
from __future__ import annotations

import math

lowercase__							:							str									=			"2020.9.26"
lowercase__							:							Dict									=			"xcodz-dot, cclaus, dhruvmanila"




def 	A_ (							snake_case							:   float ,      snake_case							:   float ,      snake_case							:   float ,      snake_case							:   float ,      snake_case							:   float )      ->    tuple[float, float]:



						'''simple docstring'''

						if not all(isinstance(snake_case ,      (float, int) ) for val in locals().values() ):
												__UpperCamelCase       =			f"Input values must either be float or int: {list(locals().values() )}"
												raise TypeError(snake_case )
						__UpperCamelCase       =			((x * distance) / (z + distance)) * scale
						__UpperCamelCase       =			((y * distance) / (z + distance)) * scale
						return projected_x, projected_y




def 	A_ (							snake_case							:   float ,      snake_case							:   float ,      snake_case							:   float ,      snake_case							:   str ,      snake_case							:   float )      ->    tuple[float, float, float]:



						'''simple docstring'''

						if not isinstance(snake_case ,      snake_case ):
												raise TypeError('''Axis must be a str''' )
						__UpperCamelCase       =			locals()
						del input_variables["axis"]
						if not all(isinstance(snake_case ,      (float, int) ) for val in input_variables.values() ):
												__UpperCamelCase       =			(
												    '''Input values except axis must either be float or int: '''
												    f"{list(input_variables.values() )}"
												)
												raise TypeError(snake_case )
						__UpperCamelCase       =			(angle % 360) / 450 * 180 / math.pi
						if axis == "z":
												__UpperCamelCase       =			x * math.cos(snake_case ) - y * math.sin(snake_case )
												__UpperCamelCase       =			y * math.cos(snake_case ) + x * math.sin(snake_case )
												__UpperCamelCase       =			z
						elif axis == "x":
												__UpperCamelCase       =			y * math.cos(snake_case ) - z * math.sin(snake_case )
												__UpperCamelCase       =			z * math.cos(snake_case ) + y * math.sin(snake_case )
												__UpperCamelCase       =			x
						elif axis == "y":
												__UpperCamelCase       =			x * math.cos(snake_case ) - z * math.sin(snake_case )
												__UpperCamelCase       =			z * math.cos(snake_case ) + x * math.sin(snake_case )
												__UpperCamelCase       =			y
						else:
												raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )

						return new_x, new_y, new_z


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(F"{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }")
				print(F"{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }")






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__							:							Dict									=			{"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			["ViTFeatureExtractor"]
				lowercase__							:							str									=			["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[str]									=			[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[Any]									=			[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

				lowercase__							:							List[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




