




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 DetaImageProcessor




class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def __init__(   self       :  Dict      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Any=7      ,   __lowerCamelCase       :  List[Any]=3      ,   __lowerCamelCase       :  int=30      ,   __lowerCamelCase       :  int=4_00      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  Tuple=None      ,   __lowerCamelCase       :  Optional[int]=True      ,   __lowerCamelCase       :  Optional[Any]=[0.5, 0.5, 0.5]      ,   __lowerCamelCase       :  Optional[Any]=[0.5, 0.5, 0.5]      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  List[str]=1 / 2_55      ,   __lowerCamelCase       :  Optional[int]=True      ,   )      ->  str:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							a					=    size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
							a					=    parent
							a					=    batch_size
							a					=    num_channels
							a					=    min_resolution
							a					=    max_resolution
							a					=    do_resize
							a					=    size
							a					=    do_normalize
							a					=    image_mean
							a					=    image_std
							a					=    do_rescale
							a					=    rescale_factor
							a					=    do_pad





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							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       :  Union[str, Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  str=False							)      ->  List[str]:
							if not batched:
										a					=    image_inputs[0]
										if isinstance(__lowerCamelCase      ,   Image.Image							):
													a					,	a					=    image.size
										else:
													a					,	a					=    image.shape[1], image.shape[2]
										if w < h:
													a					=    int(self.size["shortest_edge"] * h / w							)
													a					=    self.size["shortest_edge"]
										elif w > h:
													a					=    self.size["shortest_edge"]
													a					=    int(self.size["shortest_edge"] * w / h							)
										else:
													a					=    self.size["shortest_edge"]
													a					=    self.size["shortest_edge"]

							else:
										a					=    []
										for image in image_inputs:
													a					,	a					=    self.get_expected_values([image]							)
													expected_values.append((expected_height, expected_width)							)
										a					=    max(__lowerCamelCase      ,   key=lambda __lowerCamelCase							: item[0]							)[0]
										a					=    max(__lowerCamelCase      ,   key=lambda __lowerCamelCase							: item[1]							)[1]

							return expected_height, expected_width





@require_torch
@require_vision
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Tuple											=      DetaImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					=    DetaImageProcessingTester(self							)





				@property
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							a					=    self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_mean"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_std"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_normalize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_resize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_rescale"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_pad"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "size"							)							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Union[str, Any]:
							a					=    self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size      ,   {"shortest_edge": 18, "longest_edge": 13_33}							)
							self.assertEqual(image_processor.do_pad      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  int:
							pass





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Any:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   Image.Image							)

							# Test not batched input
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

							# Test batched
							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

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

							# Test not batched input
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							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[str]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase      ,   torchify=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   torch.Tensor							)

							# Test not batched input
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							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       :  Any							)      ->  List[Any]:
							# prepare image and target
							a					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,   "r"							) as f:
										a					=    json.loads(f.read()							)

							a					=    {"image_id": 3_97_69, "annotations": target}

							# encode them
							a					=    DetaImageProcessor()
							a					=    image_processing(images=__lowerCamelCase      ,   annotations=__lowerCamelCase      ,   return_tensors="pt"							)

							# verify pixel values
							a					=    torch.Size([1, 3, 8_00, 10_66]							)
							self.assertEqual(encoding["pixel_values"].shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)





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

							a					=    {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

							a					=    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"							)

							# encode them
							a					=    DetaImageProcessor(format="coco_panoptic"							)
							a					=    image_processing(images=__lowerCamelCase      ,   annotations=__lowerCamelCase      ,   masks_path=__lowerCamelCase      ,   return_tensors="pt"							)

							# verify pixel values
							a					=    torch.Size([1, 3, 8_00, 10_66]							)
							self.assertEqual(encoding["pixel_values"].shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify masks
							a					=    82_28_73
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,   __lowerCamelCase							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase	:       Dict     =	{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	['BlipImageProcessor']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       List[Any]     =	[
		    'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlipModel',
		    'BlipPreTrainedModel',
		    'BlipForConditionalGeneration',
		    'BlipForQuestionAnswering',
		    'BlipVisionModel',
		    'BlipTextModel',
		    'BlipForImageTextRetrieval',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Any     =	[
		    'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBlipModel',
		    'TFBlipPreTrainedModel',
		    'TFBlipForConditionalGeneration',
		    'TFBlipForQuestionAnswering',
		    'TFBlipVisionModel',
		    'TFBlipTextModel',
		    'TFBlipForImageTextRetrieval',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		__lowerCAmelCase	:       List[str]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






