






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]					,  snake_case_ :							List[str]       ):
	# Initialise PyTorch model
	snake_case__			:				Optional[int]       =  BertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				List[str]       =  BertForPreTraining(snake_case_       )

	# Load weights from tf checkpoint
	load_tf_weights_in_bert(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__":
	__lowerCamelCase :      List[str]										=				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(
	    """--bert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained BERT 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."""
	)
	__lowerCamelCase :      Optional[int]										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_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 DeformableDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def __init__( self					:  List[str]							,		__A					:  int							,		__A					:  str=7							,		__A					:  Union[str, Any]=3							,		__A					:  Union[str, Any]=3_0							,		__A					:  Optional[int]=4_0_0							,		__A					:  Optional[Any]=True							,		__A					:  Optional[int]=None							,		__A					:  Union[str, Any]=True							,		__A					:  Optional[int]=[0.5, 0.5, 0.5]							,		__A					:  Any=[0.5, 0.5, 0.5]							,		__A					:  Optional[int]=True							,		__A					:  Optional[Any]=1 / 2_5_5							,		__A					:  Union[str, Any]=True							,		):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__			:				Optional[Any]       =  size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__			:				List[Any]       =  parent
						snake_case__			:				Union[str, Any]       =  batch_size
						snake_case__			:				Tuple       =  num_channels
						snake_case__			:				List[Any]       =  min_resolution
						snake_case__			:				Optional[Any]       =  max_resolution
						snake_case__			:				str       =  do_resize
						snake_case__			:				List[str]       =  size
						snake_case__			:				List[Any]       =  do_normalize
						snake_case__			:				Dict       =  image_mean
						snake_case__			:				List[Any]       =  image_std
						snake_case__			:				int       =  do_rescale
						snake_case__			:				Tuple       =  rescale_factor
						snake_case__			:				str       =  do_pad



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

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

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		DeformableDetrImageProcessor if is_vision_available() else None



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				str       =  DeformableDetrImageProcessingTester(self	)



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



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				int       =  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					:  Tuple	):
						snake_case__			:				int       =  self.image_processing_class.from_dict(self.image_processor_dict	)
						self.assertEqual(image_processor.size							,		{"shortest_edge": 1_8, "longest_edge": 1_3_3_3}	)
						self.assertEqual(image_processor.do_pad							,		__A	)

						snake_case__			:				List[Any]       =  self.image_processing_class.from_dict(
						    self.image_processor_dict							,		size=4_2							,		max_size=8_4							,		pad_and_return_pixel_mask=__A	)
						self.assertEqual(image_processor.size							,		{"shortest_edge": 4_2, "longest_edge": 8_4}	)
						self.assertEqual(image_processor.do_pad							,		__A	)



					def 	_lowercase					( self					:  Any	):
						pass



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

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

						snake_case__,						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
						snake_case__,						snake_case__			:				Optional[Any]       =  self.image_processor_tester.get_expected_values(__A							,		batched=__A	)

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

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

						snake_case__,						snake_case__			:				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					:  Union[str, Any]	):
						# Initialize image_processing
						snake_case__			:				str       =  self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						snake_case__			:				List[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
						snake_case__			:				Tuple       =  image_processing(image_inputs[0]							,		return_tensors="pt"	).pixel_values

						snake_case__,						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
						snake_case__			:				Optional[Any]       =  image_processing(__A							,		return_tensors="pt"	).pixel_values

						snake_case__,						snake_case__			:				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[int]	):
						# prepare image and target
						snake_case__			:				List[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__			:				Any       =  json.loads(f.read()	)

						snake_case__			:				List[str]       =  {"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__			:				Optional[Any]       =  DeformableDetrImageProcessor()
						snake_case__			:				Tuple       =  image_processing(images=__A							,		annotations=__A							,		return_tensors="pt"	)

						# verify pixel values
						snake_case__			:				str       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
						self.assertEqual(encoding["pixel_values"].shape							,		__A	)

						snake_case__			:				int       =  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]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				int       =  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"]							,		__A	)	)
						# verify boxes
						snake_case__			:				str       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						snake_case__			:				List[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]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				int       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				Dict       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				Optional[int]       =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,		__A	)	)
						# verify orig_size
						snake_case__			:				Union[str, Any]       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				List[str]       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)






					@slow
					def 	_lowercase					( self					:  Union[str, Any]	):
						# prepare image, target and masks_path
						snake_case__			:				str       =  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__			:				Any       =  {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

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

						# encode them
						snake_case__			:				Dict       =  DeformableDetrImageProcessor(format="coco_panoptic"	)
						snake_case__			:				List[Any]       =  image_processing(images=__A							,		annotations=__A							,		masks_path=__A							,		return_tensors="pt"	)

						# verify pixel values
						snake_case__			:				Optional[int]       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]	)
						self.assertEqual(encoding["pixel_values"].shape							,		__A	)

						snake_case__			:				Tuple       =  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]							,		__A							,		atol=1e-4	)	)

						# verify area
						snake_case__			:				Any       =  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"]							,		__A	)	)
						# verify boxes
						snake_case__			:				Union[str, Any]       =  torch.Size([6, 4]	)
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,		__A	)
						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]							,		__A							,		atol=1e-3	)	)
						# verify image_id
						snake_case__			:				Union[str, Any]       =  torch.tensor([3_9_7_6_9]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,		__A	)	)
						# verify is_crowd
						snake_case__			:				List[Any]       =  torch.tensor([0, 0, 0, 0, 0, 0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,		__A	)	)
						# verify class_labels
						snake_case__			:				List[str]       =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,		__A	)	)
						# verify masks
						snake_case__			:				Optional[Any]       =  8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,		__A	)
						# verify orig_size
						snake_case__			:				Dict       =  torch.tensor([4_8_0, 6_4_0]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,		__A	)	)
						# verify size
						snake_case__			:				Dict       =  torch.tensor([8_0_0, 1_0_6_6]	)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,		__A	)	)



