code: 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			(  _A ,_A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	FNetConfig.from_json_file(_A					)
  							print(f'''Building PyTorch model from configuration: {config}'''					)
  							_lowercase				=	FNetForPreTraining(_A					)
  
  							_lowercase				=	restore_checkpoint(_A ,_A					)
  							_lowercase				=	checkpoint_dict["""target"""]
  
  							# Embeddings
  							# Position IDs
  							_lowercase				=	fnet_pretraining_model.state_dict()
  
  							_lowercase				=	state_dict["""fnet.embeddings.position_ids"""]
  							_lowercase				=	{"""fnet.embeddings.position_ids""": position_ids}
  							# Embedding Layers
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]					).T
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]					)
  
  							# Encoder Layers
  							for layer in range(config.num_hidden_layers					):
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]					)
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]					)
  
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]					).T
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]					)
  
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]					).T
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]					)
  
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]					)
  														_lowercase				=	torch.tensor(
  														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]					)
  
  							# Pooler Layers
  							_lowercase				=	torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]					).T
  							_lowercase				=	torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]					)
  
  							# Masked LM Layers
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""predictions_dense"""]["""kernel"""]					).T
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""predictions_dense"""]["""bias"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""predictions_output"""]["""output_bias"""]					)
  							_lowercase				=	torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]					)
  
  							# Seq Relationship Layers
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""classification"""]["""output_kernel"""]					)
  							_lowercase				=	torch.tensor(
  							    pretrained_model_params["""classification"""]["""output_bias"""]					)
  
  							# Load State Dict
  							fnet_pretraining_model.load_state_dict(_A					)
  
  							# Save PreTrained
  							print(f'''Saving pretrained model to {save_path}'''					)
  							fnet_pretraining_model.save_pretrained(_A					)
  
  
  if __name__ == "__main__":
  				A_:      Tuple        =			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.')
  				A_:      Dict        =			parser.parse_args()
  				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
  
  
  
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
  				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					c_float
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return sizeof(self.value  )
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A = 0					):
  
  
  
  							"""simple docstring"""
  
  
  							if dimension <= 0:
  														_lowercase				=	fixed_dimension
  
  							dimension -= num_token_to_add
  							return dimension
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  							try:
  														return AutoProcessor.from_pretrained(_A					)
  							except (ValueError, OSError, KeyError):
  														_lowercase				,       _lowercase				=	None, None
  														try:
  																					_lowercase				=	AutoTokenizer.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  														try:
  																					_lowercase				=	AutoFeatureExtractor.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  
  														if tokenizer is not None and feature_extractor is not None:
  																					raise ValueError(
  																					    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''					)
  														elif tokenizer is None and feature_extractor is None:
  																					return None
  														elif tokenizer is not None:
  																					return tokenizer
  														else:
  																					return feature_extractor
  
  
  