

"""simple docstring"""





import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 				_lowerCAmelCase					(  UpperCAmelCase   :							Dict					,							UpperCAmelCase   :							List[str]					,							UpperCAmelCase   :							int ):


					'''simple docstring'''






					UpperCamelCase__						:			Dict				     =FNetConfig.from_json_file(lowerCAmelCase_ )
					print(F'''Building PyTorch model from configuration: {config}''' )
					UpperCamelCase__						:			Union[str, Any]				     =FNetForPreTraining(lowerCAmelCase_ )

					UpperCamelCase__						:			str				     =restore_checkpoint(lowerCAmelCase_					,							lowerCAmelCase_ )
					UpperCamelCase__						:			Optional[Any]				     =checkpoint_dict['''target''']

					# Embeddings
					# Position IDs
					UpperCamelCase__						:			Optional[Any]				     =fnet_pretraining_model.state_dict()

					UpperCamelCase__						:			Any				     =state_dict['''fnet.embeddings.position_ids''']
					UpperCamelCase__						:			Tuple				     ={'''fnet.embeddings.position_ids''': position_ids}
					# Embedding Layers
					UpperCamelCase__						:			List[str]				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding'''] )
					UpperCamelCase__						:			Any				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0] )
					UpperCamelCase__						:			int				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding'''] )
					UpperCamelCase__						:			Optional[int]				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel'''] ).T
					UpperCamelCase__						:			Optional[Any]				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias'''] )
					UpperCamelCase__						:			Optional[Any]				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale'''] )
					UpperCamelCase__						:			Tuple				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias'''] )

					# Encoder Layers
					for layer in range(config.num_hidden_layers ):
										UpperCamelCase__						:			Tuple				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''mixing_layer_norm''']['''scale'''] )
										UpperCamelCase__						:			Any				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''mixing_layer_norm''']['''bias'''] )

										UpperCamelCase__						:			Tuple				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''intermediate''']['''kernel'''] ).T
										UpperCamelCase__						:			int				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''intermediate''']['''bias'''] )

										UpperCamelCase__						:			str				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''output''']['''kernel'''] ).T
										UpperCamelCase__						:			int				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''feed_forward_{layer}''']['''output''']['''bias'''] )

										UpperCamelCase__						:			Dict				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''output_layer_norm''']['''scale'''] )
										UpperCamelCase__						:			Dict				     =torch.tensor(
										    pretrained_model_params['''encoder'''][F'''encoder_{layer}''']['''output_layer_norm''']['''bias'''] )

					# Pooler Layers
					UpperCamelCase__						:			Optional[Any]				     =torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel'''] ).T
					UpperCamelCase__						:			Any				     =torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias'''] )

					# Masked LM Layers
					UpperCamelCase__						:			Tuple				     =torch.tensor(
					    pretrained_model_params['''predictions_dense''']['''kernel'''] ).T
					UpperCamelCase__						:			int				     =torch.tensor(
					    pretrained_model_params['''predictions_dense''']['''bias'''] )
					UpperCamelCase__						:			Tuple				     =torch.tensor(
					    pretrained_model_params['''predictions_layer_norm''']['''scale'''] )
					UpperCamelCase__						:			List[str]				     =torch.tensor(
					    pretrained_model_params['''predictions_layer_norm''']['''bias'''] )
					UpperCamelCase__						:			str				     =torch.tensor(
					    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding'''] )
					UpperCamelCase__						:			Optional[int]				     =torch.tensor(
					    pretrained_model_params['''predictions_output''']['''output_bias'''] )
					UpperCamelCase__						:			Any				     =torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias'''] )

					# Seq Relationship Layers
					UpperCamelCase__						:			Optional[int]				     =torch.tensor(
					    pretrained_model_params['''classification''']['''output_kernel'''] )
					UpperCamelCase__						:			List[Any]				     =torch.tensor(
					    pretrained_model_params['''classification''']['''output_bias'''] )

					# Load State Dict
					fnet_pretraining_model.load_state_dict(lowerCAmelCase_ )

					# Save PreTrained
					print(F'''Saving pretrained model to {save_path}''' )
					fnet_pretraining_model.save_pretrained(lowerCAmelCase_ )


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE				:     Optional[int]       				=       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
				)
				parser.add_argument(
				    """--fnet_config_file""",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        """The config json file corresponding to the pre-trained FNet model. \n"""
				        """This specifies the model architecture."""
				    ),
				)
				parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
				_SCREAMING_SNAKE_CASE				:     Union[str, Any]       				=       parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase								=     TypeVar('T')





class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       List[Any]:
         lowerCAmelCase__		= data  # Assign data
         lowerCAmelCase__		= None  # Initialize next as null
         lowerCAmelCase__		= None  # Initialize prev as null






class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     Any   )      ->       None:
         lowerCAmelCase__		= None


    def    a      (       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       None:
         if self.head is None:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
         else:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
              lowerCAmelCase__		= new_node
              lowerCAmelCase__		= self.head
              lowerCAmelCase__		= None
              lowerCAmelCase__		= new_node


    def    a      (       self     :     int   )      ->       T | None:
         if self.head is None:
              return None
         else:
              assert self.head is not None
              lowerCAmelCase__		= self.head.data
              lowerCAmelCase__		= self.head.next
              if self.head is not None:
                   lowerCAmelCase__		= None
              return temp


    def    a      (       self     :     int   )      ->       T | None:
         return self.head.data if self.head is not None else None


    def __len__(       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= self.head
         lowerCAmelCase__		= 0
         while temp is not None:
              count += 1
              lowerCAmelCase__		= temp.next
         return count


    def    a      (       self     :     Tuple   )      ->       bool:
         return self.head is None


    def    a      (       self     :     Dict   )      ->       None:
         print("stack elements are:"   )
         lowerCAmelCase__		= self.head
         while temp is not None:
              print(temp.data   ,	end="->"   )
              lowerCAmelCase__		= temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     UpperCamelCase								=     Stack()

     # Insert 4 at the beginning. So stack becomes 4->None
     print('Stack operations using Doubly LinkedList')
     stack.push(4)

     # Insert 5 at the beginning. So stack becomes 4->5->None
     stack.push(5)

     # Insert 6 at the beginning. So stack becomes 4->5->6->None
     stack.push(6)

     # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
     stack.push(7)

     # Print the stack
     stack.print_stack()

     # Print the top element
     print('\nTop element is ', stack.top())

     # Print the stack size
     print('Size of the stack is ', len(stack))

     # pop the top element
     stack.pop()

     # pop the top element
     stack.pop()

     # two elements have now been popped off
     stack.print_stack()

     # Print True if the stack is empty else False
     print('\nstack is empty:', stack.is_empty())
