"""simple docstring"""






import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class 							snake_case			:




					"""simple docstring"""

					def __init__(							self  :      Optional[int]       ,lowerCamelCase__  :      Union[str, Any]       ,lowerCamelCase__  :      Dict     ):
									UpperCAmelCase__			 =				0
									# max greyscale value for #FFFFFF
									UpperCAmelCase__			 =				int(self.get_greyscale(255       ,255       ,255     )     )

									if not self.min_threshold < threshold < self.max_threshold:
													UpperCAmelCase__			 =				f'''Factor value should be from 0 to {self.max_threshold}'''
													raise ValueError(lowerCamelCase__     )

									UpperCAmelCase__			 =				input_img
									UpperCAmelCase__			 =				threshold
									UpperCAmelCase__			 =				self.input_img.shape[1], self.input_img.shape[0]

									# error table size (+4 columns and +1 row) greater than input image because of
									# lack of if statements
									UpperCAmelCase__			 =				[
									    [0 for _ in range(self.height + 4     )] for __ in range(self.width + 1     )
									]
									UpperCAmelCase__			 =				np.ones((self.width, self.height, 3)       ,np.uinta     ) * 255







					@classmethod
					def 						__lowerCAmelCase		(							cls  :      Dict       ,lowerCamelCase__  :      Optional[Any]       ,lowerCamelCase__  :      Optional[Any]       ,lowerCamelCase__  :      List[str]     ):
									return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







					def 						__lowerCAmelCase		(							self  :      int     ):
									for y in range(self.height     ):
													for x in range(self.width     ):
																	UpperCAmelCase__			 =				int(self.get_greyscale(*self.input_img[y][x]     )     )
																	if self.threshold > greyscale + self.error_table[y][x]:
																					UpperCAmelCase__			 =				(0, 0, 0)
																					UpperCAmelCase__			 =				greyscale + self.error_table[x][y]
																	else:
																					UpperCAmelCase__			 =				(255, 255, 255)
																					UpperCAmelCase__			 =				greyscale + self.error_table[x][y] - 255
																	self.error_table[y][x + 1] += int(8 / 32 * current_error     )
																	self.error_table[y][x + 2] += int(4 / 32 * current_error     )
																	self.error_table[y + 1][x] += int(8 / 32 * current_error     )
																	self.error_table[y + 1][x + 1] += int(4 / 32 * current_error     )
																	self.error_table[y + 1][x + 2] += int(2 / 32 * current_error     )
																	self.error_table[y + 1][x - 1] += int(4 / 32 * current_error     )
																	self.error_table[y + 1][x - 2] += int(2 / 32 * current_error     )


if __name__ == "__main__":
						# create Burke's instances with original images in greyscale
						lowerCAmelCase__    :    Optional[int]			=      [
						    Burkes(imread('image_data/lena.jpg', 1), threshold)
						    for threshold in (1, 126, 130, 140)
						]

						for burkes in burkes_instances:
												burkes.process()

						for burkes in burkes_instances:
												imshow(
												    F"""Original image with dithering threshold: {burkes.threshold}""",
												    burkes.output_img,
												)

						waitKey(0)
						destroyAllWindows()








'''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.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a  :     Optional[Any]       =       {
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :     Dict       =       ["""DocumentQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""ImageCaptioningTool"""]
							a  :     Tuple       =       ["""ImageQuestionAnsweringTool"""]
							a  :     Tuple       =       ["""ImageSegmentationTool"""]
							a  :     List[str]       =       ["""SpeechToTextTool"""]
							a  :     Dict       =       ["""TextClassificationTool"""]
							a  :     Union[str, Any]       =       ["""TextQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""TextSummarizationTool"""]
							a  :     Optional[Any]       =       ["""TextToSpeechTool"""]
							a  :     str       =       ["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							a  :     Dict       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

