

"""simple docstring"""





import argparse

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


logging.set_verbosity_info()



def 				_lowerCAmelCase					(  UpperCAmelCase   :							Optional[Any]					,							UpperCAmelCase   :							Tuple ):


					'''simple docstring'''






					UpperCamelCase__						:			Any				     =CanineConfig()
					UpperCamelCase__						:			str				     =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
					UpperCamelCase__						:			Tuple				     =CanineTokenizer()
					print(F'''Save tokenizer files to {pytorch_dump_path}''' )
					tokenizer.save_pretrained(lowerCAmelCase_ )


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








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  __lowerCamelCase       (    unittest.TestCase	,				UpperCamelCase__    ):




    """simple docstring"""

    def    a      (       self     :     Any   )      ->       Tuple:
         lowerCAmelCase__		= load_tool("image-captioning"   )
         self.tool.setup()
         lowerCAmelCase__		= load_tool("image-captioning"   ,	remote=SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Any   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Dict   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )
