


'''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 YolosImageProcessor




class  a     (     unittest.TestCase       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__=7      ,				__magic_name__=3      ,				__magic_name__=30      ,				__magic_name__=4_00      ,				__magic_name__=True      ,				__magic_name__=None      ,				__magic_name__=True      ,				__magic_name__=[0.5, 0.5, 0.5]      ,				__magic_name__=[0.5, 0.5, 0.5]      ,				__magic_name__=True      ,				__magic_name__=1 / 2_55      ,				__magic_name__=True      ,				)					->							str:
      _a			      =							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
      _a			      =							parent
      _a			      =							batch_size
      _a			      =							num_channels
      _a			      =							min_resolution
      _a			      =							max_resolution
      _a			      =							do_resize
      _a			      =							size
      _a			      =							do_normalize
      _a			      =							image_mean
      _a			      =							image_std
      _a			      =							do_rescale
      _a			      =							rescale_factor
      _a			      =							do_pad
 def 							__UpperCAmelCase		( self						)					->							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 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__=False						)					->							List[Any]:



      if not batched:
           _a			      =							image_inputs[0]
           if isinstance(a_      ,				Image.Image						):
                _a			      =							image.size
           else:
                _a			      =							image.shape[1], image.shape[2]
           if w < h:
                _a			      =							int(self.size['shortest_edge'] * h / w						)
                _a			      =							self.size["""shortest_edge"""]
           elif w > h:
                _a			      =							self.size["""shortest_edge"""]
                _a			      =							int(self.size['shortest_edge'] * w / h						)
           else:
                _a			      =							self.size["""shortest_edge"""]
                _a			      =							self.size["""shortest_edge"""]

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

      return expected_height, expected_width





@require_torch
@require_vision
class  a     (     __a  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	YolosImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							YolosImageProcessingTester(self						)
 @property
 def 							__UpperCAmelCase		( self						)					->							Dict:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							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_      ,				'size'						)						)
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							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_						)

      _a			      =							self.image_processing_class.from_dict(
          self.image_processor_dict      ,				size=42      ,				max_size=84      ,				pad_and_return_pixel_mask=a_						)
      self.assertEqual(image_processor.size      ,				{'shortest_edge': 42, 'longest_edge': 84}						)
      self.assertEqual(image_processor.do_pad      ,				a_						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      pass
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      _a			      =							prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=a_						)
      for image in image_inputs:
           self.assertIsInstance(a_      ,				Image.Image						)

      # Test not batched input
      _a			      =							image_processing(image_inputs[0]      ,				return_tensors='pt'						).pixel_values

      _a			      =							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
      _a			      =							self.image_processor_tester.get_expected_values(a_      ,				batched=a_						)

      _a			      =							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						)					->							Any:
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _a			      =							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
      _a			      =							image_processing(image_inputs[0]      ,				return_tensors='pt'						).pixel_values

      _a			      =							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
      _a			      =							image_processing(a_      ,				return_tensors='pt'						).pixel_values

      _a			      =							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]:
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _a			      =							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
      _a			      =							image_processing(image_inputs[0]      ,				return_tensors='pt'						).pixel_values

      _a			      =							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
      _a			      =							image_processing(a_      ,				return_tensors='pt'						).pixel_values

      _a			      =							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						)					->							List[str]:
      _a			      =							self.image_processing_class(**self.image_processor_dict						)
      _a			      =							self.image_processing_class(do_resize=a_      ,				do_normalize=a_      ,				do_rescale=a_						)
      # create random PyTorch tensors
      _a			      =							prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=a_      ,				torchify=a_						)
      for image in image_inputs:
           self.assertIsInstance(a_      ,				torch.Tensor						)

      # Test whether the method "pad" and calling the image processor return the same tensors
      _a			      =							image_processing_a.pad(a_      ,				return_tensors='pt'						)
      _a			      =							image_processing_a(a_      ,				return_tensors='pt'						)

      self.assertTrue(
          torch.allclose(encoded_images_with_method['pixel_values']      ,				encoded_images['pixel_values']      ,				atol=1e-4						)						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,				'r'						) as f:
           _a			      =							json.loads(f.read()						)

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

      # encode them
      _a			      =							YolosImageProcessor.from_pretrained('hustvl/yolos-small'						)
      _a			      =							image_processing(images=a_      ,				annotations=a_      ,				return_tensors='pt'						)

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

      _a			      =							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
      _a			      =							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
      _a			      =							torch.Size([6, 4]						)
      self.assertEqual(encoding['labels'][0]['boxes'].shape      ,				a_						)
      _a			      =							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
      _a			      =							torch.tensor([3_97_69]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,				a_						)						)
      # verify is_crowd
      _a			      =							torch.tensor([0, 0, 0, 0, 0, 0]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,				a_						)						)
      # verify class_labels
      _a			      =							torch.tensor([75, 75, 63, 65, 17, 17]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,				a_						)						)
      # verify orig_size
      _a			      =							torch.tensor([4_80, 6_40]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,				a_						)						)
      # verify size
      _a			      =							torch.tensor([8_00, 10_66]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,				a_						)						)




 @slow
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,				'r'						) as f:
           _a			      =							json.loads(f.read()						)

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

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

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

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

      _a			      =							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
      _a			      =							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
      _a			      =							torch.Size([6, 4]						)
      self.assertEqual(encoding['labels'][0]['boxes'].shape      ,				a_						)
      _a			      =							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
      _a			      =							torch.tensor([3_97_69]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,				a_						)						)
      # verify is_crowd
      _a			      =							torch.tensor([0, 0, 0, 0, 0, 0]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,				a_						)						)
      # verify class_labels
      _a			      =							torch.tensor([17, 17, 63, 75, 75, 93]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,				a_						)						)
      # verify masks
      _a			      =							82_28_73
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,				a_						)
      # verify orig_size
      _a			      =							torch.tensor([4_80, 6_40]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,				a_						)						)
      # verify size
      _a			      =							torch.tensor([8_00, 10_66]						)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,				a_						)						)





'''simple docstring'''






import argparse

import torch

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


logging.set_verbosity_info()






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     _a			      =							BertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							BertForPreTraining(lowerCAmelCase__		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

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


if __name__ == "__main__":
 a_					:    Optional[int]          =    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."
 )
 a_					:    Optional[int]          =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

