

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				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						# Initialise PyTorch model
						SCREAMING_SNAKE_CASE_: Tuple  		=       FunnelConfig.from_json_file(_UpperCAmelCase    )
						print(f"Building PyTorch model from configuration: {config}"    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FunnelBaseModel(_UpperCAmelCase    ) if base_model else FunnelModel(_UpperCAmelCase    )

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

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


if __name__ == "__main__":
						lowerCAmelCase							:  Union[str, 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(
						    """--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."""
						)
						lowerCAmelCase							:  Optional[int]   	=						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 math
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       list[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					[]
							__UpperCAmelCase   :						Dict      							=					2
							__UpperCAmelCase   :						Union[str, Any]      							=					int(math.sqrt(_UpperCamelCase							)							)  # Size of every segment
							__UpperCAmelCase   :						Tuple      							=					[True] * (end + 1)
							__UpperCAmelCase   :						int      							=					[]

							while start <= end:
														if temp[start] is True:
																					in_prime.append(_UpperCamelCase							)
																					for i in range(start * start       ,			end + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Dict      							=					False
														start += 1
							prime += in_prime

							__UpperCAmelCase   :						Optional[int]      							=					end + 1
							__UpperCAmelCase   :						Dict      							=					min(2 * end       ,			_UpperCamelCase							)

							while low <= n:
														__UpperCAmelCase   :						Union[str, Any]      							=					[True] * (high - low + 1)
														for each in in_prime:
																					__UpperCAmelCase   :						Dict      							=					math.floor(low / each							) * each
																					if t < low:
																												t += each

																					for j in range(_UpperCamelCase       ,			high + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Tuple      							=					False

														for j in range(len(_UpperCamelCase							)							):
																					if temp[j] is True:
																												prime.append(j + low							)

														__UpperCAmelCase   :						Tuple      							=					high + 1
														__UpperCAmelCase   :						Optional[int]      							=					min(high + end       ,			_UpperCamelCase							)

							return prime
print(sieve(10**6))
