



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 ConditionalDetrImageProcessor
class 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						Optional[int],					__lowercase					:						int,					__lowercase					:						List[str]=7,					__lowercase					:						List[Any]=3,					__lowercase					:						int=30,					__lowercase					:						str=400,					__lowercase					:						Any=True,					__lowercase					:						int=None,					__lowercase					:						Dict=True,					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					__lowercase					:						int=True,					__lowercase					:						Optional[Any]=1 / 255,					__lowercase					:						Optional[Any]=True,					):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							lowercase__					  =				size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				num_channels
							lowercase__					  =				min_resolution
							lowercase__					  =				max_resolution
							lowercase__					  =				do_resize
							lowercase__					  =				size
							lowercase__					  =				do_normalize
							lowercase__					  =				image_mean
							lowercase__					  =				image_std
							lowercase__					  =				do_rescale
							lowercase__					  =				rescale_factor
							lowercase__					  =				do_pad




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

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

							return expected_height, expected_width
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Any     		=ConditionalDetrImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				ConditionalDetrImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						List[Any]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"image_mean"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_std"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_resize"						)						)
							self.assertTrue(hasattr(__lowercase,					"size"						)						)




				def 							A__      (						self					:						Any						):
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size,					{"shortest_edge": 18, "longest_edge": 1333}						)
							self.assertEqual(image_processor.do_pad,					__lowercase						)

							lowercase__					  =				self.image_processing_class.from_dict(
							    self.image_processor_dict,					size=42,					max_size=84,					pad_and_return_pixel_mask=__lowercase						)
							self.assertEqual(image_processor.size,					{"shortest_edge": 42, "longest_edge": 84}						)
							self.assertEqual(image_processor.do_pad,					__lowercase						)




				def 							A__      (						self					:						List[str]						):
							pass




				def 							A__      (						self					:						List[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

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

							lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase						)

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

							# Test batched
							lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase,					batched=__lowercase						)

							lowercase__					  =				image_processing(__lowercase,					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 							A__      (						self					:						List[str]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					numpify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					np.ndarray						)

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

							lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase						)

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

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values

							lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase,					batched=__lowercase						)

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




				def 							A__      (						self					:						List[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					torchify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					torch.Tensor						)

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

							lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase						)

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

							# Test batched
							lowercase__					  =				image_processing(__lowercase,					return_tensors="pt"						).pixel_values

							lowercase__  ,       lowercase__					  =				self.image_processor_tester.get_expected_values(__lowercase,					batched=__lowercase						)

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




				@slow
				def 							A__      (						self					:						int						):
							# prepare image and target
							lowercase__					  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt",					"r"						) as f:
										lowercase__					  =				json.loads(f.read()						)

							lowercase__					  =				{"image_id": 3_9769, "annotations": target}

							# encode them
							lowercase__					  =				ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"						)
							lowercase__					  =				image_processing(images=__lowercase,					annotations=__lowercase,					return_tensors="pt"						)

							# verify pixel values
							lowercase__					  =				torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding["pixel_values"].shape,					__lowercase						)

							lowercase__					  =				torch.tensor([0.2796, 0.3138, 0.3481]						)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],					__lowercase,					atol=1e-4						)						)

							# verify area
							lowercase__					  =				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"],					__lowercase						)						)
							# verify boxes
							lowercase__					  =				torch.Size([6, 4]						)
							self.assertEqual(encoding["labels"][0]["boxes"].shape,					__lowercase						)
							lowercase__					  =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],					__lowercase,					atol=1e-3						)						)
							# verify image_id
							lowercase__					  =				torch.tensor([3_9769]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],					__lowercase						)						)
							# verify is_crowd
							lowercase__					  =				torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],					__lowercase						)						)
							# verify class_labels
							lowercase__					  =				torch.tensor([75, 75, 63, 65, 17, 17]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],					__lowercase						)						)
							# verify orig_size
							lowercase__					  =				torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],					__lowercase						)						)
							# verify size
							lowercase__					  =				torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"],					__lowercase						)						)




				@slow
				def 							A__      (						self					:						Optional[int]						):
							# prepare image, target and masks_path
							lowercase__					  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt",					"r"						) as f:
										lowercase__					  =				json.loads(f.read()						)

							lowercase__					  =				{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

							# encode them
							lowercase__					  =				ConditionalDetrImageProcessor(format="coco_panoptic"						)
							lowercase__					  =				image_processing(images=__lowercase,					annotations=__lowercase,					masks_path=__lowercase,					return_tensors="pt"						)

							# verify pixel values
							lowercase__					  =				torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding["pixel_values"].shape,					__lowercase						)

							lowercase__					  =				torch.tensor([0.2796, 0.3138, 0.3481]						)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],					__lowercase,					atol=1e-4						)						)

							# verify area
							lowercase__					  =				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"],					__lowercase						)						)
							# verify boxes
							lowercase__					  =				torch.Size([6, 4]						)
							self.assertEqual(encoding["labels"][0]["boxes"].shape,					__lowercase						)
							lowercase__					  =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],					__lowercase,					atol=1e-3						)						)
							# verify image_id
							lowercase__					  =				torch.tensor([3_9769]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],					__lowercase						)						)
							# verify is_crowd
							lowercase__					  =				torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],					__lowercase						)						)
							# verify class_labels
							lowercase__					  =				torch.tensor([17, 17, 63, 75, 75, 93]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],					__lowercase						)						)
							# verify masks
							lowercase__					  =				82_2873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item(),					__lowercase						)
							# verify orig_size
							lowercase__					  =				torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],					__lowercase						)						)
							# verify size
							lowercase__					  =				torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"],					__lowercase						)						)









from typing import TYPE_CHECKING

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


lowercase_								=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaPreLayerNormForCausalLM""",
							    """RobertaPreLayerNormForMaskedLM""",
							    """RobertaPreLayerNormForMultipleChoice""",
							    """RobertaPreLayerNormForQuestionAnswering""",
							    """RobertaPreLayerNormForSequenceClassification""",
							    """RobertaPreLayerNormForTokenClassification""",
							    """RobertaPreLayerNormModel""",
							    """RobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaPreLayerNormForCausalLM""",
							    """TFRobertaPreLayerNormForMaskedLM""",
							    """TFRobertaPreLayerNormForMultipleChoice""",
							    """TFRobertaPreLayerNormForQuestionAnswering""",
							    """TFRobertaPreLayerNormForSequenceClassification""",
							    """TFRobertaPreLayerNormForTokenClassification""",
							    """TFRobertaPreLayerNormMainLayer""",
							    """TFRobertaPreLayerNormModel""",
							    """TFRobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """FlaxRobertaPreLayerNormForCausalLM""",
							    """FlaxRobertaPreLayerNormForMaskedLM""",
							    """FlaxRobertaPreLayerNormForMultipleChoice""",
							    """FlaxRobertaPreLayerNormForQuestionAnswering""",
							    """FlaxRobertaPreLayerNormForSequenceClassification""",
							    """FlaxRobertaPreLayerNormForTokenClassification""",
							    """FlaxRobertaPreLayerNormModel""",
							    """FlaxRobertaPreLayerNormPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




