



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 							lowerCAmelCase__  (	unittest.TestCase						):







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







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

											else:
																	A__	:							Tuple						    = []
																	for image in image_inputs:
																							A__    ,       A__	:							Optional[int]						    = self.get_expected_values([image])
																							expected_values.append((expected_height, expected_width))
																	A__	:							Optional[int]						    = max(_A			,		key=lambda _A: item[0])[0]
																	A__	:							str						    = max(_A			,		key=lambda _A: item[1])[1]

											return expected_height, expected_width




@require_torch
@require_vision
class 							lowerCAmelCase__  (	snake_case_					,							unittest.TestCase						):
					__A     :    List[Any]           =						DetaImageProcessor if is_vision_available() else None







					def  _lowercase			(       self      :     List[str]):
											A__	:							Any						    = DetaImageProcessingTester(self)







					@property
					def  _lowercase			(       self      :     Union[str, Any]):
											return self.image_processor_tester.prepare_image_processor_dict()







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							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  _lowercase			(       self      :     Dict):
											A__	:							str						    = 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			,		_A)







					def  _lowercase			(       self      :     List[Any]):
											pass







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

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

											A__    ,       A__	:							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
											A__    ,       A__	:							Any						    = self.image_processor_tester.get_expected_values(_A			,		batched=_A)

											A__	:							Any						    = 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  _lowercase			(       self      :     Optional[Any]):
											# Initialize image_processing
											A__	:							Any						    = self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											A__	:							Optional[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
											A__	:							Any						    = image_processing(image_inputs[0]			,		return_tensors="pt").pixel_values

											A__    ,       A__	:							Dict						    = 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
											A__	:							Optional[int]						    = image_processing(_A			,		return_tensors="pt").pixel_values

											A__    ,       A__	:							Optional[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,
											    )			,		)







					def  _lowercase			(       self      :     str):
											# Initialize image_processing
											A__	:							Optional[int]						    = self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__	:							Tuple						    = 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
											A__	:							Any						    = image_processing(image_inputs[0]			,		return_tensors="pt").pixel_values

											A__    ,       A__	:							Dict						    = 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
											A__	:							Union[str, Any]						    = image_processing(_A			,		return_tensors="pt").pixel_values

											A__    ,       A__	:							Optional[int]						    = 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  _lowercase			(       self      :     Optional[Any]):
											# prepare image and target
											A__	:							Dict						    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
											with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"			,		"r") as f:
																	A__	:							int						    = json.loads(f.read())

											A__	:							Dict						    = {"image_id": 3_9769, "annotations": target}

											# encode them
											A__	:							Optional[int]						    = DetaImageProcessor()
											A__	:							Tuple						    = image_processing(images=_A			,		annotations=_A			,		return_tensors="pt")

											# verify pixel values
											A__	:							Union[str, Any]						    = torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding["pixel_values"].shape			,		_A)

											A__	:							str						    = torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,		_A			,		atol=1e-4))

											# verify area
											A__	:							Optional[Any]						    = torch.tensor([58_87.96_00, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,		_A))
											# verify boxes
											A__	:							int						    = torch.Size([6, 4])
											self.assertEqual(encoding["labels"][0]["boxes"].shape			,		_A)
											A__	:							Optional[Any]						    = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,		_A			,		atol=1e-3))
											# verify image_id
											A__	:							List[str]						    = torch.tensor([3_9769])
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,		_A))
											# verify is_crowd
											A__	:							Optional[Any]						    = torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,		_A))
											# verify class_labels
											A__	:							Dict						    = torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,		_A))
											# verify orig_size
											A__	:							Optional[int]						    = torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,		_A))
											# verify size
											A__	:							Any						    = torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,		_A))







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

											A__	:							List[str]						    = {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

											A__	:							str						    = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

											# encode them
											A__	:							Optional[int]						    = DetaImageProcessor(format="coco_panoptic")
											A__	:							Optional[int]						    = image_processing(images=_A			,		annotations=_A			,		masks_path=_A			,		return_tensors="pt")

											# verify pixel values
											A__	:							Optional[int]						    = torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding["pixel_values"].shape			,		_A)

											A__	:							List[Any]						    = torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,		_A			,		atol=1e-4))

											# verify area
											A__	:							str						    = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 58_79.65_62, 76_34.11_47])
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,		_A))
											# verify boxes
											A__	:							str						    = torch.Size([6, 4])
											self.assertEqual(encoding["labels"][0]["boxes"].shape			,		_A)
											A__	:							Dict						    = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,		_A			,		atol=1e-3))
											# verify image_id
											A__	:							Union[str, Any]						    = torch.tensor([3_9769])
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,		_A))
											# verify is_crowd
											A__	:							Any						    = torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,		_A))
											# verify class_labels
											A__	:							Optional[Any]						    = torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,		_A))
											# verify masks
											A__	:							List[Any]						    = 82_2873
											self.assertEqual(encoding["labels"][0]["masks"].sum().item()			,		_A)
											# verify orig_size
											A__	:							List[Any]						    = torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,		_A))
											# verify size
											A__	:							Dict						    = torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,		_A))





from typing import TYPE_CHECKING

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


snake_case    :						str  =						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						['AlbertTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						['AlbertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						[
		    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AlbertForMaskedLM',
		    'AlbertForMultipleChoice',
		    'AlbertForPreTraining',
		    'AlbertForQuestionAnswering',
		    'AlbertForSequenceClassification',
		    'AlbertForTokenClassification',
		    'AlbertModel',
		    'AlbertPreTrainedModel',
		    'load_tf_weights_in_albert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Union[str, Any]  =						[
		    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAlbertForMaskedLM',
		    'TFAlbertForMultipleChoice',
		    'TFAlbertForPreTraining',
		    'TFAlbertForQuestionAnswering',
		    'TFAlbertForSequenceClassification',
		    'TFAlbertForTokenClassification',
		    'TFAlbertMainLayer',
		    'TFAlbertModel',
		    'TFAlbertPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						[
		    'FlaxAlbertForMaskedLM',
		    'FlaxAlbertForMultipleChoice',
		    'FlaxAlbertForPreTraining',
		    'FlaxAlbertForQuestionAnswering',
		    'FlaxAlbertForSequenceClassification',
		    'FlaxAlbertForTokenClassification',
		    'FlaxAlbertModel',
		    'FlaxAlbertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert import AlbertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert_fast import AlbertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_albert import (
				    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				    AlbertPreTrainedModel,
				    load_tf_weights_in_albert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_albert import (
				    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAlbertForMaskedLM,
				    TFAlbertForMultipleChoice,
				    TFAlbertForPreTraining,
				    TFAlbertForQuestionAnswering,
				    TFAlbertForSequenceClassification,
				    TFAlbertForTokenClassification,
				    TFAlbertMainLayer,
				    TFAlbertModel,
				    TFAlbertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				    FlaxAlbertPreTrainedModel,
				)
else:
		import sys

		snake_case    :						Any  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)