

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


lowerCAmelCase_       	=	logging.get_logger(__name__)





class 	snake_case_   (		_lowerCamelCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["pixel_values"]







			def __init__(     self :				Optional[Any]							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				Dict[str, int] = None							,   _UpperCamelCase :				PILImageResampling = PILImageResampling.BILINEAR							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				Union[int, float] = 1 / 2_5_5							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				Dict[str, int] = None							,   _UpperCamelCase :				bool = True							,   **_UpperCamelCase :				Union[str, Any]							,   )      ->str:
									super().__init__(**lowercase_							)
									snake_case_   =	size if size is not None else {'''shortest_edge''': 2_2_4}
									snake_case_   =	get_size_dict(lowercase_							,   default_to_square=lowercase_							)
									snake_case_   =	crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
									snake_case_   =	get_size_dict(lowercase_							,   param_name='''crop_size'''							)

									snake_case_   =	do_resize
									snake_case_   =	size
									snake_case_   =	resample
									snake_case_   =	do_rescale
									snake_case_   =	rescale_factor
									snake_case_   =	do_center_crop
									snake_case_   =	crop_size
									snake_case_   =	do_flip_channel_order







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Dict[str, int]							,   _UpperCamelCase :				PILImageResampling = PIL.Image.BILINEAR							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							,   **_UpperCamelCase :				Optional[int]							,   )      ->List[Any]:
									snake_case_   =	get_size_dict(lowercase_							,   default_to_square=lowercase_							)
									if "shortest_edge" not in size:
															raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''							)
									snake_case_   =	get_resize_output_image_size(lowercase_							,   size=size['''shortest_edge''']							,   default_to_square=lowercase_							)
									return resize(lowercase_							,   size=lowercase_							,   resample=lowercase_							,   data_format=lowercase_							,   **lowercase_							)







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Dict[str, int]							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							,   **_UpperCamelCase :				Tuple							,   )      ->Union[str, Any]:
									snake_case_   =	get_size_dict(lowercase_							)
									if "height" not in size or "width" not in size:
															raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''							)
									return center_crop(lowercase_							,   size=(size['''height'''], size['''width'''])							,   data_format=lowercase_							,   **lowercase_							)







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Union[int, float]							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							,   **_UpperCamelCase :				int							,   )      ->int:
									return rescale(lowercase_							,   scale=lowercase_							,   data_format=lowercase_							,   **lowercase_							)







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				np.ndarray							,   _UpperCamelCase :				Optional[Union[str, ChannelDimension]] = None							)      ->Optional[int]:
									return flip_channel_order(lowercase_							,   data_format=lowercase_							)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				ImageInput							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				Dict[str, int] = None							,   _UpperCamelCase :				PILImageResampling = None							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				float = None							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				Dict[str, int] = None							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				Optional[Union[str, TensorType]] = None							,   _UpperCamelCase :				ChannelDimension = ChannelDimension.FIRST							,   **_UpperCamelCase :				Dict							,   )      ->Tuple:
									snake_case_   =	do_resize if do_resize is not None else self.do_resize
									snake_case_   =	resample if resample is not None else self.resample
									snake_case_   =	do_rescale if do_rescale is not None else self.do_rescale
									snake_case_   =	rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case_   =	do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case_   =	(
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									snake_case_   =	size if size is not None else self.size
									snake_case_   =	get_size_dict(lowercase_							,   default_to_square=lowercase_							)
									snake_case_   =	crop_size if crop_size is not None else self.crop_size
									snake_case_   =	get_size_dict(lowercase_							,   param_name='''crop_size'''							)

									snake_case_   =	make_list_of_images(lowercase_							)

									if not valid_images(lowercase_							):
															raise ValueError(
															    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
															    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

									if do_resize and size is None:
															raise ValueError('''Size must be specified if do_resize is True.'''							)

									if do_rescale and rescale_factor is None:
															raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

									if do_center_crop and crop_size is None:
															raise ValueError('''Crop size must be specified if do_center_crop is True.'''							)

									# All transformations expect numpy arrays.
									snake_case_   =	[to_numpy_array(lowercase_							) for image in images]

									if do_resize:
															snake_case_   =	[self.resize(image=lowercase_							,   size=lowercase_							,   resample=lowercase_							) for image in images]

									if do_center_crop:
															snake_case_   =	[self.center_crop(image=lowercase_							,   size=lowercase_							) for image in images]

									if do_rescale:
															snake_case_   =	[self.rescale(image=lowercase_							,   scale=lowercase_							) for image in images]

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
															snake_case_   =	[self.flip_channel_order(image=lowercase_							) for image in images]

									snake_case_   =	[to_channel_dimension_format(lowercase_							,   lowercase_							) for image in images]

									snake_case_   =	{'''pixel_values''': images}
									return BatchFeature(data=lowercase_							,   tensor_type=lowercase_							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				List[Tuple] = None							)      ->List[Any]:
									snake_case_   =	outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
															if len(lowercase_							) != len(lowercase_							):
																					raise ValueError(
																					    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''							)

															if is_torch_tensor(lowercase_							):
																					snake_case_   =	target_sizes.numpy()

															snake_case_   =	[]

															for idx in range(len(lowercase_							)							):
																					snake_case_   =	torch.nn.functional.interpolate(
																					    logits[idx].unsqueeze(dim=0							)							,   size=target_sizes[idx]							,   mode='''bilinear'''							,   align_corners=lowercase_							)
																					snake_case_   =	resized_logits[0].argmax(dim=0							)
																					semantic_segmentation.append(lowercase_							)
									else:
															snake_case_   =	logits.argmax(dim=1							)
															snake_case_   =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

									return semantic_segmentation





'''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 						a				:


											@staticmethod
											def   A_ (		*lowercase_    :					int       ,				**lowercase_    :					str					):
																pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						a				(   unittest.TestCase     ):
				snake_case_      =		MODEL_FOR_OBJECT_DETECTION_MAPPING


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


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					int					):
									snake_case_									=				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

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

									snake_case_									=				[
									    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'''],
									]
									snake_case_									=				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   A_ (		self    :					int					):
									pass


				@require_torch
				def   A_ (		self    :					Tuple					):
									snake_case_									=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				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''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									    ]       ,				)

									snake_case_									=				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''': 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[int]					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

									snake_case_									=				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''': 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    :					Tuple					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

									snake_case_									=				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''': 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					):
									snake_case_									=				0.9985
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				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''': 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    :					Dict					):
									snake_case_									=				'''Narsil/layoutlmv3-finetuned-funsd'''
									snake_case_									=				0.9993

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_       ,				threshold=lowercase_					)

									snake_case_									=				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''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
									        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
									    ]       ,				)

