

from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFCvtForImageClassification, TFCvtModel
					from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class 	snake_case_   (		_lowerCamelCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				str							)      ->Optional[int]:
									snake_case_   =	self.config_class(**self.inputs_dict							)
									self.parent.assertTrue(hasattr(lowercase_							,   '''embed_dim'''							)							)
									self.parent.assertTrue(hasattr(lowercase_							,   '''num_heads'''							)							)






class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Dict=1_3							,   _UpperCamelCase :				Optional[int]=6_4							,   _UpperCamelCase :				Optional[int]=3							,   _UpperCamelCase :				Union[str, Any]=[1_6, 4_8, 9_6]							,   _UpperCamelCase :				List[str]=[1, 3, 6]							,   _UpperCamelCase :				Optional[Any]=[1, 2, 1_0]							,   _UpperCamelCase :				List[Any]=[7, 3, 3]							,   _UpperCamelCase :				List[Any]=[4, 2, 2]							,   _UpperCamelCase :				Union[str, Any]=[2, 1, 1]							,   _UpperCamelCase :				Tuple=[2, 2, 2]							,   _UpperCamelCase :				Union[str, Any]=[False, False, True]							,   _UpperCamelCase :				str=[0.0, 0.0, 0.0]							,   _UpperCamelCase :				Optional[Any]=0.02							,   _UpperCamelCase :				Optional[Any]=1e-12							,   _UpperCamelCase :				int=True							,   _UpperCamelCase :				Union[str, Any]=True							,   _UpperCamelCase :				Any=2							,   )      ->List[str]:
									snake_case_   =	parent
									snake_case_   =	batch_size
									snake_case_   =	image_size
									snake_case_   =	patch_sizes
									snake_case_   =	patch_stride
									snake_case_   =	patch_padding
									snake_case_   =	is_training
									snake_case_   =	use_labels
									snake_case_   =	num_labels
									snake_case_   =	num_channels
									snake_case_   =	embed_dim
									snake_case_   =	num_heads
									snake_case_   =	stride_kv
									snake_case_   =	depth
									snake_case_   =	cls_token
									snake_case_   =	attention_drop_rate
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps







			def 				snake_case__(     self :				List[Any]							)      ->int:
									snake_case_   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

									snake_case_   =	None
									if self.use_labels:
															# create a random int32 tensor of given shape
															snake_case_   =	ids_tensor([self.batch_size]							,   self.num_labels							)

									snake_case_   =	self.get_config()
									return config, pixel_values, labels







			def 				snake_case__(     self :				Tuple							)      ->Optional[Any]:
									return CvtConfig(
									    image_size=self.image_size							,   num_labels=self.num_labels							,   num_channels=self.num_channels							,   embed_dim=self.embed_dim							,   num_heads=self.num_heads							,   patch_sizes=self.patch_sizes							,   patch_padding=self.patch_padding							,   patch_stride=self.patch_stride							,   stride_kv=self.stride_kv							,   depth=self.depth							,   cls_token=self.cls_token							,   attention_drop_rate=self.attention_drop_rate							,   initializer_range=self.initializer_range							,   )







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Any							)      ->List[str]:
									snake_case_   =	TFCvtModel(config=lowercase_							)
									snake_case_   =	model(lowercase_							,   training=lowercase_							)
									snake_case_   =	(self.image_size, self.image_size)
									snake_case_,    snake_case_   =	image_size[0], image_size[1]
									for i in range(len(self.depth							)							):
															snake_case_   =	floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
															snake_case_   =	floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
									self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.embed_dim[-1], height, width)							)







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Optional[Any]							)      ->Optional[Any]:
									snake_case_   =	self.num_labels
									snake_case_   =	TFCvtForImageClassification(lowercase_							)
									snake_case_   =	model(lowercase_							,   labels=lowercase_							,   training=lowercase_							)
									self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)							)







			def 				snake_case__(     self :				str							)      ->List[str]:
									snake_case_   =	self.prepare_config_and_inputs()
									snake_case_,    snake_case_,    snake_case_   =	config_and_inputs
									snake_case_   =	{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_tf
class 	snake_case_   (		_lowerCamelCase	,       _lowerCamelCase	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   str									=				(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
			SCREAMING_SNAKE_CASE     :   List[Any]									=				(
			    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
			    if is_tf_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE     :   Tuple									=				False
			SCREAMING_SNAKE_CASE     :   Dict									=				False
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				False
			SCREAMING_SNAKE_CASE     :   Dict									=				False
			SCREAMING_SNAKE_CASE     :   int									=				False







			def 				snake_case__(     self :				Dict							)      ->Optional[Any]:
									snake_case_   =	TFCvtModelTester(self							)
									snake_case_   =	TFCvtConfigTester(self							,   config_class=lowercase_							,   has_text_modality=lowercase_							,   hidden_size=3_7							)







			def 				snake_case__(     self :				List[Any]							)      ->Tuple:
									self.config_tester.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







			@unittest.skip(reason='''Cvt does not output attentions'''							)
			def 				snake_case__(     self :				Any							)      ->Optional[int]:
									pass







			@unittest.skip(reason='''Cvt does not use inputs_embeds'''							)
			def 				snake_case__(     self :				Any							)      ->List[Any]:
									pass







			@unittest.skip(reason='''Cvt does not support input and output embeddings'''							)
			def 				snake_case__(     self :				int							)      ->List[str]:
									pass







			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''							)							) == 0							,   reason='''TF does not support backprop for grouped convolutions on CPU.'''							,   )
			def 				snake_case__(     self :				List[Any]							)      ->List[Any]:
									super().test_dataset_conversion()







			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''							)							) == 0							,   reason='''TF does not support backprop for grouped convolutions on CPU.'''							,   )
			@slow
			def 				snake_case__(     self :				Union[str, Any]							)      ->str:
									super().test_keras_fit()







			@unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'''							)
			def 				snake_case__(     self :				Any							)      ->Optional[Any]:
									snake_case_   =	tf.keras.mixed_precision.Policy('''mixed_float16'''							)
									tf.keras.mixed_precision.set_global_policy(lowercase_							)
									super().test_keras_fit()
									tf.keras.mixed_precision.set_global_policy('''float32'''							)







			def 				snake_case__(     self :				Tuple							)      ->Tuple:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_   =	model_class(lowercase_							)
															snake_case_   =	inspect.signature(model.call							)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case_   =	[*signature.parameters.keys()]

															snake_case_   =	['''pixel_values''']
															self.assertListEqual(arg_names[:1]							,   lowercase_							)







			def 				snake_case__(     self :				List[str]							)      ->Dict:
									def check_hidden_states_output(_UpperCamelCase :				Any							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				int							):
															snake_case_   =	model_class(lowercase_							)

															snake_case_   =	model(**self._prepare_for_class(lowercase_							,   lowercase_							)							)
															snake_case_   =	outputs.hidden_states

															snake_case_   =	len(self.model_tester.depth							)
															self.assertEqual(len(lowercase_							)							,   lowercase_							)

															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-3:]							)							,   [
															        self.model_tester.embed_dim[0],
															        self.model_tester.image_size // 4,
															        self.model_tester.image_size // 4,
															    ]							,   )

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_   =	True
															check_hidden_states_output(lowercase_							,   lowercase_							,   lowercase_							)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case_   =	True

															check_hidden_states_output(lowercase_							,   lowercase_							,   lowercase_							)







			def 				snake_case__(     self :				str							)      ->int:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_							)







			def 				snake_case__(     self :				List[str]							)      ->List[str]:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase_							)







			@slow
			def 				snake_case__(     self :				Optional[int]							)      ->Tuple:
									for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_   =	TFCvtModel.from_pretrained(lowercase_							)
															self.assertIsNotNone(lowercase_							)






def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
						return image





@require_tf
@require_vision
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			@cached_property
			def 				snake_case__(     self :				Any							)      ->str:
									return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)







			@slow
			def 				snake_case__(     self :				Optional[Any]							)      ->List[str]:
									snake_case_   =	TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

									snake_case_   =	self.default_image_processor
									snake_case_   =	prepare_img()
									snake_case_   =	image_processor(images=lowercase_							,   return_tensors='''tf'''							)

									# forward pass
									snake_case_   =	model(**lowercase_							)

									# verify the logits
									snake_case_   =	tf.TensorShape((1, 1_0_0_0)							)
									self.assertEqual(outputs.logits.shape							,   lowercase_							)

									snake_case_   =	tf.constant([0.9285, 0.9015, -0.3150]							)
									self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()							,   lowercase_							,   atol=1e-4							)							)





'''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 						a				:


											@staticmethod
											def   A_ (		*lowercase_    :					int       ,				**lowercase_    :					str					):
																pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						a				(   unittest.TestCase     ):
				snake_case_      =		MODEL_FOR_OBJECT_DETECTION_MAPPING


				def   A_ (		self    :					Any       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]					):
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				image_processor=lowercase_					)
									return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					int					):
									snake_case_									=				object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ,				threshold=0.0					)

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

									import datasets

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

									snake_case_									=				[
									    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'''],
									]
									snake_case_									=				object_detector(lowercase_       ,				threshold=0.0					)

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


				@require_tf
				@unittest.skip('''Object detection not implemented in TF'''					)
				def   A_ (		self    :					int					):
									pass


				@require_torch
				def   A_ (		self    :					Tuple					):
									snake_case_									=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

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

									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

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

									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					Optional[int]					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

									snake_case_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					Tuple					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

									snake_case_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					str					):
									snake_case_									=				0.9985
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,				threshold=lowercase_					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					Dict					):
									snake_case_									=				'''Narsil/layoutlmv3-finetuned-funsd'''
									snake_case_									=				0.9993

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_       ,				threshold=lowercase_					)

									snake_case_									=				object_detector(
									    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

