




from __future__ import annotations

from dataclasses import dataclass



@dataclass
class       a_       :
	A    							= 42
	A    							= None
	A    							= None




def lowercase    (  SCREAMING_SNAKE_CASE )			->							bool:


			'''simple docstring'''







			def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
						if node is None:
									return True

						if not isinstance(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ):
									return False

						try:
									float(node.data )
						except (TypeError, ValueError):
									return False

						return is_valid_tree(node.left ) and is_valid_tree(node.right )

			if not is_valid_tree(SCREAMING_SNAKE_CASE ):
						raise ValueError(
						    'Each node should be type of TreeNode and data should be float.' )

			def is_binary_search_tree_recursive_check(
			    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ) -> bool:

						if node is None:
									return True

						return (
						    left_bound < node.data < right_bound
						    and is_binary_search_tree_recursive_check(node.left       ,    SCREAMING_SNAKE_CASE       ,    node.data )
						    and is_binary_search_tree_recursive_check(
						        node.right       ,    node.data       ,    SCREAMING_SNAKE_CASE )
						)

			return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE       ,    -float('inf' )       ,    float('inf' ) )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

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

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


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     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."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
