

"""simple docstring"""





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			["vqvae"]







					def __init__(			self     :							List[Any]  ,lowercase_     :							AutoencoderKL  ,lowercase_     :							UNetaDConditionModel  ,lowercase_     :							Mel  ,lowercase_     :							Union[DDIMScheduler, DDPMScheduler]  ,):
						super().__init__()
						self.register_modules(unet=lowercase_  ,scheduler=lowercase_  ,mel=lowercase_  ,vqvae=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						return 5_0 if isinstance(self.scheduler  ,lowercase_						) else 1_0_0_0







					@torch.no_grad()
					def __call__(			self     :							List[str]  ,lowercase_     :							int = 1  ,lowercase_     :							str = None  ,lowercase_     :							np.ndarray = None  ,lowercase_     :							int = 0  ,lowercase_     :							int = 0  ,lowercase_     :							int = None  ,lowercase_     :							torch.Generator = None  ,lowercase_     :							float = 0  ,lowercase_     :							float = 0  ,lowercase_     :							torch.Generator = None  ,lowercase_     :							float = 0  ,lowercase_     :							torch.Tensor = None  ,lowercase_     :							torch.Tensor = None  ,lowercase_     :							str=True  ,):
						lowerCAmelCase__      :		Dict    							=      steps or self.get_default_steps()
						self.scheduler.set_timesteps(lowercase_						)
						lowerCAmelCase__      :		int    							=      step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size						) == int:
							lowerCAmelCase__      :		Any    							=      (self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
							lowerCAmelCase__      :		Optional[Any]    							=      randn_tensor(
							    (
							        batch_size,
							        self.unet.config.in_channels,
							        self.unet.config.sample_size[0],
							        self.unet.config.sample_size[1],
							    )  ,generator=lowercase_  ,device=self.device  ,)
						lowerCAmelCase__      :		Optional[Any]    							=      noise
						lowerCAmelCase__      :		Optional[Any]    							=      None

						if audio_file is not None or raw_audio is not None:
							self.mel.load_audio(lowercase_  ,lowercase_						)
							lowerCAmelCase__      :		Tuple    							=      self.mel.audio_slice_to_image(lowercase_						)
							lowerCAmelCase__      :		Optional[int]    							=      np.frombuffer(input_image.tobytes()  ,dtype='''uint8'''						).reshape(
							    (input_image.height, input_image.width)						)
							lowerCAmelCase__      :		Dict    							=      (input_image / 2_5_5) * 2 - 1
							lowerCAmelCase__      :		Optional[Any]    							=      torch.tensor(input_image[np.newaxis, :, :]  ,dtype=torch.float						).to(self.device						)

							if self.vqvae is not None:
								lowerCAmelCase__      :		Union[str, Any]    							=      self.vqvae.encode(torch.unsqueeze(lowercase_  ,0						)						).latent_dist.sample(
								    generator=lowercase_						)[0]
								lowerCAmelCase__      :		int    							=      self.vqvae.config.scaling_factor * input_images

							if start_step > 0:
								lowerCAmelCase__      :		List[str]    							=      self.scheduler.add_noise(lowercase_  ,lowercase_  ,self.scheduler.timesteps[start_step - 1]						)

							lowerCAmelCase__      :		str    							=      (
							    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
							)
							lowerCAmelCase__      :		Any    							=      int(mask_start_secs * pixels_per_second						)
							lowerCAmelCase__      :		Optional[int]    							=      int(mask_end_secs * pixels_per_second						)
							lowerCAmelCase__      :		List[Any]    							=      self.scheduler.add_noise(lowercase_  ,lowercase_  ,torch.tensor(self.scheduler.timesteps[start_step:]						)						)

						for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
							if isinstance(self.unet  ,lowercase_						):
								lowerCAmelCase__      :		Optional[int]    							=      self.unet(lowercase_  ,lowercase_  ,lowercase_						)['''sample''']
							else:
								lowerCAmelCase__      :		Dict    							=      self.unet(lowercase_  ,lowercase_						)['''sample''']

							if isinstance(self.scheduler  ,lowercase_						):
								lowerCAmelCase__      :		str    							=      self.scheduler.step(
								    model_output=lowercase_  ,timestep=lowercase_  ,sample=lowercase_  ,eta=lowercase_  ,generator=lowercase_  ,)['''prev_sample''']
							else:
								lowerCAmelCase__      :		Dict    							=      self.scheduler.step(
								    model_output=lowercase_  ,timestep=lowercase_  ,sample=lowercase_  ,generator=lowercase_  ,)['''prev_sample''']

							if mask is not None:
								if mask_start > 0:
									lowerCAmelCase__      :		Tuple    							=      mask[:, step, :, :mask_start]
								if mask_end > 0:
									lowerCAmelCase__      :		Union[str, Any]    							=      mask[:, step, :, -mask_end:]

						if self.vqvae is not None:
							# 0.18215 was scaling factor used in training to ensure unit variance
							lowerCAmelCase__      :		Tuple    							=      1 / self.vqvae.config.scaling_factor * images
							lowerCAmelCase__      :		Dict    							=      self.vqvae.decode(lowercase_						)['''sample''']

						lowerCAmelCase__      :		str    							=      (images / 2 + 0.5).clamp(0  ,1						)
						lowerCAmelCase__      :		Any    							=      images.cpu().permute(0  ,2  ,3  ,1						).numpy()
						lowerCAmelCase__      :		Union[str, Any]    							=      (images * 2_5_5).round().astype('''uint8'''						)
						lowerCAmelCase__      :		Dict    							=      list(
						    (Image.fromarray(_[:, :, 0]						) for _ in images)
						    if images.shape[3] == 1
						    else (Image.fromarray(lowercase_  ,mode='''RGB'''						).convert('''L'''						) for _ in images)						)

						lowerCAmelCase__      :		Any    							=      [self.mel.image_to_audio(lowercase_						) for _ in images]
						if not return_dict:
							return images, (self.mel.get_sample_rate(), audios)

						return BaseOutput(**AudioPipelineOutput(np.array(lowercase_						)[:, np.newaxis, :]						)  ,**ImagePipelineOutput(lowercase_						)						)







					@torch.no_grad()
					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							List[Image.Image]  ,lowercase_     :							int = 5_0						):
						assert isinstance(self.scheduler  ,lowercase_						)
						self.scheduler.set_timesteps(lowercase_						)
						lowerCAmelCase__      :		List[str]    							=      np.array(
						    [np.frombuffer(image.tobytes()  ,dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
						lowerCAmelCase__      :		List[str]    							=      (sample / 2_5_5) * 2 - 1
						lowerCAmelCase__      :		int    							=      torch.Tensor(lowercase_						).to(self.device						)

						for t in self.progress_bar(torch.flip(self.scheduler.timesteps  ,(0,)						)						):
							lowerCAmelCase__      :		Tuple    							=      t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
							lowerCAmelCase__      :		Optional[Any]    							=      self.scheduler.alphas_cumprod[t]
							lowerCAmelCase__      :		Tuple    							=      (
							    self.scheduler.alphas_cumprod[prev_timestep]
							    if prev_timestep >= 0
							    else self.scheduler.final_alpha_cumprod
							)
							lowerCAmelCase__      :		Union[str, Any]    							=      1 - alpha_prod_t
							lowerCAmelCase__      :		int    							=      self.unet(lowercase_  ,lowercase_						)['''sample''']
							lowerCAmelCase__      :		Tuple    							=      (1 - alpha_prod_t_prev) ** 0.5 * model_output
							lowerCAmelCase__      :		Any    							=      (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
							lowerCAmelCase__      :		Union[str, Any]    							=      sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

						return sample







					@staticmethod
					def 			__lowerCAmelCase					(			lowercase_     :							torch.Tensor  ,lowercase_     :							torch.Tensor  ,lowercase_     :							float						):
						lowerCAmelCase__      :		Optional[int]    							=      acos(torch.dot(torch.flatten(lowercase_						)  ,torch.flatten(lowercase_						)						) / torch.norm(lowercase_						) / torch.norm(lowercase_						)						)
						return sin((1 - alpha) * theta						) * xa / sin(lowercase_						) + sin(alpha * theta						) * xa / sin(lowercase_						)







"""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 							SCREAMING_SNAKE_CASE   :






												"""simple docstring"""



												@staticmethod
												def 			__lowerCAmelCase					(			*lowercase_     :							Dict  ,**lowercase_     :							str						):
													pass







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






					"""simple docstring"""



					lowercase__												=			MODEL_FOR_OBJECT_DETECTION_MAPPING







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







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							str  ,lowercase_     :							str						):
						lowerCAmelCase__      :		List[str]    							=      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

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

						lowerCAmelCase__      :		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'''],
						]
						lowerCAmelCase__      :		Optional[Any]    							=      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 			__lowerCAmelCase					(			self     :							Optional[int]						):
						pass







					@require_torch
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''hf-internal-testing/tiny-detr-mobilenetsv3'''

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

						lowerCAmelCase__      :		Any    							=      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''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      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''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        ],
						        [
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''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     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Any    							=      AutoModelForObjectDetection.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      AutoFeatureExtractor.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Any    							=      ObjectDetectionPipeline(model=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      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''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Tuple    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Tuple    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		Any    							=      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''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      0.9985
						lowerCAmelCase__      :		Dict    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		List[Any]    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		int    							=      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''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''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     :							str						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''Narsil/layoutlmv3-finetuned-funsd'''
						lowerCAmelCase__      :		List[Any]    							=      0.9993

						lowerCAmelCase__      :		Any    							=      pipeline('''object-detection'''  ,model=lowercase_  ,threshold=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      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''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						    ]  ,)




