



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class    __lowerCAmelCase					(	unittest.TestCase			):




				@patch("""diffusers.utils.hub_utils.get_full_repo_name"""						)
				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       """full_repo_name"""
									with TemporaryDirectory() as tmpdir:
														# Dummy args values
														_lowerCAmelCase         =       Mock()
														_lowerCAmelCase         =       tmpdir
														_lowerCAmelCase         =       0
														_lowerCAmelCase         =       """hub_token"""
														_lowerCAmelCase         =       """dataset_name"""
														_lowerCAmelCase         =       0.01
														_lowerCAmelCase         =       100000
														_lowerCAmelCase         =       10000
														_lowerCAmelCase         =       0.01
														_lowerCAmelCase         =       0.02
														_lowerCAmelCase         =       0.03
														_lowerCAmelCase         =       0.0005
														_lowerCAmelCase         =       0.00_0001
														_lowerCAmelCase         =       1
														_lowerCAmelCase         =       10
														_lowerCAmelCase         =       0.001
														_lowerCAmelCase         =       0.1
														_lowerCAmelCase         =       0.2
														_lowerCAmelCase         =       True

														# Model card mush be rendered and saved
														diffusers.utils.hub_utils.create_model_card(_snake_case		,							model_name="""model_name"""						)
														self.assertTrue((Path(_snake_case						) / """README.md""").is_file()						)









import argparse

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


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				str    						=	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."""
		)
		__A    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






