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 		a__						( unittest.TestCase   ,		A__ ):

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   load_tool("image-segmentation"   )
							self.tool.setup()
							SCREAMING_SNAKE_CASE_  :		int    				=   load_tool("image-segmentation",remote=_A   )

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.tool(_A,"cat"   )
							self.assertTrue(isinstance(_A,Image.Image   )   )

					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
							SCREAMING_SNAKE_CASE_  :		str    				=   self.remote_tool(_A,"cat"   )
							self.assertTrue(isinstance(_A,Image.Image   )   )

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.tool(image=_A,label="cat"   )
							self.assertTrue(isinstance(_A,Image.Image   )   )






					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
							SCREAMING_SNAKE_CASE_  :		int    				=   self.remote_tool(image=_A,label="cat"   )
							self.assertTrue(isinstance(_A,Image.Image   )   )







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  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(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT 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.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
