



import requests





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     str					)       ->				None:







			'''simple docstring'''
			_UpperCAmelCase	 =     {'Content-Type': 'application/json'}
			_UpperCAmelCase	 =     requests.post(_UpperCAmelCase							,     json={'text': message_body}							,     headers=_UpperCAmelCase					)
			if response.status_code != 200:
						_UpperCAmelCase	 =     (
						    'Request to slack returned an error '
						    F"{response.status_code}, the response is:\n{response.text}"
						)
						raise ValueError(_UpperCAmelCase					)


if __name__ == "__main__":
							# Set the slack url to the one provided by Slack when you create the webhook at
							# https://my.slack.com/services/new/incoming-webhook/
							send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


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 path."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
