


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__       :




         '''simple docstring'''


         @staticmethod
         def  _lowerCamelCase (						*__lowerCamelCase ,						**__lowerCamelCase)			->				Union[str, Any]:
                pass







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




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= MODEL_FOR_OBJECT_DETECTION_MAPPING


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[Any]:
            _A      :       Dict				    = ObjectDetectionPipeline(model=__lowerCamelCase ,						image_processor=__lowerCamelCase)
            return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Tuple:
            _A      :       Optional[int]				    = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" ,						threshold=0.0)

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

            import datasets

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

            _A      :       str				    = [
                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"],
            ]
            _A      :       str				    = object_detector(__lowerCamelCase ,						threshold=0.0)

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


     @require_tf
     @unittest.skip("Object detection not implemented in TF")
     def  _lowerCamelCase (						self)			->				List[Any]:
            pass


     @require_torch
     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[Any]				    = "hf-internal-testing/tiny-detr-mobilenetsv3"

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

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

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

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

            self.assertEqual(
                nested_simplify(__lowerCamelCase ,						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  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       List[Any]				    = "facebook/detr-resnet-50"

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

            _A      :       Optional[Any]				    = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
            self.assertEqual(
                nested_simplify(__lowerCamelCase ,						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}},
                ] ,						)

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

            _A      :       str				    = pipeline("object-detection" ,						model=__lowerCamelCase)

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

            _A      :       List[Any]				    = object_detector(
                [
                    "http://images.cocodataset.org/val2017/000000039769.jpg",
                    "http://images.cocodataset.org/val2017/000000039769.jpg",
                ])
            self.assertEqual(
                nested_simplify(__lowerCamelCase ,						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  _lowerCamelCase (						self)			->				int:
            _A      :       Any				    = 0.9_9_8_5
            _A      :       List[str]				    = "facebook/detr-resnet-50"

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

            _A      :       List[Any]				    = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" ,						threshold=__lowerCamelCase)
            self.assertEqual(
                nested_simplify(__lowerCamelCase ,						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  _lowerCamelCase (						self)			->				Tuple:
            _A      :       str				    = "Narsil/layoutlmv3-finetuned-funsd"
            _A      :       Optional[Any]				    = 0.9_9_9_3

            _A      :       Any				    = pipeline("object-detection" ,						model=__lowerCamelCase ,						threshold=__lowerCamelCase)

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




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            _A      :       Optional[Any]				    = (batch_size, self.unet.config.in_channels // 2, height, width)
            _A      :       str				    = next(self.unet.parameters()).dtype

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _A      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

            for t in self.progress_bar(__lowerCamelCase):
                   # concat latents and low resolution image in the channel dimension.
                   _A      :       List[Any]				    = torch.cat([latents, image] ,						dim=1)
                   _A      :       str				    = self.scheduler.scale_model_input(__lowerCamelCase ,						__lowerCamelCase)
                   # predict the noise residual
                   _A      :       Any				    = self.unet(__lowerCamelCase ,						__lowerCamelCase).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   _A      :       Optional[int]				    = self.scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase).prev_sample

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
