"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCAmelCase_     =	imread(R'''digital_image_processing/image_data/lena_small.jpg''')
lowerCAmelCase_     =	cvtColor(img, COLOR_BGR2GRAY)



def      lowerCamelCase_()->    str:
							_SCREAMING_SNAKE_CASE		:						int			  =    cn.convert_to_negative(__SCREAMING_SNAKE_CASE  )
							# assert negative_img array for at least one True
							assert negative_img.any()



def      lowerCamelCase_()->    Dict:
							with Image.open("""digital_image_processing/image_data/lena_small.jpg"""  ) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(__SCREAMING_SNAKE_CASE     ,	110  )  ).startswith(
														    """<PIL.Image.Image image mode=RGB size=100x100 at"""  )



def      lowerCamelCase_()->    Any:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    canny.gen_gaussian_kernel(9     ,	sigma=1.4  )
							# Assert ambiguous array
							assert resp.all()



def      lowerCamelCase_()->    int:
							_SCREAMING_SNAKE_CASE		:						str			  =    imread("""digital_image_processing/image_data/lena_small.jpg"""     ,	0  )
							# assert ambiguous array for all == True
							assert canny_img.all()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    canny.canny(__SCREAMING_SNAKE_CASE  )
							# assert canny array for at least one True
							assert canny_array.any()



def      lowerCamelCase_()->    List[str]:
							assert gg.gaussian_filter(__SCREAMING_SNAKE_CASE     ,	5     ,	sigma=0.9  ).all()



def      lowerCamelCase_()->    List[str]:
							# laplace diagonals
							_SCREAMING_SNAKE_CASE		:						Dict			  =    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    conv.img_convolve(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).astype(__SCREAMING_SNAKE_CASE  )
							assert res.any()



def      lowerCamelCase_()->    str:
							assert med.median_filter(__SCREAMING_SNAKE_CASE     ,	3  ).any()



def      lowerCamelCase_()->    int:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    sob.sobel_filter(__SCREAMING_SNAKE_CASE  )
							assert grad.any() and theta.any()



def      lowerCamelCase_()->    str:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    sp.make_sepia(__SCREAMING_SNAKE_CASE     ,	20  )
							assert sepia.all()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"  )->    str:
							_SCREAMING_SNAKE_CASE		:						Any			  =    bs.Burkes(imread(__SCREAMING_SNAKE_CASE     ,	1  )     ,	120  )
							burkes.process()
							assert burkes.output_img.any()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"     ,	)->    str:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    rs.NearestNeighbour(imread(__SCREAMING_SNAKE_CASE     ,	1  )     ,	400     ,	200  )
							nn.process()
							assert nn.output.any()



def      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    """digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    imread(__SCREAMING_SNAKE_CASE     ,	0  )

							# Test for get_neighbors_pixel function() return not None
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    0
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    image[x_coordinate][y_coordinate]

							_SCREAMING_SNAKE_CASE		:						int			  =    lbp.get_neighbors_pixel(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							assert neighbors_pixels is not None

							# Test for local_binary_pattern function()
							# Create a numpy array as the same height and width of read image
							_SCREAMING_SNAKE_CASE		:						Any			  =    np.zeros((image.shape[0], image.shape[1])  )

							# Iterating through the image and calculating the local binary pattern value
							# for each pixel.
							for i in range(0     ,	image.shape[0]  ):
														for j in range(0     ,	image.shape[1]  ):
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    lbp.local_binary_value(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							assert lbp_image.any()



"""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 torch

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




class  _snake_case		(    __snake_case ):







				"""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 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    labels
											return self.pre_processor(
											    [text] * len(_A)      ,    [f"""This example is {label}""" for label in labels]      ,    return_tensors="""pt"""      ,    padding="""max_length"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    outputs.logits
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.argmax(logits[:, 2]).item()
											return self._labels[label_id]


