





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 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       Union[str, Any]						)			-> Union[str, Any]:



					'''simple docstring'''




					_snake_case									=				FunnelConfig.from_json_file(UpperCamelCase__						)
					print(F'''Building PyTorch model from configuration: {config}'''						)
					_snake_case									=				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__":
					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 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_      							=     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
					)










import random





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       List[Any]						)			-> tuple:



					'''simple docstring'''




					_snake_case							,     _snake_case							,     _snake_case									=				[], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase__						)
										elif element > pivot:
															greater.append(UpperCamelCase__						)
										else:
															equal.append(UpperCamelCase__						)
					return less, equal, greater





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       int						)			-> Dict:



					'''simple docstring'''




					if index >= len(UpperCamelCase__						) or index < 0:
										return None

					_snake_case									=				items[random.randint(0       ,    len(UpperCamelCase__						) - 1						)]
					_snake_case									=				0
					_snake_case							,     _snake_case							,     _snake_case									=				_partition(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				len(UpperCamelCase__						)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase__       ,    UpperCamelCase__						)
					# must be in larger
					else:
										return quick_select(UpperCamelCase__       ,    index - (m + count)						)



