




'''simple docstring'''





import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def      __a     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   int ,						_UpperCamelCase:   List[str]   )						->			Any:

					"""simple docstring"""




					_snake_case							=				FunnelConfig.from_json_file(lowerCAmelCase_   )
					print(F"""Building PyTorch model from configuration: {config}"""   )
					_snake_case							=				FunnelBaseModel(lowerCAmelCase_   ) if base_model else FunnelModel(lowerCAmelCase_   )

					# Load weights from tf checkpoint
					load_tf_weights_in_funnel(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_   )

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
					torch.save(model.state_dict() ,						lowerCAmelCase_   )


if __name__ == "__main__":
			UpperCamelCase_						:      List[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(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained 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.'''
			)
			parser.add_argument(
			    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
			)
			UpperCamelCase_						:      Dict       =						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
			)








'''simple docstring'''





import pprint

import requests

UpperCamelCase_						:      Tuple       =						'''https://zenquotes.io/api'''





def      __a     (       )						->			list:

     """simple docstring"""




     return requests.get(API_ENDPOINT_URL + "/today"   ).json()





def      __a     (       )						->			list:

     """simple docstring"""




     return requests.get(API_ENDPOINT_URL + "/random"   ).json()


if __name__ == "__main__":
   UpperCamelCase_						:      Any       =						random_quotes()
   pprint.pprint(response)


