


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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     # Initialise PyTorch model
     a_     :  int				      =     FNetConfig.from_json_file(__UpperCamelCase			)
     print(F'''Building PyTorch model from configuration: {config}'''			)
     a_     :  Optional[int]				      =     FNetForPreTraining(__UpperCamelCase			)

     a_     :  Optional[int]				      =     restore_checkpoint(__UpperCamelCase	,	__UpperCamelCase			)
     a_     :  Dict				      =     checkpoint_dict["""target"""]

     # Embeddings
     # Position IDs
     a_     :  int				      =     fnet_pretraining_model.state_dict()

     a_     :  Union[str, Any]				      =     state_dict["""fnet.embeddings.position_ids"""]
     a_     :  Optional[Any]				      =     {"""fnet.embeddings.position_ids""": position_ids}
     # Embedding Layers
     a_     :  Any				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
     a_     :  Union[str, Any]				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
     a_     :  List[str]				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
     a_     :  List[str]				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
     a_     :  Union[str, Any]				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
     a_     :  Any				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
     a_     :  Optional[int]				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

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

          a_     :  Union[str, Any]				      =     torch.tensor(
              pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]			).T
          a_     :  Dict				      =     torch.tensor(
              pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]			)

          a_     :  Optional[Any]				      =     torch.tensor(
              pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]			).T
          a_     :  List[str]				      =     torch.tensor(
              pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]			)

          a_     :  Optional[Any]				      =     torch.tensor(
              pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]			)
          a_     :  Any				      =     torch.tensor(
              pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]			)

     # Pooler Layers
     a_     :  List[Any]				      =     torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
     a_     :  Optional[int]				      =     torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

     # Masked LM Layers
     a_     :  str				      =     torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
     a_     :  Optional[Any]				      =     torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""bias"""]			)
     a_     :  List[Any]				      =     torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
     a_     :  List[str]				      =     torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
     a_     :  List[Any]				      =     torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
     a_     :  List[Any]				      =     torch.tensor(
         pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
     a_     :  Any				      =     torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

     # Seq Relationship Layers
     a_     :  Union[str, Any]				      =     torch.tensor(
         pretrained_model_params["""classification"""]["""output_kernel"""]			)
     a_     :  List[str]				      =     torch.tensor(
         pretrained_model_params["""classification"""]["""output_bias"""]			)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(__UpperCamelCase			)

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


if __name__ == "__main__":
       __lowerCamelCase   	=  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.''')
       __lowerCamelCase   	=  parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       Queue()
					_lowerCAmelCase         =       [False] * len(snake_case     )
					_lowerCAmelCase         =       [-1] * len(snake_case     )

					def bfs():
										while not queue.empty():
															_lowerCAmelCase         =       queue.get()
															_lowerCAmelCase         =       True

															for neighbour in graph[u]:
																				if neighbour == u:
																									return False

																				if color[neighbour] == -1:
																									_lowerCAmelCase         =       1 - color[u]
																									queue.put(snake_case     )

																				elif color[neighbour] == color[u]:
																									return False

										return True

					for i in range(len(snake_case     )     ):
										if not visited[i]:
															queue.put(snake_case     )
															_lowerCAmelCase         =       0
															if bfs() is False:
																				return False

					return True


if __name__ == "__main__":
	# Adjacency List of graph
	print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))



