
import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()




def 				lowerCamelCase__				(						a__  :  Dict	,      a__  :  Union[str, Any]     )		->	List[str]:
       # Initialize PyTorch model
       UpperCamelCase_							       =       CanineConfig()
       UpperCamelCase_							       =       CanineModel(a__     )
       model.eval()

       print(f'''Building PyTorch model from configuration: {config}'''     )

       # Load weights from tf checkpoint
       load_tf_weights_in_canine(a__	,      a__	,      a__     )

       # Save pytorch-model (weights and configuration)
       print(f'''Save PyTorch model to {pytorch_dump_path}'''     )
       model.save_pretrained(a__     )

       # Save tokenizer files
       UpperCamelCase_							       =       CanineTokenizer()
       print(f'''Save tokenizer files to {pytorch_dump_path}'''     )
       tokenizer.save_pretrained(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. Should end with model.ckpt''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''',
          default=None,
          type=str,
          required=True,
          help='''Path to a folder where the PyTorch model will be placed.''',
      )
      _A	  =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 ViltImageProcessor

class 		lowercase_       (       unittest.TestCase				):

 def __init__(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=7     ,	__UpperCamelCase=3     ,	__UpperCamelCase=1_8     ,	__UpperCamelCase=3_0     ,	__UpperCamelCase=4_0_0     ,	__UpperCamelCase=True     ,	__UpperCamelCase=None     ,	__UpperCamelCase=2     ,	__UpperCamelCase=True     ,	__UpperCamelCase=[0.5, 0.5, 0.5]     ,	__UpperCamelCase=[0.5, 0.5, 0.5]     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       size if size is not None else {"""shortest_edge""": 3_0}
        UpperCamelCase_							       =       parent
        UpperCamelCase_							       =       batch_size
        UpperCamelCase_							       =       num_channels
        UpperCamelCase_							       =       image_size
        UpperCamelCase_							       =       min_resolution
        UpperCamelCase_							       =       max_resolution
        UpperCamelCase_							       =       do_resize
        UpperCamelCase_							       =       size
        UpperCamelCase_							       =       size_divisor
        UpperCamelCase_							       =       do_normalize
        UpperCamelCase_							       =       image_mean
        UpperCamelCase_							       =       image_std

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=False     ):




        """simple docstring"""


        if not batched:
               UpperCamelCase_							       =       self.size["""shortest_edge"""]
               UpperCamelCase_							       =       image_inputs[0]
               if isinstance(__UpperCamelCase     ,	Image.Image     ):
                      UpperCamelCase_       ,				UpperCamelCase_							       =       image.size
               else:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       image.shape[1], image.shape[2]
               UpperCamelCase_							       =       size / min(__UpperCamelCase     ,	__UpperCamelCase     )
               if h < w:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       size, scale * w
               else:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       scale * h, size

               UpperCamelCase_							       =       int((1_3_3_3 / 8_0_0) * size     )
               if max(__UpperCamelCase     ,	__UpperCamelCase     ) > max_size:
                      UpperCamelCase_							       =       max_size / max(__UpperCamelCase     ,	__UpperCamelCase     )
                      UpperCamelCase_							       =       newh * scale
                      UpperCamelCase_							       =       neww * scale

               UpperCamelCase_       ,				UpperCamelCase_							       =       int(newh + 0.5     ), int(neww + 0.5     )
               UpperCamelCase_       ,				UpperCamelCase_							       =       (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

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

        return expected_height, expected_width



@require_torch
@require_vision
class 		lowercase_       (       __SCREAMING_SNAKE_CASE							,	unittest.TestCase				):
 A__						:				Optional[int]         =    ViltImageProcessor if is_vision_available() else None

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       ViltImageProcessingTester(self     )

 @property
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""image_mean"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""image_std"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""do_normalize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""do_resize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""size"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""size_divisor"""     )     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 3_0}     )

        UpperCamelCase_							       =       self.image_processing_class.from_dict(self.image_processor_dict     ,	size=4_2     )
        self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 4_2}     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        pass

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

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

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        UpperCamelCase_							       =       prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__UpperCamelCase     ,	numpify=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	np.ndarray     )

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

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        UpperCamelCase_							       =       prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__UpperCamelCase     ,	torchify=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	torch.Tensor     )

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

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)



