






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      __SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def __init__(   self:					Any				,		UpperCamelCase:					Dict				,		UpperCamelCase:					Union[str, Any]=7				,		UpperCamelCase:					Optional[int]=3				,		UpperCamelCase:					List[str]=30				,		UpperCamelCase:					List[Any]=4_00				,		UpperCamelCase:					Dict=True				,		UpperCamelCase:					Union[str, Any]=None				,		UpperCamelCase:					Union[str, Any]=True				,		UpperCamelCase:					Optional[int]=[0.5, 0.5, 0.5]				,		UpperCamelCase:					Any=[0.5, 0.5, 0.5]				,		UpperCamelCase:					int=True				,		UpperCamelCase:					List[str]=1 / 2_55				,		UpperCamelCase:					List[str]=True				,		)						->						List[str]:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											snake_case__													=   size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
											snake_case__													=   parent
											snake_case__													=   batch_size
											snake_case__													=   num_channels
											snake_case__													=   min_resolution
											snake_case__													=   max_resolution
											snake_case__													=   do_resize
											snake_case__													=   size
											snake_case__													=   do_normalize
											snake_case__													=   image_mean
											snake_case__													=   image_std
											snake_case__													=   do_rescale
											snake_case__													=   rescale_factor
											snake_case__													=   do_pad
							def lowerCAmelCase_       (   self:					Union[str, Any]  )						->						Dict:
											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 lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					Optional[int]				,		UpperCamelCase:					List[Any]=False  )						->						Optional[Any]:
											if not batched:
															snake_case__													=   image_inputs[0]
															if isinstance(_a				,		Image.Image  ):
																			snake_case__													=   image.size
															else:
																			snake_case__													=   image.shape[1], image.shape[2]
															if w < h:
																			snake_case__													=   int(self.size['shortest_edge'] * h / w  )
																			snake_case__													=   self.size["shortest_edge"]
															elif w > h:
																			snake_case__													=   self.size["shortest_edge"]
																			snake_case__													=   int(self.size['shortest_edge'] * w / h  )
															else:
																			snake_case__													=   self.size["shortest_edge"]
																			snake_case__													=   self.size["shortest_edge"]

											else:
															snake_case__													=   []
															for image in image_inputs:
																			snake_case__													=   self.get_expected_values([image]  )
																			expected_values.append((expected_height, expected_width)  )
															snake_case__													=   max(_a				,		key=lambda UpperCamelCase  : item[0]  )[0]
															snake_case__													=   max(_a				,		key=lambda UpperCamelCase  : item[1]  )[1]

											return expected_height, expected_width


@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE(		a_			,     unittest.TestCase		):
							_UpperCAmelCase       			=				DetaImageProcessor if is_vision_available() else None
							def lowerCAmelCase_       (   self:					Optional[int]  )						->						int:
											snake_case__													=   DetaImageProcessingTester(self  )
							@property
							def lowerCAmelCase_       (   self:					List[str]  )						->						str:
											return self.image_processor_tester.prepare_image_processor_dict()
							def lowerCAmelCase_       (   self:					Dict  )						->						Union[str, Any]:
											snake_case__													=   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 lowerCAmelCase_       (   self:					Dict  )						->						List[Any]:
											snake_case__													=   self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size				,		{'shortest_edge': 18, 'longest_edge': 13_33}  )
											self.assertEqual(image_processor.do_pad				,		_a  )
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Union[str, Any]:
											pass
							def lowerCAmelCase_       (   self:					List[Any]  )						->						Optional[Any]:
											# Initialize image_processing
											snake_case__													=   self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											snake_case__													=   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__													=   image_processing(image_inputs[0]				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   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__													=   self.image_processor_tester.get_expected_values(_a				,		batched=_a  )

											snake_case__													=   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 lowerCAmelCase_       (   self:					Union[str, Any]  )						->						Tuple:
											# Initialize image_processing
											snake_case__													=   self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											snake_case__													=   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__													=   image_processing(image_inputs[0]				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   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__													=   image_processing(_a				,		return_tensors='pt'  ).pixel_values

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

											snake_case__													=   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__													=   image_processing(_a				,		return_tensors='pt'  ).pixel_values

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

											snake_case__													=   {"image_id": 3_97_69, "annotations": target}

											# encode them
											snake_case__													=   DetaImageProcessor()
											snake_case__													=   image_processing(images=_a				,		annotations=_a				,		return_tensors='pt'  )

											# verify pixel values
											snake_case__													=   torch.Size([1, 3, 8_00, 10_66]  )
											self.assertEqual(encoding['pixel_values'].shape				,		_a  )

											snake_case__													=   torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,		_a				,		atol=1e-4  )  )

											# verify area
											snake_case__													=   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,		_a  )  )
											# verify boxes
											snake_case__													=   torch.Size([6, 4]  )
											self.assertEqual(encoding['labels'][0]['boxes'].shape				,		_a  )
											snake_case__													=   torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,		_a				,		atol=1e-3  )  )
											# verify image_id
											snake_case__													=   torch.tensor([3_97_69]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,		_a  )  )
											# verify is_crowd
											snake_case__													=   torch.tensor([0, 0, 0, 0, 0, 0]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,		_a  )  )
											# verify class_labels
											snake_case__													=   torch.tensor([75, 75, 63, 65, 17, 17]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,		_a  )  )
											# verify orig_size
											snake_case__													=   torch.tensor([4_80, 6_40]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,		_a  )  )
											# verify size
											snake_case__													=   torch.tensor([8_00, 10_66]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,		_a  )  )


							@slow
							def lowerCAmelCase_       (   self:					List[str]  )						->						Union[str, Any]:
											# prepare image, target and masks_path
											snake_case__													=   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__													=   json.loads(f.read()  )

											snake_case__													=   {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

											snake_case__													=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'  )

											# encode them
											snake_case__													=   DetaImageProcessor(format='coco_panoptic'  )
											snake_case__													=   image_processing(images=_a				,		annotations=_a				,		masks_path=_a				,		return_tensors='pt'  )

											# verify pixel values
											snake_case__													=   torch.Size([1, 3, 8_00, 10_66]  )
											self.assertEqual(encoding['pixel_values'].shape				,		_a  )

											snake_case__													=   torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,		_a				,		atol=1e-4  )  )

											# verify area
											snake_case__													=   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,		_a  )  )
											# verify boxes
											snake_case__													=   torch.Size([6, 4]  )
											self.assertEqual(encoding['labels'][0]['boxes'].shape				,		_a  )
											snake_case__													=   torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,		_a				,		atol=1e-3  )  )
											# verify image_id
											snake_case__													=   torch.tensor([3_97_69]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,		_a  )  )
											# verify is_crowd
											snake_case__													=   torch.tensor([0, 0, 0, 0, 0, 0]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,		_a  )  )
											# verify class_labels
											snake_case__													=   torch.tensor([17, 17, 63, 75, 75, 93]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,		_a  )  )
											# verify masks
											snake_case__													=   82_28_73
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,		_a  )
											# verify orig_size
											snake_case__													=   torch.tensor([4_80, 6_40]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,		_a  )  )
											# verify size
											snake_case__													=   torch.tensor([8_00, 10_66]  )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,		_a  )  )






"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

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

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


if __name__ == "__main__":
   _A			:       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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


