
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax
			import jax.numpy as jnp

			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)
			from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
			import torch





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=14				,				_UpperCamelCase=7				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=False				,				_UpperCamelCase=True				,				_UpperCamelCase=99				,				_UpperCamelCase=32				,				_UpperCamelCase=4				,				_UpperCamelCase=4				,				_UpperCamelCase=4				,				_UpperCamelCase=37				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=0.02				,				)				->      List[str]:
							lowerCAmelCase_						=	parent
							lowerCAmelCase_						=	batch_size
							lowerCAmelCase_						=	seq_length
							lowerCAmelCase_						=	is_training
							lowerCAmelCase_						=	use_input_mask
							lowerCAmelCase_						=	use_token_type_ids
							lowerCAmelCase_						=	use_labels
							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	rotary_dim
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	None
							lowerCAmelCase_						=	vocab_size - 1
							lowerCAmelCase_						=	vocab_size - 1
							lowerCAmelCase_						=	vocab_size - 1




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size     )

							lowerCAmelCase_						=	None
							if self.use_input_mask:
										lowerCAmelCase_						=	random_attention_mask([self.batch_size, self.seq_length]     )

							lowerCAmelCase_						=	GPTJConfig(
							    vocab_size=self.vocab_size				,				n_embd=self.hidden_size				,				n_layer=self.num_hidden_layers				,				n_head=self.num_attention_heads				,				n_positions=self.max_position_embeddings				,				use_cache=_UpperCamelCase				,				bos_token_id=self.bos_token_id				,				eos_token_id=self.eos_token_id				,				pad_token_id=self.pad_token_id				,				rotary_dim=self.rotary_dim				,				)

							return (config, input_ids, input_mask)




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	config_and_inputs
							lowerCAmelCase_						=	{"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Dict:
							lowerCAmelCase_						=	20
							lowerCAmelCase_						=	model_class_name(_UpperCamelCase     )

							lowerCAmelCase_						=	model.init_cache(input_ids.shape[0]				,				_UpperCamelCase     )
							lowerCAmelCase_						=	jnp.ones((input_ids.shape[0], max_decoder_length)				,				dtype="i4"     )

							lowerCAmelCase_						=	jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1     )[None, :]				,				(input_ids.shape[0], input_ids.shape[-1] - 1)     )
							lowerCAmelCase_						=	model(
							    input_ids[:, :-1]				,				attention_mask=_UpperCamelCase				,				past_key_values=_UpperCamelCase				,				position_ids=_UpperCamelCase				,				)

							lowerCAmelCase_						=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,				dtype="i4"     )
							lowerCAmelCase_						=	model(
							    input_ids[:, -1:]				,				attention_mask=_UpperCamelCase				,				past_key_values=outputs_cache.past_key_values				,				position_ids=_UpperCamelCase				,				)

							lowerCAmelCase_						=	model(_UpperCamelCase     )

							lowerCAmelCase_						=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
							self.parent.assertTrue(diff < 1e-3				,				msg=f"""Max diff is {diff}"""     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	20
							lowerCAmelCase_						=	model_class_name(_UpperCamelCase     )

							lowerCAmelCase_						=	jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]				,				axis=-1				,				)

							lowerCAmelCase_						=	model.init_cache(input_ids.shape[0]				,				_UpperCamelCase     )
							lowerCAmelCase_						=	jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1     )[None, :]				,				(input_ids.shape[0], input_ids.shape[-1] - 1)     )

							lowerCAmelCase_						=	model(
							    input_ids[:, :-1]				,				attention_mask=_UpperCamelCase				,				past_key_values=_UpperCamelCase				,				position_ids=_UpperCamelCase				,				)
							lowerCAmelCase_						=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,				dtype="i4"     )
							lowerCAmelCase_						=	model(
							    input_ids[:, -1:]				,				past_key_values=outputs_cache.past_key_values				,				attention_mask=_UpperCamelCase				,				position_ids=_UpperCamelCase				,				)

							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase     )

							lowerCAmelCase_						=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
							self.parent.assertTrue(diff < 1e-3				,				msg=f"""Max diff is {diff}"""     )





@require_flax
class 	_lowerCAmelCase					(	__a					,      __a					,      unittest.TestCase       ):
				_lowercase					=(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
				_lowercase					=(FlaxGPTJForCausalLM,) if is_flax_available() else ()




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	FlaxGPTJModelTester(self     )




				def      __a						(					self     )				->      Any:
							for model_class_name in self.all_model_classes:
										lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							for model_class_name in self.all_model_classes:
										lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward_with_attn_mask(
										    _UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )




				@tooslow
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	GPTaTokenizer.from_pretrained("gpt2"				,				pad_token="<|endoftext|>"				,				padding_side="left"     )
							lowerCAmelCase_						=	tokenizer(["Hello this is a long string", "Hey"]				,				return_tensors="np"				,				padding=_UpperCamelCase				,				truncation=_UpperCamelCase     )

							lowerCAmelCase_						=	FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"     )
							lowerCAmelCase_						=	False
							lowerCAmelCase_						=	model.config.eos_token_id

							lowerCAmelCase_						=	jax.jit(model.generate     )

							lowerCAmelCase_						=	jit_generate(
							    inputs["input_ids"]				,				attention_mask=inputs["attention_mask"]				,				pad_token_id=tokenizer.pad_token_id     ).sequences

							lowerCAmelCase_						=	tokenizer.batch_decode(_UpperCamelCase				,				skip_special_tokens=_UpperCamelCase     )

							lowerCAmelCase_						=	[
							    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
							    "Hey, I'm a little late to the party. I'm going to",
							]

							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				@is_pt_flax_cross_test
				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_	,  lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__     ):
													# prepare inputs
													lowerCAmelCase_						=	self._prepare_for_class(_UpperCamelCase				,				_UpperCamelCase     )
													lowerCAmelCase_						=	{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													lowerCAmelCase_						=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
													lowerCAmelCase_						=	getattr(_UpperCamelCase				,				_UpperCamelCase     )

													lowerCAmelCase_	,  lowerCAmelCase_						=	pt_inputs["input_ids"].shape
													lowerCAmelCase_						=	np.random.randint(0				,				seq_length - 1				,				size=(batch_size,)     )
													for batch_idx, start_index in enumerate(_UpperCamelCase     ):
																lowerCAmelCase_						=	0
																lowerCAmelCase_						=	1
																lowerCAmelCase_						=	0
																lowerCAmelCase_						=	1
													lowerCAmelCase_						=	pt_model_class(_UpperCamelCase     ).eval()
													lowerCAmelCase_						=	model_class(_UpperCamelCase				,				dtype=jnp.floataa     )

													lowerCAmelCase_						=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,				_UpperCamelCase     )
													lowerCAmelCase_						=	fx_state

													with torch.no_grad():
																lowerCAmelCase_						=	pt_model(**_UpperCamelCase     ).to_tuple()

													lowerCAmelCase_						=	fx_model(**_UpperCamelCase     ).to_tuple()
													self.assertEqual(len(_UpperCamelCase     )				,				len(_UpperCamelCase     )				,				"Output lengths differ between Flax and PyTorch"     )
													for fx_output, pt_output in zip(_UpperCamelCase				,				_UpperCamelCase     ):
																self.assert_almost_equals(fx_output[:, -1]				,				pt_output[:, -1].numpy()				,				4e-2     )

													with tempfile.TemporaryDirectory() as tmpdirname:
																pt_model.save_pretrained(_UpperCamelCase     )
																lowerCAmelCase_						=	model_class.from_pretrained(_UpperCamelCase				,				from_pt=_UpperCamelCase     )

													lowerCAmelCase_						=	fx_model_loaded(**_UpperCamelCase     ).to_tuple()
													self.assertEqual(
													    len(_UpperCamelCase     )				,				len(_UpperCamelCase     )				,				"Output lengths differ between Flax and PyTorch"     )
													for fx_output_loaded, pt_output in zip(_UpperCamelCase				,				_UpperCamelCase     ):
																self.assert_almost_equals(fx_output_loaded[:, -1]				,				pt_output[:, -1].numpy()				,				4e-2     )




				@is_pt_flax_cross_test
				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_	,  lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__     ):
													# prepare inputs
													lowerCAmelCase_						=	self._prepare_for_class(_UpperCamelCase				,				_UpperCamelCase     )
													lowerCAmelCase_						=	{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													lowerCAmelCase_						=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
													lowerCAmelCase_						=	getattr(_UpperCamelCase				,				_UpperCamelCase     )

													lowerCAmelCase_						=	pt_model_class(_UpperCamelCase     ).eval()
													lowerCAmelCase_						=	model_class(_UpperCamelCase				,				dtype=jnp.floataa     )

													lowerCAmelCase_						=	load_flax_weights_in_pytorch_model(_UpperCamelCase				,				fx_model.params     )
													lowerCAmelCase_	,  lowerCAmelCase_						=	pt_inputs["input_ids"].shape
													lowerCAmelCase_						=	np.random.randint(0				,				seq_length - 1				,				size=(batch_size,)     )
													for batch_idx, start_index in enumerate(_UpperCamelCase     ):
																lowerCAmelCase_						=	0
																lowerCAmelCase_						=	1
																lowerCAmelCase_						=	0
																lowerCAmelCase_						=	1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
																lowerCAmelCase_						=	pt_model(**_UpperCamelCase     ).to_tuple()

													lowerCAmelCase_						=	fx_model(**_UpperCamelCase     ).to_tuple()
													self.assertEqual(len(_UpperCamelCase     )				,				len(_UpperCamelCase     )				,				"Output lengths differ between Flax and PyTorch"     )
													for fx_output, pt_output in zip(_UpperCamelCase				,				_UpperCamelCase     ):
																self.assert_almost_equals(fx_output[:, -1]				,				pt_output[:, -1].numpy()				,				4e-2     )

													with tempfile.TemporaryDirectory() as tmpdirname:
																fx_model.save_pretrained(_UpperCamelCase     )
																lowerCAmelCase_						=	pt_model_class.from_pretrained(_UpperCamelCase				,				from_flax=_UpperCamelCase     )

													with torch.no_grad():
																lowerCAmelCase_						=	pt_model_loaded(**_UpperCamelCase     ).to_tuple()

													self.assertEqual(
													    len(_UpperCamelCase     )				,				len(_UpperCamelCase     )				,				"Output lengths differ between Flax and PyTorch"     )
													for fx_output, pt_output in zip(_UpperCamelCase				,				_UpperCamelCase     ):
																self.assert_almost_equals(fx_output[:, -1]				,				pt_output[:, -1].numpy()				,				4e-2     )




				@tooslow
				def      __a						(					self     )				->      Dict:
							for model_class_name in self.all_model_classes:
										lowerCAmelCase_						=	model_class_name.from_pretrained("EleutherAI/gpt-j-6B"     )
										lowerCAmelCase_						=	model(np.ones((1, 1)     )     )
										self.assertIsNotNone(_UpperCamelCase     )





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 	_lowerCAmelCase					:




							@staticmethod
							def      __a						(					*_UpperCamelCase				,				**_UpperCamelCase     )				->      str:
										pass





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					=MODEL_FOR_OBJECT_DETECTION_MAPPING




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				image_processor=_UpperCamelCase     )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"				,				threshold=0.0     )

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

							import datasets

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

							lowerCAmelCase_						=	[
							    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_						=	object_detector(_UpperCamelCase				,				threshold=0.0     )

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




				@require_tf
				@unittest.skip("Object detection not implemented in TF"     )
				def      __a						(					self     )				->      Optional[int]:
							pass




				@require_torch
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"hf-internal-testing/tiny-detr-mobilenetsv3"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

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

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

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

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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[Any]:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

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

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

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

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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     )				->      Optional[Any]:
							lowerCAmelCase_						=	0.9985
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=_UpperCamelCase     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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     )				->      List[str]:
							lowerCAmelCase_						=	"Narsil/layoutlmv3-finetuned-funsd"
							lowerCAmelCase_						=	0.9993

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase				,				threshold=_UpperCamelCase     )

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



