
			_UpperCamelCase   :		Any			=  np.multiply(_lowerCamelCase							,       _lowerCamelCase	)
			_UpperCamelCase   :		Tuple			=  np.sum(_lowerCamelCase	) / np.sum(_lowerCamelCase	)
			_UpperCamelCase   :		Optional[Any]			=  val
	return imga






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=  args[1] if args[1:] else """../image_data/lena.jpg"""
	_UpperCamelCase   :		Union[str, Any]			=  float(args[2]	) if args[2:] else 1.0
	_UpperCamelCase   :		Optional[int]			=  float(args[3]	) if args[3:] else 1.0
	if args[4:]:
		_UpperCamelCase   :		Any			=  int(args[4]	)
		_UpperCamelCase   :		Any			=  kernel_size + abs(kernel_size % 2 - 1	)
	else:
		_UpperCamelCase   :		Optional[int]			=  5
	return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
							lowerCAmelCase__			, lowerCAmelCase__			, lowerCAmelCase__			, lowerCAmelCase__								=		parse_args(sys.argv)
							lowerCAmelCase__								=		cva.imread(filename, 0)
							cva.imshow("""input image""", img)

							lowerCAmelCase__								=		img / 2_5_5
							lowerCAmelCase__								=		out.astype("""float32""")
							lowerCAmelCase__								=		bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
							lowerCAmelCase__								=		out * 2_5_5
							lowerCAmelCase__								=		np.uinta(out)
							cva.imshow("""output image""", out)
							cva.waitKey(0)
							cva.destroyAllWindows()







# 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 torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "facebook/bart-large-mnli"
		a__					       =  (
		    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
		    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
		    "It returns the most likely label in the list of provided `labels` for the input text."
		)
		a__					       =  "text_classifier"
		a__					       =  AutoTokenizer
		a__					       =  AutoModelForSequenceClassification

		a__					       =  ["text", ["text"]]
		a__					       =  ["text"]


		def 		A__					(				self):
			super().setup()
			_UpperCamelCase   :		List[Any]			=						self.model.config
			_UpperCamelCase   :		Optional[int]			=						-1
			for idx, label in config.idalabel.items():
				if label.lower().startswith('entail'):
					_UpperCamelCase   :		Tuple			=						int(__snake_case)
			if self.entailment_id == -1:
				raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.')


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						labels
			return self.pre_processor(
			    [text] * len(__snake_case)	,       [f'''This example is {label}''' for label in labels]	,       return_tensors='pt'	,       padding='max_length'	,       )






		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						outputs.logits
			_UpperCamelCase   :		Optional[Any]			=						torch.argmax(logits[:, 2]).item()
			return self._labels[label_id]




