





'''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       UpperCAmelCase :




					@staticmethod
					def 				UpperCAmelCase__   (*A__      :	Union[str, Any]							,			**A__      :	Optional[int]				)					-> Optional[int]:
									pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       UpperCAmelCase (						unittest.TestCase  ):
		UpperCAmelCase					:	List[Any] 			=		MODEL_FOR_OBJECT_DETECTION_MAPPING




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	int							,			A__      :	List[str]							,			A__      :	List[Any]				)					-> str:
						lowercase         = ObjectDetectionPipeline(model=A__							,			image_processor=A__				)
						return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	Union[str, Any]							,			A__      :	Tuple				)					-> Tuple:
						lowercase         = 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

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

						lowercase         = [
						    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				),
						    "http://images.cocodataset.org/val2017/000000039769.jpg",
						    # RGBA
						    dataset[0]["file"],
						    # LA
						    dataset[1]["file"],
						    # L
						    dataset[2]["file"],
						]
						lowercase         = object_detector(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]				)					-> List[str]:
						pass




		@require_torch
		def 				UpperCAmelCase__   (self      :	str				)					-> Union[str, Any]:
						lowercase         = "hf-internal-testing/tiny-detr-mobilenetsv3"

						lowercase         = AutoModelForObjectDetection.from_pretrained(A__				)
						lowercase         = AutoFeatureExtractor.from_pretrained(A__				)
						lowercase         = ObjectDetectionPipeline(model=A__							,			feature_extractor=A__				)

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

						self.assertEqual(
						    nested_simplify(A__							,			decimals=4				)							,			[
						        {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
						        {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
						    ]							,			)

						lowercase         = 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": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
						            {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
						        ],
						        [
						            {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
						            {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
						        ],
						    ]							,			)




		@require_torch
		@slow
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Any:
						lowercase         = "facebook/detr-resnet-50"

						lowercase         = AutoModelForObjectDetection.from_pretrained(A__				)
						lowercase         = AutoFeatureExtractor.from_pretrained(A__				)
						lowercase         = ObjectDetectionPipeline(model=A__							,			feature_extractor=A__				)

						lowercase         = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				)
						self.assertEqual(
						    nested_simplify(A__							,			decimals=4				)							,			[
						        {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
						        {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
						        {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
						        {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
						        {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
						    ]							,			)

						lowercase         = object_detector(
						    [
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						    ]				)
						self.assertEqual(
						    nested_simplify(A__							,			decimals=4				)							,			[
						        [
						            {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
						            {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
						            {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
						            {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
						            {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
						        ],
						        [
						            {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
						            {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
						            {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
						            {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
						            {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
						        ],
						    ]							,			)




		@require_torch
		@slow
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> int:
						lowercase         = "facebook/detr-resnet-50"

						lowercase         = pipeline("object-detection"							,			model=A__				)

						lowercase         = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				)
						self.assertEqual(
						    nested_simplify(A__							,			decimals=4				)							,			[
						        {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
						        {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
						        {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
						        {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
						        {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
						    ]							,			)

						lowercase         = object_detector(
						    [
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						    ]				)
						self.assertEqual(
						    nested_simplify(A__							,			decimals=4				)							,			[
						        [
						            {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
						            {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
						            {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
						            {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
						            {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
						        ],
						        [
						            {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
						            {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
						            {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
						            {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
						            {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
						        ],
						    ]							,			)




		@require_torch
		@slow
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> List[Any]:
						lowercase         = 0.9_9_8_5
						lowercase         = "facebook/detr-resnet-50"

						lowercase         = pipeline("object-detection"							,			model=A__				)

						lowercase         = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"							,			threshold=A__				)
						self.assertEqual(
						    nested_simplify(A__							,			decimals=4				)							,			[
						        {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
						        {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
						    ]							,			)





		@require_torch
		@require_pytesseract
		@slow
		def 				UpperCAmelCase__   (self      :	Any				)					-> str:
						lowercase         = "Narsil/layoutlmv3-finetuned-funsd"
						lowercase         = 0.9_9_9_3

						lowercase         = pipeline("object-detection"							,			model=A__							,			threshold=A__				)

						lowercase         = object_detector(
						    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"				)
						self.assertEqual(
						    nested_simplify(A__							,			decimals=4				)							,			[
						        {"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
						        {"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
						    ]							,			)







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




