






'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCAmelCase__      =							{
    '''configuration_open_llama''': ['''OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenLlamaConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      =							['''LlamaTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      =							['''LlamaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      =							[
					    '''OpenLlamaForCausalLM''',
					    '''OpenLlamaModel''',
					    '''OpenLlamaPreTrainedModel''',
					    '''OpenLlamaForSequenceClassification''',
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__      =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''





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





			'''simple docstring'''

			def __init__(     self       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int  )			->		Optional[int]:
						__A=  0
						# max greyscale value for #FFFFFF
						__A=  int(self.get_greyscale(255 ,						255 ,						255  )  )

						if not self.min_threshold < threshold < self.max_threshold:
									__A=  F"""Factor value should be from 0 to {self.max_threshold}"""
									raise ValueError(lowerCAmelCase_  )

						__A=  input_img
						__A=  threshold
						__A,  __A=  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
						__A=  [
						    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
						]
						__A=  np.ones((self.width, self.height, 3) ,						np.uinta  ) * 255
			@classmethod
			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int  )			->		float:
						return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red

			def 				lowerCAmelCase    (     self       : str  )			->		None:
						for y in range(self.height  ):
									for x in range(self.width  ):
												__A=  int(self.get_greyscale(*self.input_img[y][x]  )  )
												if self.threshold > greyscale + self.error_table[y][x]:
															__A=  (0, 0, 0)
															__A=  greyscale + self.error_table[x][y]
												else:
															__A=  (255, 255, 255)
															__A=  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
					UpperCAmelCase__      =							[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
					]

					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()



