



"""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     A__     (						unittest.TestCase							):




	'''simple docstring'''





	def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Any=7 ,    _SCREAMING_SNAKE_CASE:	Dict=3 ,    _SCREAMING_SNAKE_CASE:	Tuple=30 ,    _SCREAMING_SNAKE_CASE:	Dict=400 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=True ,    _SCREAMING_SNAKE_CASE:	str=None ,    _SCREAMING_SNAKE_CASE:	Any=True ,    _SCREAMING_SNAKE_CASE:	int=1 / 255 ,    _SCREAMING_SNAKE_CASE:	Any=True ,    _SCREAMING_SNAKE_CASE:	Any=[0.5, 0.5, 0.5] ,    _SCREAMING_SNAKE_CASE:	Any=[0.5, 0.5, 0.5] ,    _SCREAMING_SNAKE_CASE:	int=True ,    )       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
				__lowerCAmelCase   :						Dict				= parent
				__lowerCAmelCase   :						Dict				= batch_size
				__lowerCAmelCase   :						int				= num_channels
				__lowerCAmelCase   :						Dict				= min_resolution
				__lowerCAmelCase   :						Union[str, Any]				= max_resolution
				__lowerCAmelCase   :						Tuple				= do_resize
				__lowerCAmelCase   :						Optional[int]				= size
				__lowerCAmelCase   :						Dict				= do_rescale
				__lowerCAmelCase   :						Optional[int]				= rescale_factor
				__lowerCAmelCase   :						Dict				= do_normalize
				__lowerCAmelCase   :						List[str]				= image_mean
				__lowerCAmelCase   :						Optional[int]				= image_std
				__lowerCAmelCase   :						Any				= do_pad

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

				"""simple docstring"""

				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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Tuple=False)       -> str:

				"""simple docstring"""

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

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

				return expected_height, expected_width






@require_torch
@require_vision
class     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			DetrImageProcessor if is_vision_available() else None

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= DetrImageProcessingTester(self)

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Optional[Any]:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= self.image_processing_class(**self.image_processor_dict)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "image_mean"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "image_std"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "do_normalize"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "do_rescale"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "rescale_factor"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "do_resize"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "size"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,    "do_pad"))

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= 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 ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= self.image_processing_class.from_dict(
				    self.image_processor_dict ,    size=42 ,    max_size=84 ,    pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE)
				self.assertEqual(image_processor.size ,    {"shortest_edge": 42, "longest_edge": 84})
				self.assertEqual(image_processor.do_pad ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Any:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= self.image_processing_class(**self.image_processor_dict)
				# create random PIL images
				__lowerCAmelCase   :						int				= prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_SCREAMING_SNAKE_CASE)
				for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    Image.Image)

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

				__lowerCAmelCase		,			__lowerCAmelCase   :						int				= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE)

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

				# Test batched
				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,    batched=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= image_processing(_SCREAMING_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 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.image_processing_class(**self.image_processor_dict)
				# create random numpy tensors
				__lowerCAmelCase   :						Optional[Any]				= prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_SCREAMING_SNAKE_CASE ,    numpify=_SCREAMING_SNAKE_CASE)
				for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    np.ndarray)

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

				__lowerCAmelCase		,			__lowerCAmelCase   :						List[str]				= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE)

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

				# Test batched
				__lowerCAmelCase   :						List[str]				= image_processing(_SCREAMING_SNAKE_CASE ,    return_tensors="pt").pixel_values

				__lowerCAmelCase		,			__lowerCAmelCase   :						str				= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,    batched=_SCREAMING_SNAKE_CASE)

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= self.image_processing_class(**self.image_processor_dict)
				# create random PyTorch tensors
				__lowerCAmelCase   :						Any				= prepare_image_inputs(self.image_processor_tester ,    equal_resolution=_SCREAMING_SNAKE_CASE ,    torchify=_SCREAMING_SNAKE_CASE)
				for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    torch.Tensor)

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

				__lowerCAmelCase		,			__lowerCAmelCase   :						str				= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE)

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

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

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,    batched=_SCREAMING_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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
				with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,    "r") as f:
							__lowerCAmelCase   :						Tuple				= json.loads(f.read())

				__lowerCAmelCase   :						Optional[Any]				= {"image_id": 3_9769, "annotations": target}

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

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

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

				# verify area
				__lowerCAmelCase   :						List[str]				= torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,    _SCREAMING_SNAKE_CASE))
				# verify boxes
				__lowerCAmelCase   :						Optional[Any]				= torch.Size([6, 4])
				self.assertEqual(encoding["labels"][0]["boxes"].shape ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[Any]				= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-3))
				# verify image_id
				__lowerCAmelCase   :						Any				= torch.tensor([3_9769])
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,    _SCREAMING_SNAKE_CASE))
				# verify is_crowd
				__lowerCAmelCase   :						List[Any]				= torch.tensor([0, 0, 0, 0, 0, 0])
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,    _SCREAMING_SNAKE_CASE))
				# verify class_labels
				__lowerCAmelCase   :						Optional[Any]				= torch.tensor([75, 75, 63, 65, 17, 17])
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,    _SCREAMING_SNAKE_CASE))
				# verify orig_size
				__lowerCAmelCase   :						Tuple				= torch.tensor([480, 640])
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,    _SCREAMING_SNAKE_CASE))
				# verify size
				__lowerCAmelCase   :						Any				= torch.tensor([800, 1066])
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,    _SCREAMING_SNAKE_CASE))







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
				with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,    "r") as f:
							__lowerCAmelCase   :						Dict				= json.loads(f.read())

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

				__lowerCAmelCase   :						Optional[Any]				= pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

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

				# verify pixel values
				__lowerCAmelCase   :						Dict				= torch.Size([1, 3, 800, 1066])
				self.assertEqual(encoding["pixel_values"].shape ,    _SCREAMING_SNAKE_CASE)

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

				# verify area
				__lowerCAmelCase   :						List[Any]				= torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,    _SCREAMING_SNAKE_CASE))
				# verify boxes
				__lowerCAmelCase   :						Union[str, Any]				= torch.Size([6, 4])
				self.assertEqual(encoding["labels"][0]["boxes"].shape ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Union[str, Any]				= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-3))
				# verify image_id
				__lowerCAmelCase   :						List[Any]				= torch.tensor([3_9769])
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,    _SCREAMING_SNAKE_CASE))
				# verify is_crowd
				__lowerCAmelCase   :						List[Any]				= torch.tensor([0, 0, 0, 0, 0, 0])
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,    _SCREAMING_SNAKE_CASE))
				# verify class_labels
				__lowerCAmelCase   :						List[str]				= torch.tensor([17, 17, 63, 75, 75, 93])
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,    _SCREAMING_SNAKE_CASE))
				# verify masks
				__lowerCAmelCase   :						int				= 82_2873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,    _SCREAMING_SNAKE_CASE)
				# verify orig_size
				__lowerCAmelCase   :						Optional[int]				= torch.tensor([480, 640])
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,    _SCREAMING_SNAKE_CASE))
				# verify size
				__lowerCAmelCase   :						Any				= torch.tensor([800, 1066])
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,    _SCREAMING_SNAKE_CASE))




"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

			# Load weights from tf checkpoint
			load_tf_weights_in_albert(__snake_case      ,__snake_case      ,__snake_case			)

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
			torch.save(model.state_dict()      ,__snake_case			)


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)