


import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image






class    UpperCamelCase    (						unittest.TestCase  ,					UpperCamelCase__   ):


   '''simple docstring'''







   def 						UpperCamelCase     (   self						):
        lowercase_				:List[str]       			=				load_tool('''image-captioning'''						)
        self.tool.setup()
        lowercase_				:str       			=				load_tool('''image-captioning'''      ,							remote=SCREAMING_SNAKE_CASE__						)
   def 						UpperCamelCase     (   self						):
        lowercase_				:str       			=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
        lowercase_				:Optional[int]       			=				self.tool(SCREAMING_SNAKE_CASE__						)
        self.assertEqual(SCREAMING_SNAKE_CASE__      ,							'''two cats sleeping on a couch'''						)
   def 						UpperCamelCase     (   self						):
        lowercase_				:int       			=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
        lowercase_				:Union[str, Any]       			=				self.remote_tool(SCREAMING_SNAKE_CASE__						)
        self.assertEqual(SCREAMING_SNAKE_CASE__      ,							'''two cats sleeping on a couch'''						)
   def 						UpperCamelCase     (   self						):
        lowercase_				:int       			=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
        lowercase_				:List[str]       			=				self.tool(image=SCREAMING_SNAKE_CASE__						)
        self.assertEqual(SCREAMING_SNAKE_CASE__      ,							'''two cats sleeping on a couch'''						)




   def 						UpperCamelCase     (   self						):
        lowercase_				:str       			=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
        lowercase_				:Dict       			=				self.remote_tool(image=SCREAMING_SNAKE_CASE__						)
        self.assertEqual(SCREAMING_SNAKE_CASE__      ,							'''two cats sleeping on a couch'''						)







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)
