



'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DeformableDetrImageProcessor






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						int      ,						a_   :						Union[str, Any]      ,						a_   :						Union[str, Any]=7      ,						a_   :						List[Any]=3      ,						a_   :						Tuple=3_0      ,						a_   :						Optional[int]=4_0_0      ,						a_   :						List[Any]=True      ,						a_   :						Optional[int]=None      ,						a_   :						List[Any]=True      ,						a_   :						Union[str, Any]=[0.5, 0.5, 0.5]      ,						a_   :						int=[0.5, 0.5, 0.5]      ,						a_   :						Any=True      ,						a_   :						int=1 / 2_5_5      ,						a_   :						List[str]=True      ,						)			->					int:
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										snake_case:    Tuple      							=size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
										snake_case:    str      							=parent
										snake_case:    str      							=batch_size
										snake_case:    str      							=num_channels
										snake_case:    List[str]      							=min_resolution
										snake_case:    str      							=max_resolution
										snake_case:    Optional[Any]      							=do_resize
										snake_case:    Optional[Any]      							=size
										snake_case:    List[str]      							=do_normalize
										snake_case:    Optional[int]      							=image_mean
										snake_case:    List[str]      							=image_std
										snake_case:    Dict      							=do_rescale
										snake_case:    List[str]      							=rescale_factor
										snake_case:    int      							=do_pad


							def    UpperCamelCase     (				self   :						List[str]			)			->					Optional[int]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}




							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Union[str, Any]      ,						a_   :						Optional[Any]=False			)			->					List[Any]:
										if not batched:
													snake_case:    str      							=image_inputs[0]
													if isinstance(a_      ,						Image.Image			):
																snake_case:    Any      							=image.size
													else:
																snake_case:    List[Any]      							=image.shape[1], image.shape[2]
													if w < h:
																snake_case:    List[Any]      							=int(self.size['shortest_edge'] * h / w			)
																snake_case:    List[Any]      							=self.size['shortest_edge']
													elif w > h:
																snake_case:    int      							=self.size['shortest_edge']
																snake_case:    Tuple      							=int(self.size['shortest_edge'] * w / h			)
													else:
																snake_case:    List[str]      							=self.size['shortest_edge']
																snake_case:    Union[str, Any]      							=self.size['shortest_edge']

										else:
													snake_case:    Dict      							=[]
													for image in image_inputs:
																snake_case:    List[Any]      							=self.get_expected_values([image]			)
																expected_values.append((expected_height, expected_width)			)
													snake_case:    Union[str, Any]      							=max(a_      ,						key=lambda a_			: item[0]			)[0]
													snake_case:    List[str]      							=max(a_      ,						key=lambda a_			: item[1]			)[1]

										return expected_height, expected_width






@require_torch
@require_vision
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		str        =				DeformableDetrImageProcessor if is_vision_available() else None


							def    UpperCamelCase     (				self   :						List[str]			)			->					List[str]:
										snake_case:    List[Any]      							=DeformableDetrImageProcessingTester(self			)


							@property
							def    UpperCamelCase     (				self   :						Dict			)			->					int:
										return self.image_processor_tester.prepare_image_processor_dict()


							def    UpperCamelCase     (				self   :						List[Any]			)			->					Optional[Any]:
										snake_case:    Any      							=self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(a_      ,						'image_mean'			)			)
										self.assertTrue(hasattr(a_      ,						'image_std'			)			)
										self.assertTrue(hasattr(a_      ,						'do_normalize'			)			)
										self.assertTrue(hasattr(a_      ,						'do_resize'			)			)
										self.assertTrue(hasattr(a_      ,						'do_rescale'			)			)
										self.assertTrue(hasattr(a_      ,						'do_pad'			)			)
										self.assertTrue(hasattr(a_      ,						'size'			)			)


							def    UpperCamelCase     (				self   :						Any			)			->					List[str]:
										snake_case:    int      							=self.image_processing_class.from_dict(self.image_processor_dict			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}			)
										self.assertEqual(image_processor.do_pad      ,						a_			)

										snake_case:    Tuple      							=self.image_processing_class.from_dict(
										    self.image_processor_dict      ,						size=4_2      ,						max_size=8_4      ,						pad_and_return_pixel_mask=a_			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 4_2, 'longest_edge': 8_4}			)
										self.assertEqual(image_processor.do_pad      ,						a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										pass


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					int:
										# Initialize image_processing
										snake_case:    int      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										snake_case:    Optional[Any]      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						Image.Image			)

										# Test not batched input
										snake_case:    str      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case:    Union[str, Any]      							=self.image_processor_tester.get_expected_values(a_			)

										self.assertEqual(
										    encoded_images.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

										# Test batched
										snake_case:    Optional[Any]      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)

										snake_case:    List[str]      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,						)


							def    UpperCamelCase     (				self   :						Any			)			->					List[Any]:
										# Initialize image_processing
										snake_case:    Union[str, Any]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										snake_case:    List[Any]      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_      ,						numpify=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						np.ndarray			)

										# Test not batched input
										snake_case:    Optional[int]      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case:    Optional[int]      							=self.image_processor_tester.get_expected_values(a_			)

										self.assertEqual(
										    encoded_images.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

										# Test batched
										snake_case:    Union[str, Any]      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case:    str      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)

										self.assertEqual(
										    encoded_images.shape      ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,						)


							def    UpperCamelCase     (				self   :						List[str]			)			->					Optional[Any]:
										# Initialize image_processing
										snake_case:    Union[str, Any]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										snake_case:    List[str]      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_      ,						torchify=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						torch.Tensor			)

										# Test not batched input
										snake_case:    Dict      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case:    int      							=self.image_processor_tester.get_expected_values(a_			)

										self.assertEqual(
										    encoded_images.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

										# Test batched
										snake_case:    List[str]      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case:    List[Any]      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)

										self.assertEqual(
										    encoded_images.shape      ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,						)


							@slow
							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[str]:
										# prepare image and target
										snake_case:    Dict      							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
										with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,						'r'			) as f:
													snake_case:    Union[str, Any]      							=json.loads(f.read()			)

										snake_case:    Optional[Any]      							={'image_id': 3_9_7_6_9, 'annotations': target}

										# encode them
										snake_case:    str      							=DeformableDetrImageProcessor()
										snake_case:    List[str]      							=image_processing(images=a_      ,						annotations=a_      ,						return_tensors='pt'			)

										# verify pixel values
										snake_case:    Dict      							=torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
										self.assertEqual(encoding['pixel_values'].shape      ,						a_			)

										snake_case:    List[str]      							=torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
										self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,						a_      ,						atol=1E-4			)			)

										# verify area
										snake_case:    Tuple      							=torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,						a_			)			)
										# verify boxes
										snake_case:    Tuple      							=torch.Size([6, 4]			)
										self.assertEqual(encoding['labels'][0]['boxes'].shape      ,						a_			)
										snake_case:    Any      							=torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,						a_      ,						atol=1E-3			)			)
										# verify image_id
										snake_case:    Any      							=torch.tensor([3_9_7_6_9]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,						a_			)			)
										# verify is_crowd
										snake_case:    str      							=torch.tensor([0, 0, 0, 0, 0, 0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,						a_			)			)
										# verify class_labels
										snake_case:    Any      							=torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,						a_			)			)
										# verify orig_size
										snake_case:    List[Any]      							=torch.tensor([4_8_0, 6_4_0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,						a_			)			)
										# verify size
										snake_case:    Dict      							=torch.tensor([8_0_0, 1_0_6_6]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,						a_			)			)




							@slow
							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										# prepare image, target and masks_path
										snake_case:    Any      							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
										with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,						'r'			) as f:
													snake_case:    Dict      							=json.loads(f.read()			)

										snake_case:    int      							={'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

										snake_case:    Dict      							=pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

										# encode them
										snake_case:    Tuple      							=DeformableDetrImageProcessor(format='coco_panoptic'			)
										snake_case:    int      							=image_processing(images=a_      ,						annotations=a_      ,						masks_path=a_      ,						return_tensors='pt'			)

										# verify pixel values
										snake_case:    Tuple      							=torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
										self.assertEqual(encoding['pixel_values'].shape      ,						a_			)

										snake_case:    List[Any]      							=torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
										self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,						a_      ,						atol=1E-4			)			)

										# verify area
										snake_case:    str      							=torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,						a_			)			)
										# verify boxes
										snake_case:    Dict      							=torch.Size([6, 4]			)
										self.assertEqual(encoding['labels'][0]['boxes'].shape      ,						a_			)
										snake_case:    Optional[int]      							=torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,						a_      ,						atol=1E-3			)			)
										# verify image_id
										snake_case:    Union[str, Any]      							=torch.tensor([3_9_7_6_9]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,						a_			)			)
										# verify is_crowd
										snake_case:    Optional[Any]      							=torch.tensor([0, 0, 0, 0, 0, 0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,						a_			)			)
										# verify class_labels
										snake_case:    Optional[int]      							=torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,						a_			)			)
										# verify masks
										snake_case:    str      							=8_2_2_8_7_3
										self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,						a_			)
										# verify orig_size
										snake_case:    Optional[Any]      							=torch.tensor([4_8_0, 6_4_0]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,						a_			)			)
										# verify size
										snake_case:    Optional[int]      							=torch.tensor([8_0_0, 1_0_6_6]			)
										self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,						a_			)			)










"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """dpr"""
			def __init__(    self    , lowercase=30522    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-12    , lowercase=0    , lowercase="absolute"    , lowercase = 0    , **lowercase    , ):
							super().__init__(pad_token_id=lowercase    , **lowercase    )

							_lowerCamelCase	:     int       =       vocab_size
							_lowerCamelCase	:     Optional[int]       =       hidden_size
							_lowerCamelCase	:     str       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     Tuple       =       hidden_act
							_lowerCamelCase	:     int       =       intermediate_size
							_lowerCamelCase	:     str       =       hidden_dropout_prob
							_lowerCamelCase	:     Tuple       =       attention_probs_dropout_prob
							_lowerCamelCase	:     Union[str, Any]       =       max_position_embeddings
							_lowerCamelCase	:     str       =       type_vocab_size
							_lowerCamelCase	:     int       =       initializer_range
							_lowerCamelCase	:     Optional[int]       =       layer_norm_eps
							_lowerCamelCase	:     Dict       =       projection_dim
							_lowerCamelCase	:     int       =       position_embedding_type