

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
			    TimesformerForVideoClassification,
			    TimesformerModel,
			)
			from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from transformers import VideoMAEImageProcessor






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			Optional[int]   ,   a				:			List[str]=13   ,   a				:			Optional[int]=10   ,   a				:			str=3   ,   a				:			int=2   ,   a				:			Optional[Any]=2   ,   a				:			Any=True   ,   a				:			Optional[int]=True   ,   a				:			List[Any]=32   ,   a				:			Optional[Any]=5   ,   a				:			str=4   ,   a				:			Optional[int]=37   ,   a				:			int="gelu"   ,   a				:			Dict=0.1   ,   a				:			Optional[Any]=0.1   ,   a				:			Dict=10   ,   a				:			List[Any]=0.02   ,   a				:			Dict="divided_space_time"   ,   a				:			Any=None   ,   )				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							parent
				SCREAMING_SNAKE_CASE :   str					  =							batch_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_size
				SCREAMING_SNAKE_CASE :   Any					  =							num_channels
				SCREAMING_SNAKE_CASE :   Dict					  =							patch_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							num_frames
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							is_training
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							use_labels
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_size
				SCREAMING_SNAKE_CASE :   int					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Any					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   str					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Dict					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   str					  =							attention_type
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Dict					  =							scope
				SCREAMING_SNAKE_CASE :   Dict					  =							num_labels

				# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
				SCREAMING_SNAKE_CASE :   List[Any]					  =							(image_size // patch_size) ** 2
				SCREAMING_SNAKE_CASE :   Dict					  =							(num_frames) * self.num_patches_per_frame + 1
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							floats_tensor(
				    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							None
				if self.use_labels:
					SCREAMING_SNAKE_CASE :   int					  =							ids_tensor([self.batch_size]   ,   self.num_labels						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.get_config()

				return config, pixel_values, labels
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							TimesformerConfig(
				    image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   num_frames=self.num_frames   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   initializer_range=self.initializer_range   ,   attention_type=self.attention_type   ,   )
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.num_labels
				return config
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Tuple   ,   a				:			List[Any]   ,   a				:			int						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							TimesformerModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   str					  =							model(a						)
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Tuple   ,   a				:			Dict   ,   a				:			Any						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							TimesformerForVideoClassification(a						)
				model.to(a						)
				model.eval()

				SCREAMING_SNAKE_CASE :   List[str]					  =							model(a						)

				# verify the logits shape
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.Size((self.batch_size, self.num_labels)						)
				self.parent.assertEqual(result.logits.shape   ,   a						)






			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							{"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
			lowerCamelCase__							     =(
			    {'feature-extraction': TimesformerModel, 'video-classification': TimesformerForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			def 				__UpperCamelCase			(      self				:			int						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							TimesformerModelTester(self						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							ConfigTester(
				    self   ,   config_class=a   ,   has_text_modality=a   ,   hidden_size=37						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			Union[str, Any]   ,   a				:			Dict   ,   a				:			Optional[Any]=False						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							copy.deepcopy(a						)

				if return_labels:
					if model_class in get_values(a						):
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.zeros(
						    self.model_tester.batch_size   ,   dtype=torch.long   ,   device=a						)

				return inputs_dict
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Optional[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			@unittest.skip(reason="TimeSformer does not use inputs_embeds"						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				pass
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   Dict					  =							model_class(a						)
					self.assertIsInstance(model.get_input_embeddings()   ,   (nn.Module)						)
					SCREAMING_SNAKE_CASE :   int					  =							model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(a   ,   nn.Linear						)						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   Any					  =							model_class(a						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							inspect.signature(model.forward						)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE :   str					  =							[*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE :   str					  =							["pixel_values"]
					self.assertListEqual(arg_names[:1]   ,   a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_video_classification(*a						)
			@slow
			def 				__UpperCamelCase			(      self				:			Any						)				->							List[Any]:

				"""simple docstring"""

				for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE :   List[str]					  =							TimesformerModel.from_pretrained(a						)
					self.assertIsNotNone(a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							List[str]:

				"""simple docstring"""

				if not self.has_attentions:
					pass

				else:
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True

					for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.model_tester.seq_length
						SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.num_frames

						SCREAMING_SNAKE_CASE :   int					  =							True
						SCREAMING_SNAKE_CASE :   str					  =							False
						SCREAMING_SNAKE_CASE :   str					  =							True
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model_class(a						)
						model.to(a						)
						model.eval()
						with torch.no_grad():
							SCREAMING_SNAKE_CASE :   Any					  =							model(**self._prepare_for_class(a   ,   a						)						)
						SCREAMING_SNAKE_CASE :   str					  =							outputs.attentions
						self.assertEqual(len(a						)   ,   self.model_tester.num_hidden_layers						)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						SCREAMING_SNAKE_CASE :   Dict					  =							True
						SCREAMING_SNAKE_CASE :   Any					  =							model_class(a						)
						model.to(a						)
						model.eval()
						with torch.no_grad():
							SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(**self._prepare_for_class(a   ,   a						)						)
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							outputs.attentions
						self.assertEqual(len(a						)   ,   self.model_tester.num_hidden_layers						)

						# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
						self.assertListEqual(
						    list(attentions[0].shape[-3:]						)   ,   [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]   ,   )
						SCREAMING_SNAKE_CASE :   List[Any]					  =							len(a						)

						# Check attention is always last and order is fine
						SCREAMING_SNAKE_CASE :   List[Any]					  =							True
						SCREAMING_SNAKE_CASE :   Any					  =							True
						SCREAMING_SNAKE_CASE :   int					  =							model_class(a						)
						model.to(a						)
						model.eval()
						with torch.no_grad():
							SCREAMING_SNAKE_CASE :   List[str]					  =							model(**self._prepare_for_class(a   ,   a						)						)

						self.assertEqual(out_len + 1   ,   len(a						)						)

						SCREAMING_SNAKE_CASE :   Tuple					  =							outputs.attentions

						self.assertEqual(len(a						)   ,   self.model_tester.num_hidden_layers						)

						# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:]						)   ,   [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]   ,   )






			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Dict:

				"""simple docstring"""

				def check_hidden_states_output(a				:			Tuple   ,   a				:			Dict   ,   a				:			Tuple						):
					SCREAMING_SNAKE_CASE :   str					  =							model_class(a						)
					model.to(a						)
					model.eval()

					with torch.no_grad():
						SCREAMING_SNAKE_CASE :   Any					  =							model(**self._prepare_for_class(a   ,   a						)						)

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							outputs.hidden_states
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.num_hidden_layers + 1
					self.assertEqual(len(a						)   ,   a						)

					SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.seq_length

					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]						)   ,   [seq_length, self.model_tester.hidden_size]   ,   )

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   Tuple					  =							True
					check_hidden_states_output(a   ,   a   ,   a						)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE :   Dict					  =							True

					check_hidden_states_output(a   ,   a   ,   a						)

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							hf_hub_download(
	    repo_id="hf-internal-testing/spaghetti-video"				,      filename="eating_spaghetti.npy"				,      repo_type="dataset")
	SCREAMING_SNAKE_CASE :   Any					  =							np.load(_a)
	return list(_a)






@require_torch
@require_vision
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@cached_property
			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

				return (
				    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,   image_std=[0.5, 0.5, 0.5]						)
				    if is_vision_available()
				    else None
				)






			@slow
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"						).to(
				    a						)

				SCREAMING_SNAKE_CASE :   Any					  =							self.default_image_processor
				SCREAMING_SNAKE_CASE :   str					  =							prepare_video()
				SCREAMING_SNAKE_CASE :   Dict					  =							image_processor(video[:8]   ,   return_tensors="pt"						).to(a						)

				# forward pass
				with torch.no_grad():
					SCREAMING_SNAKE_CASE :   int					  =							model(**a						)

				# verify the logits
				SCREAMING_SNAKE_CASE :   str					  =							torch.Size((1, 400)						)
				self.assertEqual(outputs.logits.shape   ,   a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([-0.3016, -0.7713, -0.4205]						).to(a						)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,   a   ,   atol=1e-4						)						)


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 _UpperCamelCase				:
						'''simple docstring'''







						@staticmethod
						def 				__UpperCamelCase			(      *a				:			str   ,   **a				:			int						)				->							str:

							"""simple docstring"""

							pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MODEL_FOR_OBJECT_DETECTION_MAPPING
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			Optional[Any]   ,   a				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ObjectDetectionPipeline(model=a   ,   image_processor=a						)
				return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, 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

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

				SCREAMING_SNAKE_CASE :   Dict					  =							[
				    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"],
				]
				SCREAMING_SNAKE_CASE :   Tuple					  =							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 				__UpperCamelCase			(      self				:			Optional[int]						)				->							str:

				"""simple docstring"""

				pass
			@require_torch
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							"hf-internal-testing/tiny-detr-mobilenetsv3"

				SCREAMING_SNAKE_CASE :   Dict					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

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

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

				SCREAMING_SNAKE_CASE :   Dict					  =							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": 0.3376, "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 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   int					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   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}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   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": 0.9982, "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 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Tuple					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   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}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        [
				            {"score": 0.9982, "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 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							0.9985
				SCREAMING_SNAKE_CASE :   int					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   List[str]					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   str					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"   ,   threshold=a						)
				self.assertEqual(
				    nested_simplify(a   ,   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 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							"Narsil/layoutlmv3-finetuned-funsd"
				SCREAMING_SNAKE_CASE :   Dict					  =							0.9993

				SCREAMING_SNAKE_CASE :   str					  =							pipeline("object-detection"   ,   model=a   ,   threshold=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							object_detector(
				    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"						)
				self.assertEqual(
				    nested_simplify(a   ,   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}},
				    ]   ,   )