





"""simple docstring"""


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 							lowercase__			:

					"""simple docstring"""







					@staticmethod
					def   _a      (      *_A						,   **_A    ):






											'''simple docstring'''







											pass






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

			"""simple docstring"""
			__lowerCAmelCase     :				Tuple										=  MODEL_FOR_OBJECT_DETECTION_MAPPING







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			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

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

									UpperCamelCase				:		Optional[int]				   =			[
									    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"""],
									]
									UpperCamelCase				:		Dict				   =			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   _a      (      self    ):






									'''simple docstring'''







									pass







			@require_torch
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			"""hf-internal-testing/tiny-detr-mobilenetsv3"""

									UpperCamelCase				:		Optional[int]				   =			AutoModelForObjectDetection.from_pretrained(_A    )
									UpperCamelCase				:		List[Any]				   =			AutoFeatureExtractor.from_pretrained(_A    )
									UpperCamelCase				:		Tuple				   =			ObjectDetectionPipeline(model=_A						,   feature_extractor=_A    )

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

									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [
									        {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
									        {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
									    ]						,   )

									UpperCamelCase				:		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""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
									            {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
									        ],
									        [
									            {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
									            {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_5_9, """ymin""": 1_2_0, """xmax""": 4_8_0, """ymax""": 3_5_9}},
									        ],
									    ]						,   )







			@require_torch
			@slow
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			"""facebook/detr-resnet-50"""

									UpperCamelCase				:		int				   =			AutoModelForObjectDetection.from_pretrained(_A    )
									UpperCamelCase				:		Union[str, Any]				   =			AutoFeatureExtractor.from_pretrained(_A    )
									UpperCamelCase				:		Dict				   =			ObjectDetectionPipeline(model=_A						,   feature_extractor=_A    )

									UpperCamelCase				:		Dict				   =			object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [
									        {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
									        {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
									        {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
									        {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
									        {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
									    ]						,   )

									UpperCamelCase				:		Optional[int]				   =			object_detector(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [
									        [
									            {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
									            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
									            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
									            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
									            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
									        ],
									        [
									            {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
									            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
									            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
									            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
									            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
									        ],
									    ]						,   )







			@require_torch
			@slow
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			"""facebook/detr-resnet-50"""

									UpperCamelCase				:		Optional[Any]				   =			pipeline("""object-detection"""						,   model=_A    )

									UpperCamelCase				:		Optional[Any]				   =			object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [
									        {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
									        {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
									        {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
									        {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
									        {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
									    ]						,   )

									UpperCamelCase				:		int				   =			object_detector(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [
									        [
									            {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
									            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
									            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
									            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
									            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
									        ],
									        [
									            {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_0, """xmax""": 1_7_5, """ymax""": 1_1_7}},
									            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_3_3, """ymin""": 7_2, """xmax""": 3_6_8, """ymax""": 1_8_7}},
									            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_3_9, """ymax""": 4_7_3}},
									            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
									            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
									        ],
									    ]						,   )







			@require_torch
			@slow
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			0.99_85
									UpperCamelCase				:		Tuple				   =			"""facebook/detr-resnet-50"""

									UpperCamelCase				:		Dict				   =			pipeline("""object-detection"""						,   model=_A    )

									UpperCamelCase				:		str				   =			object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""						,   threshold=_A    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [
									        {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 1_3, """ymin""": 5_2, """xmax""": 3_1_4, """ymax""": 4_7_0}},
									        {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_4_5, """ymin""": 2_3, """xmax""": 6_4_0, """ymax""": 3_6_8}},
									    ]						,   )







			@require_torch
			@require_pytesseract
			@slow
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			"""Narsil/layoutlmv3-finetuned-funsd"""
									UpperCamelCase				:		Optional[int]				   =			0.99_93

									UpperCamelCase				:		List[Any]				   =			pipeline("""object-detection"""						,   model=_A						,   threshold=_A    )

									UpperCamelCase				:		Tuple				   =			object_detector(
									    """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""    )
									self.assertEqual(
									    nested_simplify(_A						,   decimals=4    )						,   [
									        {"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_9_4, """ymin""": 2_5_4, """xmax""": 3_4_3, """ymax""": 2_6_4}},
									        {"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_9_4, """ymin""": 2_5_4, """xmax""": 3_4_3, """ymax""": 2_6_4}},
									    ]						,   )







'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





