



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   :


									'''simple docstring'''





									@staticmethod
									def     _snake_case	(	*lowerCamelCase	:							Union[str, Any]							,      **lowerCamelCase	:							Union[str, Any]	):






										'''simple docstring'''



										pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Union[str, Any]      							=      MODEL_FOR_OBJECT_DETECTION_MAPPING



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			ObjectDetectionPipeline(model=lowerCamelCase							,      image_processor=lowerCamelCase	)
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"							,      threshold=0.0	)

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

							import datasets

							__lowercase		       =			datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,      "image"							,      split="test"	)

							__lowercase		       =			[
							    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"],
							]
							__lowercase		       =			object_detector(lowerCamelCase							,      threshold=0.0	)

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



						@require_tf
						@unittest.skip("Object detection not implemented in TF"	)
						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							pass



						@require_torch
						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			"hf-internal-testing/tiny-detr-mobilenetsv3"

							__lowercase		       =			AutoModelForObjectDetection.from_pretrained(lowerCamelCase	)
							__lowercase		       =			AutoFeatureExtractor.from_pretrained(lowerCamelCase	)
							__lowercase		       =			ObjectDetectionPipeline(model=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

							__lowercase		       =			object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"							,      threshold=0.0	)

							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							    ]							,      )

							__lowercase		       =			object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]							,      threshold=0.0							,      )

							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							    ]							,      )



						@require_torch
						@slow
						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			"facebook/detr-resnet-50"

							__lowercase		       =			AutoModelForObjectDetection.from_pretrained(lowerCamelCase	)
							__lowercase		       =			AutoFeatureExtractor.from_pretrained(lowerCamelCase	)
							__lowercase		       =			ObjectDetectionPipeline(model=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

							__lowercase		       =			object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"	)
							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]							,      )

							__lowercase		       =			object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]	)
							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]							,      )



						@require_torch
						@slow
						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			"facebook/detr-resnet-50"

							__lowercase		       =			pipeline("object-detection"							,      model=lowerCamelCase	)

							__lowercase		       =			object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"	)
							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]							,      )

							__lowercase		       =			object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]	)
							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]							,      )



						@require_torch
						@slow
						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			0.9985
							__lowercase		       =			"facebook/detr-resnet-50"

							__lowercase		       =			pipeline("object-detection"							,      model=lowerCamelCase	)

							__lowercase		       =			object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"							,      threshold=lowerCamelCase	)
							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]							,      )



						@require_torch
						@require_pytesseract
						@slow
						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			"Narsil/layoutlmv3-finetuned-funsd"
							__lowercase		       =			0.9993

							__lowercase		       =			pipeline("object-detection"							,      model=lowerCamelCase							,      threshold=lowerCamelCase	)

							__lowercase		       =			object_detector(
							    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"	)
							self.assertEqual(
							    nested_simplify(lowerCamelCase							,      decimals=4	)							,      [
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							    ]							,      )








from __future__ import annotations

import bisect



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] < item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] <= item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			0
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   ) - 1

	while left <= right:
		__lowercase		       =			left + (right - left) // 2
		__lowercase		       =			sorted_collection[midpoint]
		if current_item == item:
			return midpoint
		elif item < current_item:
			__lowercase		       =			midpoint - 1
		else:
			__lowercase		       =			midpoint + 1
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			bisect.bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	if index != len(_SCREAMING_SNAKE_CASE   ) and sorted_collection[index] == item:
		return index
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if right < left:
		return None

	__lowercase		       =			left + (right - left) // 2

	if sorted_collection[midpoint] == item:
		return midpoint
	elif sorted_collection[midpoint] > item:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint - 1   )
	else:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint + 1  ,	_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Optional[Any]    		=   input("""Enter numbers separated by comma:\n""").strip()
	snake_case__					:     Any    		=   sorted(int(item) for item in user_input.split(""","""))
	snake_case__					:     Any    		=   int(input("""Enter a single number to be found in the list:\n"""))
	snake_case__					:     List[Any]    		=   binary_search(collection, target)
	if result is None:
		print(F'''{target} was not found in {collection}.''')
	else:
		print(F'''{target} was found at position {result} in {collection}.''')



