

import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	# Initialise PyTorch model
	SCREAMING_SNAKE_CASE   :      Dict				     =       LxmertConfig.from_json_file(_a)
	print(f"Building PyTorch model from configuration: {config}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       LxmertForPreTraining(_a)

	# Load weights from tf checkpoint
	load_tf_weights_in_lxmert(_a							,			_a							,			_a)

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


if __name__ == "__main__":
			a_						     =    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(
			    '--config_file',
			    default=None,
			    type=str,
			    required=True,
			    help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			a_						     =    parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


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 _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
				SCREAMING_SNAKE_CASE   :      Any				     =       parent
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       num_channels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       min_resolution
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_resolution
				SCREAMING_SNAKE_CASE   :      str				     =       do_resize
				SCREAMING_SNAKE_CASE   :      Any				     =       size
				SCREAMING_SNAKE_CASE   :      str				     =       do_normalize
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_mean
				SCREAMING_SNAKE_CASE   :      Dict				     =       image_std
				SCREAMING_SNAKE_CASE   :      Any				     =       do_rescale
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rescale_factor
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       do_pad
			def 							__UpperCamelCase (			self							: str	)							->       List[str]:

				"""simple docstring"""

				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							: Any      ,      a							: Union[str, Any]      ,      a							: int=False	)							->       int:

				"""simple docstring"""

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

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

				return expected_height, expected_width






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =DetaImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       DetaImageProcessingTester(self	)
			@property
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       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 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_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": 1333}	)
				self.assertEqual(image_processor.do_pad      ,      a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       List[Any]:

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      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
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.image_processor_tester.get_expected_values(a      ,      batched=a	)

				SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE   :      str				     =       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
				SCREAMING_SNAKE_CASE   :      List[str]				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[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
				SCREAMING_SNAKE_CASE   :      List[str]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       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 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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
				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(image_inputs[0]      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, 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
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_processing(a      ,      return_tensors="pt"	).pixel_values

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       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 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      List[str]				     =       {"image_id": 3_9769, "annotations": target}

				# encode them
				SCREAMING_SNAKE_CASE   :      int				     =       DetaImageProcessor()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       image_processing(images=a      ,      annotations=a      ,      return_tensors="pt"	)

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

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

				# verify area
				SCREAMING_SNAKE_CASE   :      Any				     =       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"]      ,      a	)	)
				# verify boxes
				SCREAMING_SNAKE_CASE   :      str				     =       torch.Size([6, 4]	)
				self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      a      ,      atol=1e-3	)	)
				# verify image_id
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([3_9769]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      a	)	)
				# verify is_crowd
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      a	)	)
				# verify class_labels
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor([75, 75, 63, 65, 17, 17]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      a	)	)
				# verify orig_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      a	)	)
				# verify size
				SCREAMING_SNAKE_CASE   :      int				     =       torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      a	)	)






			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,      "r"	) as f:
					SCREAMING_SNAKE_CASE   :      Any				     =       json.loads(f.read()	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

				# encode them
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       DetaImageProcessor(format="coco_panoptic"	)
				SCREAMING_SNAKE_CASE   :      str				     =       image_processing(images=a      ,      annotations=a      ,      masks_path=a      ,      return_tensors="pt"	)

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

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

				# verify area
				SCREAMING_SNAKE_CASE   :      str				     =       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"]      ,      a	)	)
				# verify boxes
				SCREAMING_SNAKE_CASE   :      int				     =       torch.Size([6, 4]	)
				self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      a	)
				SCREAMING_SNAKE_CASE   :      str				     =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      a      ,      atol=1e-3	)	)
				# verify image_id
				SCREAMING_SNAKE_CASE   :      int				     =       torch.tensor([3_9769]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      a	)	)
				# verify is_crowd
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.tensor([0, 0, 0, 0, 0, 0]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      a	)	)
				# verify class_labels
				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.tensor([17, 17, 63, 75, 75, 93]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      a	)	)
				# verify masks
				SCREAMING_SNAKE_CASE   :      Tuple				     =       82_2873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,      a	)
				# verify orig_size
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor([480, 640]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      a	)	)
				# verify size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.tensor([800, 1066]	)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      a	)	)