




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 ConvNextImageProcessor





class  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     str=7   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=3   ,	SCREAMING_SNAKE_CASE__     :     Dict=18   ,	SCREAMING_SNAKE_CASE__     :     str=30   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]=400   ,	SCREAMING_SNAKE_CASE__     :     Tuple=True   ,	SCREAMING_SNAKE_CASE__     :     str=None   ,	SCREAMING_SNAKE_CASE__     :     str=0.875   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]=True   ,	SCREAMING_SNAKE_CASE__     :     Dict=[0.5, 0.5, 0.5]   ,	SCREAMING_SNAKE_CASE__     :     Dict=[0.5, 0.5, 0.5]   ,	)      ->       Optional[int]:
         lowerCAmelCase__		= size if size is not None else {"shortest_edge": 20}
         lowerCAmelCase__		= parent
         lowerCAmelCase__		= batch_size
         lowerCAmelCase__		= num_channels
         lowerCAmelCase__		= image_size
         lowerCAmelCase__		= min_resolution
         lowerCAmelCase__		= max_resolution
         lowerCAmelCase__		= do_resize
         lowerCAmelCase__		= size
         lowerCAmelCase__		= crop_pct
         lowerCAmelCase__		= do_normalize
         lowerCAmelCase__		= image_mean
         lowerCAmelCase__		= image_std


    def    a      (       self     :     Any   )      ->       Tuple:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }






@require_torch
@require_vision
class  __lowerCamelCase       (    UpperCamelCase__	,				unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      ConvNextImageProcessor if is_vision_available() else None


    def    a      (       self     :     str   )      ->       Union[str, Any]:
         lowerCAmelCase__		= ConvNextImageProcessingTester(self   )


    @property
    def    a      (       self     :     Any   )      ->       Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()


    def    a      (       self     :     Dict   )      ->       Optional[Any]:
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"do_resize"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"size"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"crop_pct"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"do_normalize"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"image_mean"   )   )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,	"image_std"   )   )


    def    a      (       self     :     Optional[Any]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size   ,	{"shortest_edge": 20}   )

         lowerCAmelCase__		= self.image_processing_class.from_dict(self.image_processor_dict   ,	size=42   )
         self.assertEqual(image_processor.size   ,	{"shortest_edge": 42}   )


    def    a      (       self     :     Dict   )      ->       int:
         pass


    def    a      (       self     :     str   )      ->       Optional[Any]:
         # Initialize image_processing
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         lowerCAmelCase__		= prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=SCREAMING_SNAKE_CASE__   )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	Image.Image   )

         # Test not batched input
         lowerCAmelCase__		= image_processing(image_inputs[0]   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )   ,	)

         # Test batched
         lowerCAmelCase__		= image_processing(SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )   ,	)


    def    a      (       self     :     int   )      ->       str:
         # Initialize image_processing
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         lowerCAmelCase__		= prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=SCREAMING_SNAKE_CASE__   ,	numpify=SCREAMING_SNAKE_CASE__   )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	np.ndarray   )

         # Test not batched input
         lowerCAmelCase__		= image_processing(image_inputs[0]   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )   ,	)

         # Test batched
         lowerCAmelCase__		= image_processing(SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )   ,	)


    def    a      (       self     :     Tuple   )      ->       str:
         # Initialize image_processing
         lowerCAmelCase__		= self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         lowerCAmelCase__		= prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=SCREAMING_SNAKE_CASE__   ,	torchify=SCREAMING_SNAKE_CASE__   )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   )

         # Test not batched input
         lowerCAmelCase__		= image_processing(image_inputs[0]   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )   ,	)

         # Test batched
         lowerCAmelCase__		= image_processing(SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )   ,	)






import argparse

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


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

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

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

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

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     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.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
