
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       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     str:
			# Initialise PyTorch model
			UpperCamelCase__    :		Union[str, Any]     =				FNetConfig.from_json_file(__SCREAMING_SNAKE_CASE )
			print(F"""Building PyTorch model from configuration: {config}""" )
			UpperCamelCase__    :		Dict     =				FNetForPreTraining(__SCREAMING_SNAKE_CASE )

			UpperCamelCase__    :		List[str]     =				restore_checkpoint(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		int     =				checkpoint_dict['target']

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

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

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

						UpperCamelCase__    :		Tuple     =				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__    :		Union[str, Any]     =				torch.tensor(
						    pretrained_model_params['encoder'][F"""encoder_{layer}"""]['output_layer_norm']['scale'] )
						UpperCamelCase__    :		int     =				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__    :		str     =				torch.tensor(pretrained_model_params['encoder']['pooler']['bias'] )

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

			# Seq Relationship Layers
			UpperCamelCase__    :		Dict     =				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(__SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
							UpperCAmelCase__				:  List[Any]       					=				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.''')
							UpperCAmelCase__				:  Any       					=				parser.parse_args()
							convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





def       _lowercase					(      __SCREAMING_SNAKE_CASE = "mytext.txt"				,    __SCREAMING_SNAKE_CASE = False )	->     None:
			import socket

			UpperCamelCase__    :		Tuple     =				1_2312  # Reserve a port for your service.
			UpperCamelCase__    :		Optional[int]     =				socket.socket()  # Create a socket object
			UpperCamelCase__    :		Optional[int]     =				socket.gethostname()  # Get local machine name
			sock.bind((host, port) )  # Bind to the port
			sock.listen(5 )  # Now wait for client connection.

			print('Server listening....' )

			while True:
						UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				sock.accept()  # Establish connection with client.
						print(F"""Got connection from {addr}""" )
						UpperCamelCase__    :		Union[str, Any]     =				conn.recv(1024 )
						print(F"""Server received: {data = }""" )

						with open(__SCREAMING_SNAKE_CASE				,    'rb' ) as in_file:
									UpperCamelCase__    :		Tuple     =				in_file.read(1024 )
									while data:
												conn.send(__SCREAMING_SNAKE_CASE )
												print(F"""Sent {data!r}""" )
												UpperCamelCase__    :		str     =				in_file.read(1024 )

						print('Done sending' )
						conn.close()
						if testing:  # Allow the test to complete
									break

			sock.shutdown(1 )
			sock.close()


if __name__ == "__main__":
							send_file()



