"""simple docstring"""





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Union[str, Any]      ) ->      Any:



							'''simple docstring'''

							if openai_config_file == "":
														__lowerCamelCase					:		Union[str, Any]		   =     OpenAIGPTConfig()
							else:
														__lowerCamelCase					:		Union[str, Any]		   =     OpenAIGPTConfig.from_json_file(_lowerCamelCase      )
							__lowerCamelCase					:		Any		   =     OpenAIGPTModel(_lowerCamelCase      )

							# Load weights from numpy
							load_tf_weights_in_openai_gpt(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

							# Save pytorch-model
							__lowerCamelCase					:		Tuple		   =     pytorch_dump_folder_path + "/" + WEIGHTS_NAME
							__lowerCamelCase					:		Optional[Any]		   =     pytorch_dump_folder_path + "/" + CONFIG_NAME
							print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""      )
							torch.save(model.state_dict()						,   _lowerCamelCase      )
							print(F"""Save configuration file to {pytorch_config_dump_path}"""      )
							with open(_lowerCamelCase						,   "w"						,   encoding="utf-8"      ) as f:
														f.write(config.to_json_string()      )


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--openai_checkpoint_folder_path''',
				    default=None,
				    type=str,
				    required=True,
				    help='''Path to the TensorFlow checkpoint path.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--openai_config_file''',
				    default='''''',
				    type=str,
				    help=(
				        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				__A  =				parser.parse_args()
				convert_openai_checkpoint_to_pytorch(
				    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
				)
"""simple docstring"""





import math
from datetime import datetime, timedelta


def    lowercase_   (   _lowerCamelCase:	int      ) ->      datetime:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     year % 19
							__lowerCamelCase					:		List[str]		   =     year % 4
							__lowerCamelCase					:		Dict		   =     year % 7
							__lowerCamelCase					:		Optional[Any]		   =     math.floor(year / 100      )
							__lowerCamelCase					:		Tuple		   =     math.floor((13 + 8 * leap_day_inhibits) / 25      )
							__lowerCamelCase					:		List[str]		   =     leap_day_inhibits / 4
							__lowerCamelCase					:		Optional[Any]		   =     (
							    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
							) % 30
							__lowerCamelCase					:		Union[str, Any]		   =     (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

							# days to be added to March 21
							__lowerCamelCase					:		Union[str, Any]		   =     (19 * metonic_cycle + secular_moon_shift) % 30

							# PHM -> Paschal Full Moon
							__lowerCamelCase					:		List[Any]		   =     (
							    2 * julian_leap_year
							    + 4 * non_leap_year
							    + 6 * days_to_add
							    + century_starting_point
							) % 7

							if days_to_add == 29 and days_from_phm_to_sunday == 6:
														return datetime(_lowerCamelCase						,   4						,   19      )
							elif days_to_add == 28 and days_from_phm_to_sunday == 6:
														return datetime(_lowerCamelCase						,   4						,   18      )
							else:
														return datetime(_lowerCamelCase						,   3						,   22      ) + timedelta(
														    days=int(days_to_add + days_from_phm_to_sunday      )      )


if __name__ == "__main__":
				for year in (1994, 2000, 2010, 2021, 2023):
								__A  =				'''will be''' if year > datetime.now().year else '''was'''
								print(F"""Easter in {year} {tense} {gauss_easter(year)}""")