





'''simple docstring'''







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 					__a      (lowerCamelCase     ):
							__a					:   Optional[int]									=       ["image_processor", "tokenizer"]
							__a					:   Optional[Any]									=       "BlipImageProcessor"
							__a					:   List[str]									=       ("BertTokenizer", "BertTokenizerFast")

							def __init__(							self							:     List[str]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Dict   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  False
													super().__init__(__magic_name__       ,     __magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  self.image_processor

							def __call__(							self							:     Optional[int]       ,     __magic_name__							:     ImageInput = None       ,     __magic_name__							:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     Union[bool, str, PaddingStrategy] = False       ,     __magic_name__							:     Union[bool, str, TruncationStrategy] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     int = 0       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     bool = False       ,     __magic_name__							:     bool = False       ,     __magic_name__							:     bool = False       ,     __magic_name__							:     bool = False       ,     __magic_name__							:     bool = False       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     Optional[Union[str, TensorType]] = None       ,     **__magic_name__							:     Optional[int]       ,     )					->       BatchEncoding:







													"""simple docstring"""
													if images is None and text is None:
																			raise ValueError('''You have to specify either images or text.'''   )

													# Get only text
													if images is None:
																			UpperCAmelCase_			:      Any     						=  self.tokenizer
																			UpperCAmelCase_			:      Any     						=  self.tokenizer(
																			    text=__magic_name__       ,     add_special_tokens=__magic_name__       ,     padding=__magic_name__       ,     truncation=__magic_name__       ,     max_length=__magic_name__       ,     stride=__magic_name__       ,     pad_to_multiple_of=__magic_name__       ,     return_attention_mask=__magic_name__       ,     return_overflowing_tokens=__magic_name__       ,     return_special_tokens_mask=__magic_name__       ,     return_offsets_mapping=__magic_name__       ,     return_token_type_ids=__magic_name__       ,     return_length=__magic_name__       ,     verbose=__magic_name__       ,     return_tensors=__magic_name__       ,     **__magic_name__       ,     )
																			return text_encoding

													# add pixel_values
													UpperCAmelCase_			:      Union[str, Any]     						=  self.image_processor(__magic_name__       ,     return_tensors=__magic_name__   )

													if text is not None:
																			UpperCAmelCase_			:      int     						=  self.tokenizer(
																			    text=__magic_name__       ,     add_special_tokens=__magic_name__       ,     padding=__magic_name__       ,     truncation=__magic_name__       ,     max_length=__magic_name__       ,     stride=__magic_name__       ,     pad_to_multiple_of=__magic_name__       ,     return_attention_mask=__magic_name__       ,     return_overflowing_tokens=__magic_name__       ,     return_special_tokens_mask=__magic_name__       ,     return_offsets_mapping=__magic_name__       ,     return_token_type_ids=__magic_name__       ,     return_length=__magic_name__       ,     verbose=__magic_name__       ,     return_tensors=__magic_name__       ,     **__magic_name__       ,     )
													else:
																			UpperCAmelCase_			:      Optional[Any]     						=  None

													if text_encoding is not None:
																			encoding_image_processor.update(__magic_name__   )

													return encoding_image_processor

							def     UpperCAmelCase__	(							self							:     int       ,     *__magic_name__							:     Any       ,     **__magic_name__							:     Any   )					->       Union[str, Any]:







													"""simple docstring"""
													return self.tokenizer.batch_decode(*__magic_name__       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     str       ,     *__magic_name__							:     Optional[Any]       ,     **__magic_name__							:     List[str]   )					->       Dict:







													"""simple docstring"""
													return self.tokenizer.decode(*__magic_name__       ,     **__magic_name__   )



							@property
							def     UpperCAmelCase__	(							self							:     str   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.tokenizer.model_input_names
													UpperCAmelCase_			:      Any     						=  self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )












'''simple docstring'''







import sys
import turtle





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float]   ) ->	tuple[float, float]:
						return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			int,			) ->	None:
						my_pen.up()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.down()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )

						if depth == 0:
												return

						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )


if __name__ == "__main__":
					if len(sys.argv) != 2:
										raise ValueError(
										    "Correct format for using this script: "
										    "python fractals.py <int:depth_for_fractal>"
										)
					snake_case_     :       Any						=  turtle.Turtle()
					my_pen.ht()
					my_pen.speed(5)
					my_pen.pencolor("red")

					snake_case_     :       Tuple						=  [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
					triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))





