



"""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 DetrImageProcessor
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							Any						,     snake_case:							Optional[Any]						,     snake_case:							List[str]=7						,     snake_case:							Optional[Any]=3						,     snake_case:							int=30						,     snake_case:							str=400						,     snake_case:							str=True						,     snake_case:							List[str]=None						,     snake_case:							Union[str, Any]=True						,     snake_case:							List[str]=1 / 255						,     snake_case:							Optional[int]=True						,     snake_case:							Dict=[0.5, 0.5, 0.5]						,     snake_case:							Optional[int]=[0.5, 0.5, 0.5]						,     snake_case:							Union[str, Any]=True						,     )   ->	Any:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											snake_case_ :str						=			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
											snake_case_ :List[Any]						=			parent
											snake_case_ :str						=			batch_size
											snake_case_ :List[Any]						=			num_channels
											snake_case_ :Dict						=			min_resolution
											snake_case_ :Optional[int]						=			max_resolution
											snake_case_ :Optional[int]						=			do_resize
											snake_case_ :Union[str, Any]						=			size
											snake_case_ :Optional[int]						=			do_rescale
											snake_case_ :List[Any]						=			rescale_factor
											snake_case_ :int						=			do_normalize
											snake_case_ :int						=			image_mean
											snake_case_ :str						=			image_std
											snake_case_ :List[str]						=			do_pad






					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_pad": self.do_pad,
											}






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

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

											return expected_height, expected_width

@require_torch
@require_vision
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     DetrImageProcessor if is_vision_available() else None






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Union[str, Any]:
											snake_case_ :Optional[Any]						=			DetrImageProcessingTester(self						)






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






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Any:
											snake_case_ :List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case						,     """image_mean"""						)						)
											self.assertTrue(hasattr(snake_case						,     """image_std"""						)						)
											self.assertTrue(hasattr(snake_case						,     """do_normalize"""						)						)
											self.assertTrue(hasattr(snake_case						,     """do_rescale"""						)						)
											self.assertTrue(hasattr(snake_case						,     """rescale_factor"""						)						)
											self.assertTrue(hasattr(snake_case						,     """do_resize"""						)						)
											self.assertTrue(hasattr(snake_case						,     """size"""						)						)
											self.assertTrue(hasattr(snake_case						,     """do_pad"""						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[int]:
											snake_case_ :Union[str, Any]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,     {"""shortest_edge""": 18, """longest_edge""": 1_333}						)
											self.assertEqual(image_processor.do_pad						,     snake_case						)

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






					def    lowerCAmelCase_				(       self:							Any						)   ->	List[str]:
											pass






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

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

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

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

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

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

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

											snake_case_,       snake_case_ :Any						=			self.image_processor_tester.get_expected_values(snake_case						)

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

											# Test batched
											snake_case_ :Optional[int]						=			image_processing(snake_case						,     return_tensors="""pt"""						).pixel_values

											snake_case_,       snake_case_ :List[str]						=			self.image_processor_tester.get_expected_values(snake_case						,     batched=snake_case						)

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






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

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

											snake_case_,       snake_case_ :str						=			self.image_processor_tester.get_expected_values(snake_case						)

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

											# Test batched
											snake_case_ :Optional[int]						=			image_processing(snake_case						,     return_tensors="""pt"""						).pixel_values

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

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






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

											snake_case_ :int						=			{"""image_id""": 39_769, """annotations""": target}

											# encode them
											snake_case_ :Optional[int]						=			DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""						)
											snake_case_ :List[Any]						=			image_processing(images=snake_case						,     annotations=snake_case						,     return_tensors="""pt"""						)

											# verify pixel values
											snake_case_ :Any						=			torch.Size([1, 3, 800, 1_066]						)
											self.assertEqual(encoding["""pixel_values"""].shape						,     snake_case						)

											snake_case_ :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]						,     snake_case						,     atol=1E-4						)						)

											# verify area
											snake_case_ :Optional[Any]						=			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"""]						,     snake_case						)						)
											# verify boxes
											snake_case_ :List[str]						=			torch.Size([6, 4]						)
											self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,     snake_case						)
											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]						,     snake_case						,     atol=1E-3						)						)
											# verify image_id
											snake_case_ :Union[str, Any]						=			torch.tensor([39_769]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,     snake_case						)						)
											# verify is_crowd
											snake_case_ :List[str]						=			torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,     snake_case						)						)
											# verify class_labels
											snake_case_ :List[str]						=			torch.tensor([75, 75, 63, 65, 17, 17]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,     snake_case						)						)
											# verify orig_size
											snake_case_ :List[str]						=			torch.tensor([480, 640]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,     snake_case						)						)
											# verify size
											snake_case_ :str						=			torch.tensor([800, 1_066]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,     snake_case						)						)






					@slow
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Optional[int]:
											# 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_ :Optional[int]						=			json.loads(f.read()						)

											snake_case_ :Optional[Any]						=			{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

											snake_case_ :Tuple						=			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

											# encode them
											snake_case_ :Optional[Any]						=			DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""						)
											snake_case_ :Union[str, Any]						=			image_processing(images=snake_case						,     annotations=snake_case						,     masks_path=snake_case						,     return_tensors="""pt"""						)

											# verify pixel values
											snake_case_ :int						=			torch.Size([1, 3, 800, 1_066]						)
											self.assertEqual(encoding["""pixel_values"""].shape						,     snake_case						)

											snake_case_ :Dict						=			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]						,     snake_case						,     atol=1E-4						)						)

											# verify area
											snake_case_ :Tuple						=			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"""]						,     snake_case						)						)
											# verify boxes
											snake_case_ :str						=			torch.Size([6, 4]						)
											self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,     snake_case						)
											snake_case_ :Tuple						=			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]						,     snake_case						,     atol=1E-3						)						)
											# verify image_id
											snake_case_ :Optional[Any]						=			torch.tensor([39_769]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,     snake_case						)						)
											# verify is_crowd
											snake_case_ :Union[str, Any]						=			torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,     snake_case						)						)
											# verify class_labels
											snake_case_ :Dict						=			torch.tensor([17, 17, 63, 75, 75, 93]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,     snake_case						)						)
											# verify masks
											snake_case_ :Dict						=			822_873
											self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						,     snake_case						)
											# verify orig_size
											snake_case_ :Dict						=			torch.tensor([480, 640]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,     snake_case						)						)
											# verify size
											snake_case_ :List[str]						=			torch.tensor([800, 1_066]						)
											self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,     snake_case						)						)





"""simple docstring"""
from typing import TYPE_CHECKING

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


__a      =				{
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["MobileBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MobileBertForMaskedLM",
						    "MobileBertForMultipleChoice",
						    "MobileBertForNextSentencePrediction",
						    "MobileBertForPreTraining",
						    "MobileBertForQuestionAnswering",
						    "MobileBertForSequenceClassification",
						    "MobileBertForTokenClassification",
						    "MobileBertLayer",
						    "MobileBertModel",
						    "MobileBertPreTrainedModel",
						    "load_tf_weights_in_mobilebert",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMobileBertForMaskedLM",
						    "TFMobileBertForMultipleChoice",
						    "TFMobileBertForNextSentencePrediction",
						    "TFMobileBertForPreTraining",
						    "TFMobileBertForQuestionAnswering",
						    "TFMobileBertForSequenceClassification",
						    "TFMobileBertForTokenClassification",
						    "TFMobileBertMainLayer",
						    "TFMobileBertModel",
						    "TFMobileBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mobilebert import (
						    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileBertConfig,
						    MobileBertOnnxConfig,
						)
						from .tokenization_mobilebert import MobileBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mobilebert_fast import MobileBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilebert import (
												    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileBertForMaskedLM,
												    MobileBertForMultipleChoice,
												    MobileBertForNextSentencePrediction,
												    MobileBertForPreTraining,
												    MobileBertForQuestionAnswering,
												    MobileBertForSequenceClassification,
												    MobileBertForTokenClassification,
												    MobileBertLayer,
												    MobileBertModel,
												    MobileBertPreTrainedModel,
												    load_tf_weights_in_mobilebert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilebert import (
												    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileBertForMaskedLM,
												    TFMobileBertForMultipleChoice,
												    TFMobileBertForNextSentencePrediction,
												    TFMobileBertForPreTraining,
												    TFMobileBertForQuestionAnswering,
												    TFMobileBertForSequenceClassification,
												    TFMobileBertForTokenClassification,
												    TFMobileBertMainLayer,
												    TFMobileBertModel,
												    TFMobileBertPreTrainedModel,
												)

else:
						import sys

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