

"""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 		A_	(   __UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				Dict       ,	__UpperCamelCase     :				int				):
		# Initialise PyTorch model
		lowercase										=							FNetConfig.from_json_file(__UpperCamelCase				)
		print(f"""Building PyTorch model from configuration: {config}"""				)
		lowercase										=							FNetForPreTraining(__UpperCamelCase				)

		lowercase										=							restore_checkpoint(__UpperCamelCase       ,	__UpperCamelCase				)
		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(__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)


"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__lowerCAmelCase				      =   logging.get_logger(__name__)
class 	_lowerCAmelCase    (      __snake_case       ):

							def __init__( self       :    Dict ,		*a       :    Any ,		**a       :    List[str]    )   ->    None:

									"""simple docstring"""
									warnings.warn(
									    '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
									    ''' Please use ConvNextImageProcessor instead.''' ,		a ,		)
									super().__init__(*a ,		**a    )