
import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:







					class      A__							:






									@staticmethod
									def 							_lowerCamelCase		(						*a					:      Dict    ,     **a					:      Dict   ):






												'''simple docstring'''







												pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class      A__							(     unittest.TestCase	):
				lowercase			    =  MODEL_FOR_OBJECT_DETECTION_MAPPING






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      Tuple    ,     a					:      Dict    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		ObjectDetectionPipeline(model=a    ,     image_processor=a   )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      int    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'    ,     threshold=0.0   )

							self.assertGreater(len(a   )    ,     0   )
							for detected_object in outputs:
										self.assertEqual(
										    a    ,     {
										        'score': ANY(a   ),
										        'label': ANY(a   ),
										        'box': {'xmin': ANY(a   ), 'ymin': ANY(a   ), 'xmax': ANY(a   ), 'ymax': ANY(a   )},
										    }    ,     )

							import datasets

							lowerCAmelCase__      :      List[str]         =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'    ,     'image'    ,     split='test'   )

							lowerCAmelCase__      :      Any         =		[
							    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   ),
							    'http://images.cocodataset.org/val2017/000000039769.jpg',
							    # RGBA
							    dataset[0]['file'],
							    # LA
							    dataset[1]['file'],
							    # L
							    dataset[2]['file'],
							]
							lowerCAmelCase__      :      Union[str, Any]         =		object_detector(a    ,     threshold=0.0   )

							self.assertEqual(len(a   )    ,     len(a   )   )
							for outputs in batch_outputs:
										self.assertGreater(len(a   )    ,     0   )
										for detected_object in outputs:
													self.assertEqual(
													    a    ,     {
													        'score': ANY(a   ),
													        'label': ANY(a   ),
													        'box': {'xmin': ANY(a   ), 'ymin': ANY(a   ), 'xmax': ANY(a   ), 'ymax': ANY(a   )},
													    }    ,     )






				@require_tf
				@unittest.skip('Object detection not implemented in TF'   )
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							pass






				@require_torch
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		'hf-internal-testing/tiny-detr-mobilenetsv3'

							lowerCAmelCase__      :      Any         =		AutoModelForObjectDetection.from_pretrained(a   )
							lowerCAmelCase__      :      Any         =		AutoFeatureExtractor.from_pretrained(a   )
							lowerCAmelCase__      :      Optional[int]         =		ObjectDetectionPipeline(model=a    ,     feature_extractor=a   )

							lowerCAmelCase__      :      Union[str, Any]         =		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'    ,     threshold=0.0   )

							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
							        {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
							    ]    ,     )

							lowerCAmelCase__      :      int         =		object_detector(
							    [
							        'http://images.cocodataset.org/val2017/000000039769.jpg',
							        'http://images.cocodataset.org/val2017/000000039769.jpg',
							    ]    ,     threshold=0.0    ,     )

							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        [
							            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
							            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
							        ],
							        [
							            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
							            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
							        ],
							    ]    ,     )






				@require_torch
				@slow
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		'facebook/detr-resnet-50'

							lowerCAmelCase__      :      Tuple         =		AutoModelForObjectDetection.from_pretrained(a   )
							lowerCAmelCase__      :      Optional[int]         =		AutoFeatureExtractor.from_pretrained(a   )
							lowerCAmelCase__      :      Optional[Any]         =		ObjectDetectionPipeline(model=a    ,     feature_extractor=a   )

							lowerCAmelCase__      :      Optional[int]         =		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'   )
							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
							        {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
							        {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
							        {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
							        {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
							    ]    ,     )

							lowerCAmelCase__      :      Tuple         =		object_detector(
							    [
							        'http://images.cocodataset.org/val2017/000000039769.jpg',
							        'http://images.cocodataset.org/val2017/000000039769.jpg',
							    ]   )
							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        [
							            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
							            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
							            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
							            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
							            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
							        ],
							        [
							            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
							            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
							            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
							            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
							            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
							        ],
							    ]    ,     )






				@require_torch
				@slow
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		'facebook/detr-resnet-50'

							lowerCAmelCase__      :      List[str]         =		pipeline('object-detection'    ,     model=a   )

							lowerCAmelCase__      :      Union[str, Any]         =		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'   )
							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
							        {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
							        {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
							        {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
							        {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
							    ]    ,     )

							lowerCAmelCase__      :      Optional[Any]         =		object_detector(
							    [
							        'http://images.cocodataset.org/val2017/000000039769.jpg',
							        'http://images.cocodataset.org/val2017/000000039769.jpg',
							    ]   )
							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        [
							            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
							            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
							            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
							            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
							            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
							        ],
							        [
							            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
							            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
							            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
							            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
							            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
							        ],
							    ]    ,     )






				@require_torch
				@slow
				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		0.9_9_8_5
							lowerCAmelCase__      :      str         =		'facebook/detr-resnet-50'

							lowerCAmelCase__      :      List[Any]         =		pipeline('object-detection'    ,     model=a   )

							lowerCAmelCase__      :      str         =		object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'    ,     threshold=a   )
							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
							        {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
							    ]    ,     )






				@require_torch
				@require_pytesseract
				@slow
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		'Narsil/layoutlmv3-finetuned-funsd'
							lowerCAmelCase__      :      List[str]         =		0.9_9_9_3

							lowerCAmelCase__      :      List[Any]         =		pipeline('object-detection'    ,     model=a    ,     threshold=a   )

							lowerCAmelCase__      :      int         =		object_detector(
							    'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'   )
							self.assertEqual(
							    nested_simplify(a    ,     decimals=4   )    ,     [
							        {'score': 0.9_9_9_3, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
							        {'score': 0.9_9_9_3, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
							    ]    ,     )

from __future__ import annotations

import math

lowerCamelCase__          =		"""2020.9.26"""
lowerCamelCase__          =		"""xcodz-dot, cclaus, dhruvmanila"""
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float]:
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in locals().values()     ):
						lowerCAmelCase__      :      List[str]         =		F'''Input values must either be float or int: {list(locals().values()     )}'''
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Tuple         =		((x * distance) / (z + distance)) * scale
			lowerCAmelCase__      :      Optional[int]         =		((y * distance) / (z + distance)) * scale
			return projected_x, projected_y
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float, float]:
			if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						raise TypeError('Axis must be a str'     )
			lowerCAmelCase__      :      Optional[int]         =		locals()
			del input_variables["axis"]
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in input_variables.values()     ):
						lowerCAmelCase__      :      List[Any]         =		(
						    'Input values except axis must either be float or int: '
						    F'''{list(input_variables.values()     )}'''
						)
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      int         =		(angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						lowerCAmelCase__      :      Tuple         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z
			elif axis == "x":
						lowerCAmelCase__      :      Dict         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Tuple         =		x
			elif axis == "y":
						lowerCAmelCase__      :      str         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[Any]         =		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) = }""")